Trust Aware Secured Energy Efficient Rule based Fuzzy Clustering Protocol with modified Sun Flower optimization Algorithm in Wireless Sensor Networks

Trust Aware Secured Energy Efficient Rule based Fuzzy Clustering Protocol with modified Sun Flower optimization Algorithm in Wireless Sensor Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trust Aware Secured Energy Efficient Rule based Fuzzy Clustering Protocol with modified Sun Flower optimization Algorithm in Wireless Sensor Networks K. Dinesh, Santhosh Kumar SVN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1741631/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Internet of Things (IoT) is a superset of Wireless Sensor Networks (WSNs). Due to its distributed nature, it can be employed across different hazardous environments. In WSNs, devices are treated as various resource factors such as computing resources, sources of energy, storage systems, and security resources, necessitating the use of robust dynamic routing protocol. In most significant breakthrough of wireless data transmission the devices are capable of transmitting data with low cost and very minimal power supply in order to enhance network lifetime and optimization of energy consumption in the network. Any intrusion to data and network security might have a real and physical impact on network quality and affects the security to the network will deployed by the sensor nodes for remote monitoring in unauthorized environment. Pertaining to the geographical location, WSNs plays a wide open role in addressing the modern day challenges all around the world. However in WSNs, the selection of the optimal path with reduced energy and providing optimized security still poses a major challenge. To address this challenge, a methodology in this work has been proposed by choosing multiple trust factors to detect the malicious node, thereby electing a efficient cluster head by using Fuzzy temporal rules with Modified Sun Flower Optimization (FMSFO) algorithm. Moreover, the proposed system produces high reliability and security by employing modified Elgamal Digital Signature algorithm for secured data transmission for the nodes of WSNs. The proposed system provides high potentiality against various types of attacks by reducing the packet loss in indentify the malicious node, resulting in a better QoS and enhance the lifetime of the sensor nodes. The proposed protocol is implemented in NS3 simulator by using realistic simulation parameters. The simulation shows that the proposed method is efficient in terms of energy efficiency, throughput, PDR ratio, delay and provides better security when it is compared with the other existing state of art systems. Internet of Things (IoT) Wireless Sensor Networks (WSNs) Clustering Security Modified Sun Flower Optimization (MSFO) Elgamal Digital Signature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. Introduction WSNs is consists of low power autonomous sensor devices that are deployed in ad-hoc way. WSNs are used in military and civilian activities, environmental monitoring and responding to events in an applied inaccessible environment [ 1 ]. Smart agriculture, healthcare, cities, transportation, smart homes, and other real-time applications have benefited greatly from IoT in conjunction with wireless sensor networks [ 2 ]. WSNs seem to be the most recent frequency technologies that are being used in a wide range of critical systems around the world. [ 27 ]. The sensor device forwarded sensed information to the Base Station by using multi-hop communication to conserve energy during data transmission. In WSNs, there are various issues with the adaptive and optimal communication protocol for WSNs namely inconsistency of limited-power wireless networks and the limited resources in terms of QoS requirements [ 28 ]. WSNs are subjected to various challenges like security attacks, reliability, and optimal Selection of cluster heads and secure communication path, scalability, data aggregation [ 3 ]. Clustering and Data aggregation is among the most widely used technology to increase and optimising power consumption in WSN nodes. In cluster analysis, the sensor devices with the maximum remaining energy and the shortest hop distance is designated as the CH, while the remaining nodes are designated as the CM. In WSNs, two types of clustering techniques are predominantly used namely equal Clustering and un-equal Clustering. In these techniques, each one has its own CH interface between its own CMs and Base Station. Inter and Intra -cluster communications are the two most important types of communication that takes place in WSNs. The intra-cluster communication takes place between CM to its own CH via single hop and inter-cluster communication takes place between CH to BS by using multi-hop via hierarchical clustering [ 4 – 6 ]. Formation of clusters in WSNs supports to improve the nodes energy and improves the QoS through aggregation of data and employing the communication using multi-hop improves scalability and reliability in WSNs. [ 7 ]. In WSNs, employing the single hop clustering results in data loss to increase in the distance of data transmission between sensor node and base station which consumes more energy. In WSNs employing the multi-hop clustering with optimal routing increases energy efficiency across the nodes in the network, hence the network life time of the nodes are prolonged. [ 8 ] In various clustering protocols such as LEACH, EC, HEED [ 31 , 32 , 33 ] the sensor nodes forms a group as clustering and elect the cluster head which improves the network throughput and scalability. The CH Selection by using various approaches by data aggregation, re-scheduling [ 36 ], LEACH [ 37 ] and different fuzzy approaches such as LEACH-FC [ 38 ], MOFCA [ 39 ] protocol to elect efficient and robust CH perform an equal load balance to the words in the cluster. The sensor nodes sensed information is routed to BS by using various routing protocols like AODV [ 34 ] DSDV, DSR [ 35 ] by using low power wireless links. Each SN's deployed senses the data and it is communicated to its BS by using hop-by-hop or multi-hop communication. A number of different of optimization techniques can be used in WSNs to determine the routing path from a source to the intended recipient node. Numerous investigation has been performed in recent years to explore the best path from source to intended recipient nodes for the purpose of maximize the network's sensor node lifetime [ 30 ]. There are numerous swarm intelligence based cluster routing protocols namely EELTM [ 40 ], P-WWO [ 41 ] for the purpose of maximize the network's sensor node lifetime. In WSNs, access control of key, identity verification, and trust management are the important security features in the WSNs. [ 29 ] In WSNs, to offer efficient security and improves dependability, the various trust management schemes has been proposed to compute the overall trust value in nodes related to observed actions, give the right judgment (truthful) and update trust score for providing efficient security. [ 21 ] In WSNs, providing an efficient data communication, with efficient security is very crucial in WSNs. Since the nodes of WSNs are deployed in unfriendly environment and its resource constrained nature during data transmission, it is vulnerable to various types of attacks.. Hence, a novel energy efficient trust based security mechanism is required which provides an efficient security by incorporating data authentication, data confidentiality and data integrity during the data transmission in WSNs [ 9 , 10 ]. Encouraged by all of these occurrences, in order to provide better clustering of nodes and discovering the optimal path during the data transmission and providing efficient security by employing trust based secure authentication a novel Fuzzy Modified Sun Flower optimization (FMSFO) algorithm has been proposed in this work. Table 1 gives the Acronyms and Abbreviation used in this paper. The Major Contribution of this paper includes: To provide efficient cluster formation and election of CH to enhance the lifetime of network by employing rule based fuzzy temporal algorithm. To provide the efficient computation of trust score value with various trust factors to detect the malicious node in order to provide efficient security and reliability. To discover the optimal data transmission path by using modified Sun Flower Optimization algorithm has been proposed in this work. To provide efficient security by employing modified Elgamal based Digital Signature to reduce the impact of various attacks during data transmission. Table 1: Acronym and Abbreviation Acronym Abbreviation IoT Internet of Things WSNs Wireless Sensor Networks FMSFO Fuzzy Modified Sun Flower Optimization QoS Quality of Service BS Base Station CH Cluster Head CM Cluster Member LEACH Low Energy Adaptive Clustering Hierarchy HEED Hybrid Energy Efficient Distributed LEACH-FC Low Energy Adaptive Clustering Hierarchy- Fuzzy Clustering MOFCA Multi-objective fuzzy clustering algorithm for wireless sensor networks. AODV Ad-hoc On Demand Vector DSDV Destination Sequenced Distance Vector DSR Dynamic Source Routing EELTM Energy Efficient Life Time Maximization P-WWO Particle Swarm Optimization-Water Wave Optimization EAPC Energy Aware Path Construction BEEMH Balanced Energy Efficient Multi Hop AZ-SEP Advanced Zonal Stable Election Protocol GRMRP Grid based Reliable Multi hop Routing Protocol GH Grid Head TDMS Two Dimension Mobile Sink GECR Genetic Energy efficient Clustering and Routing ROR Real Or Random CLS-FTCM Cross Layer Security based Fuzzy Trust Calculation Mechanism ECNN Enhanced Conventional Neural Network ETERS Energy Trust based efficient secure Routing Scheme ECNN Enhanced Conventional Neural Network NEHCP Novel Energy Harvesting Clustering Protocol EH Energy harvesting NPPRN Number of Preferred Probable Relay Nodes EHO Elephant herding optimization OTP One Time Pad PSO-ECSM Particle Swarm Optimization- Energy efficient Clustering Sink Mobility 2. Literature Survey Different researchers have developed numerous mechanisms on provide energy efficient clustering and secure data transmission in WSNs. Among them Wen, W et. al. [11] has proposed EAPC algorithm, which provides the energy aware data collection path to collect information from the single collection point and it’s forwarded to the next collection point through mobile sink. This proposed method reduces the consumption of energy and extends the lifetime of the sensor node in the Wireless sensor networks. Moreover this algorithm helps to reduce the traffic of the network and improves load balances. The limitation of this method is it takes more time to compute and construct the path by using loop free spanning tree algorithm. Dipak Kumar Sah et. al. [42] have have proposed the NEHCP algorithm for EH based on the EH rate of the sensor nodes, which selects the CH. This has data communication in between CH and BS using a single hop or several hops. Sethi, D [12] have proposed a framework related to mobile Base Station (BS). It employs 4 or 8 sojourn location path with one centralized static sink node, which is applied for static sensor nodes of both homogeneous and heterogeneous protocols to compare the energy consumption in WSNs. Compared with static sink, mobile sink is used to reduce the power consumption of sensors and extend their lifetime of WSNs.. The limitation of this system is reliability it has very less since mobile sink is prone to battle ad by the intruders. Shokair, M. et .al. [13] have proposed BEEMH protocol, especially designed for multi hop data transmission to optimize the energy constraint, extend their lifetime of the sensors in WSNs single hop information sharing between sensors to BS consumes high energy due to high distance. In order to avoid these issues, energy efficient multi hop communication protocol introduced with optimal route path selection. Moreover this BEEMH algorithm finds shortest path with minimum hop count by using dijkstra algorithm and calculate the weight of the path between nodes by using the nodes which has higher residual energy. Such nodes are act as relay node. Khan, F. A et. al. [14] have proposed AZ-SEP, which is designed for heterogeneous sensor nodes and routing protocols. In their protocol, the normal sensor nodes are directly communicated and data is sent to the sink. The nodes are grouped by clustering with multi hop communication from CH to BS using of hierarchical routing protocol. The advantages of this protocol is by reducing the nodes death, to increase the network stability, to increase the packet delivery ratio, improve the power consumption by prolonging the nodes lifespan in WSNs. The limitations are it has communication overhead during the routing process to transmit the sensed data. Chen, Z. et.al. [15] have proposed Protocol (GRMRP), which is used to optimize the consumption of energy using of multi hop communication and data aggregation method. In Their protocol network is partitioned into several virtual grids and one node is active that called as GH and remaining all nodes are keep in sleep made to save the consumption of energy in WSNs. In their system, the GH communicates to BS via multi-hop communication in hierarchical structure. Moreover, limitation in this protocol as grid supports only for small scale environment and provides less scalability. Basumatary, H et. al. [16] have proposed TDMS protocol, which is used to improve the energy efficiency by employing clustering method to enhance the life span of nodes in WSNs. In their protocol CHs are chosen randomly with threshold value, newly elected CHs broadcast the remaining energy to sink node. The sink node compares the CHs residual energy with threshold value and find out the nodes which has least energy of CH and the location with the help of GPS. Once they are detected least residual energy CH, the sink node is moved near to the least residual energy CH position to provide efficient communication among the nodes. Noureddine Moussa et. al. [45] have proposed ECRP-UCA which is unequal clustering, algorithm to handle the problems of uneven congestion control and optimum path finding in WSNs. In CH selection is based on remaining energy, frequency of surrounding nodes, sink distance, with a new feature called backward relay nodes which is introduced to balance load among CHs. The ant finds the ideal path based on the likelihood of nodes to optimise the ant's seeking direction using the parameter namely NPPRN. Deepa, K. et. al. [17] have proposed Density based Fuzzy C Means clustering protocol to improve the energy efficiency and optimize the lifetime of the WSNs. In their protocol, the CH are chosen by centroids of the grid environment. In their protocol, the sink node initiate the data communication by anywhere and anytime by using beacon signal broadcast to each sensor node and it is in the network. Whenever, the sensor nodes receives beacon signal, It became an active mode from drowsy mode and ready to sent the data with help of leader node. In this protocol the leaders are elected based on density of the network. The limitations are the message overhead due to broadcast the beacon message and lack of discovering optimal routing path in the network which leads for high delay. Wang, T. et. al. [18] has proposed GECR algorithm to discover optimal path between CM to the CH then BS. This protocol is employs fitness function to reduce the communication overhead in the network. The limitations are this protocol lacks reliability and security during the data transmission. Ivana Strumberger et. al. [44] has proposed protocol EHO to solve the challenge of locating sensor nodes in WSNs. It deals with optimization and NP-hard problems. There are tactics used by the clan's leader female elephant. Others are usually females and babies, with male elephants leaving the settlement to live alone after they reach full maturity. Male elephants interact with the rest of the family using sound waves vibrations, despite the fact that they live individually. The limitation of this paper as coverage and energy efficiency in WSNs. Maurya, A et. al. [19] have proposed ROR model in order to improve the reliability, and to provide better security and user authentication against various types of attacks in the network. The advantages of this protocol are its enhanced security and the limitations are its overhead and lack of optimal route path discovery which consumes more energy in the nodes of WSNs. Khalid Haseeb et. al. [43] has proposed LSDAR method to enhance the efficiency of nodes and provides the better security against various malicious activities by utilising the XoR operation. In their protocol, nodes are divided into multiple uneven clustering sizes by using various radiuses. For non-loop routing paths, an A-star optimization technique is designed. Furthermore, employing OTP for node-to-node data transfer guarantees strict security against multiple attackers. The advantages of this protocol is its enhance in packet delivery ratio. The limitation of its protocol is its scalability. Sumalatha, M. S. et. al. [20] have proposed CLS-FTCM to extract the multiple parameters for trust computation to provide better safe guard against various attackers in WSNs. The proposed protocol employs ECNN classifier is to detect the malicious node and monitoring the fault in the network. The advantage of this protocol provides highly better security, and reduced overhead in the nodes of WSNs. Biswa Mohan Sahoo et. al. [46] have proposed a PSO-ECSM to address both the cluster head election and sink node mobility issues. In their protocol, for CH selection, five parameters are considered namely remaining energy, degree of node, nodes average energy, distance, and power consumption rate. The proposed protocol find the best score for those variables. Moreover, this algorithm introduces sink mobility to handle the issue of forwarding transmitted data in a multi-hop network. To make the network more cost effective, sink mobility is explored. Khan t. et. al. [21] have proposed ETERS algorithm to provide the better shortest optimal secure route with low latency in the nodes of WSNs. The proposed protocol employs computed trust score value is to discover the optimal secure routing for data communication. Trust revealing algorithm is used to notice the internal hit of sensor node which improves the reliability and throughput. Moreover, the proposed protocol reduces the packet loss in the network. The above survey depicts the various researcher’s contributions towards security, reliability, scalability, energy efficiency for the purpose of enhance the lifespan of the WSNs. Even though, there are several methodologies that are able to solve different problems it is evident that data transfer in terms of single-hop for long distance communication potential has been a drawback. Hence multi-hop based communication is preferred. Moreover, the majority of previous cluster-based secured protocol has significant computation and communication overhead, which is a serious drawback to enhance and optimize the lifetime of the nodes in WSNs. 3. Energy Model And Network Assumption In the proposed system, the propagation model is described in [ 22 ]. The sensor nodes sense the information that is send to base station by direct or hop by hop communication. Long distance with direct communication method consumes more energy. Hence for reducing the consumption of energy the multi hop communication method is preferred. During transceiving of data, sensor nodes consume energy. Measuring the distance between nodes and base station is very essential part for energy calculation. The proposed system employs Euclidean’s distance measurements for the two points (P,Q) in the network by using Eq. ( 1 ). $$d\left(P,Q\right)=\sqrt{{({Q}_{x}-{P}_{x})}^{2}+{({Q}_{y}-{P}_{y})}^{2}}$$ 1 To transmit n bit message from transmit node, it requires \({E}_{trans}\) energy which is calculated by using Eq. ( 2 ) where \({E}_{el}\) represent the request of energy to operate the node to transmit and receive the n bit message, d is a Euclidean’s distance, d 0 is the threshold value TH, fs denotes free space, \({E}_{init}\) is the initial energy of the nodes, mp denotes multipath. $${E}_{trans}=\left\{\begin{array}{c}{E}_{el}*n+n* {e}_{fs}* {d}^{2} d<{d}_{0}\\ {E}_{el}*n+n* {e}_{mp}* {d}^{4} d={d}_{0}\end{array}\right.$$ 2 \({E}_{rece}\) Receive the n bit message which is computed by using the Eq. ( 3 ) \({E}_{usage}\) is the total usage energy by a node and its computed by equation. ( 4 ) \({ E}_{res}\) Where is the remaining energy of sensor node That is computed by using Eq. ( 5 ) $${E}_{rece}={E}_{el}*n$$ 3 $${E}_{usage}={E}_{trans}*{E}_{rece}$$ 4 $${E}_{res}={E}_{init}*{E}_{usage}$$ 5 In every round, CH is selected by comparing threshold value \(T\left(n\right)\) with the nodes which has less residual energy than current CH and its computed by using Eq. ( 6 ) $$T\left(n\right)=\left\{\begin{array}{c}\frac{P}{1-P*\left(r mod \frac{1}{P}\right)}* \frac{{E}_{res}}{{E}_{init}} if n?S\\ 0 Otherwise\end{array}\right.$$ 6 The Assumption that is considered to designing the proposed protocol are first, the proposed protocol employs random deployment of sensor nodes during pre-deployment phase. Second, the deployment location of nodes and BS are static. Third, all deployed nodes have similar initial energy and nodes are homogenous in nature. Fourth, the proposed protocol employs symmetric radio links where one link is used for transmission and another link is used for reception. The last assumption is sensor devices must include enough computational power to carry out the fundamental cryptography operation, to perform encryption, key generation, decryption of sensed data. 4. Proposed System Architecture The architecture of this paper work is depicted the given Fig. 1 is architecture of this proposed method have four major stages such as initialization of system and key generation phase, clustering phase, intelligent trust based discovery phase, final phase as data transmission and reception. In first phase, initially the sensor devices are distributed in the chaotic way in the distributed environment. The major purpose of this phase to create the public keys, private keys are generated by the BS. It is implanted to the respective sensor devices in pre-deployment phase. The proposed system employs the keys for secure data transmission in the network. In that clustering phase, CHs are elected based on Fuzzy temporal rules. Once the CH is elected, by using remaining energy, The clustering is created in try to reduce the number of redundant and unnecessary transmissions in the network. The next phase of the proposed system trust based optimal route discovery phase. Here, initially the trust values are computed for every nodes in the network. Based on the computed, the optimized route is discovered by employing modified sunflower optimization algorithm. The last phase of the proposed system is data transmission and data reception phase. The major aim is to afford efficient, secured data transmission. The proposed system employs multi party key exchange along with modified Elgammal digital signature to make sure the information integrity in the network. Figure 2 gives flowchart of the proposed protocol. 5. Fmsfo (Fuzzy Modified Sun Flower Optimization Method) The novel FMSFO protocol has been proposed in this paper to give better energy consumption and secured transmission of data in WSNs by means of efficient clustering method. The proposed FMSFO algorithm includes five major modules such as initialization of system and key generation phase, intelligent rule based fuzzy clustering phase, trust calculation phase, intelligent optimal route discovery phase by MSFO, Data transmission and reception phase by modified elgammal digital signature. 5.1 System Initialization and Key generation phase The first phase of the proposed system is system initialization and key generation phase. The major goal of this phase to make the public key and private key for all legitimate deployed nodes in the network. The generated public key and private key are used for encryption and decryption of transmitted data which ensures the integrity of the data which is transmitted from sensor node to BS. In this proposed protocol, BS generates the keys and these keys are installed into corresponding sensor nodes during pre-deployment phase. Algorithm1 gives the steps to be followed for the making of the private key by BS. Algorithm1 Generation of Private keys Step1: Begin Step2: Choose the random prime numbers p,q,α by using Pseudo random integer function. Compute \(?=\left(p-1\right)(q-1)\) Step3: Compute X A = (P α ) mod q Step4: Compute X B = ( q α ) mod \(⋋\) Step5: Compute X C = (pq) α mod pq Step6: choose two random integer prime number X g , X f from z*(P) where z*(p) is a cyclic Group Step7: Compute \({X}_{d}={\left({X}_{A}\right)}^{{X}^{g}}mod p\) Step8: Compute \({X}_{C}={{(X}_{B})}^{{X}_{f}} mod pq\) Step9: Compute \({X}_{f}={\left({X}_{d}{.X}_{C}\right)}^{?} mod p\) Return Private key \(\left\{ {X}_{C},{X}_{f}\right\}\) Algorithm2 Generation of Public keys Step1: Begin Step2: Choose the random prime integer numbers β, \({X}_{C},{X}_{f}\) Step3: Compute \({Y}_{a}= {P}^{\beta }. {X}_{C} mod P\) Step4: Compute \({Y}_{b}={q}^{p}.{X}_{f} mod q\) Step5: Compute \({Y}_{C}= {Y}_{b}^{p}.{Y}_{a}^{q} mod pq\) Step6: choose the two random prime integer \({Y}_{g},{Y}_{f} from {Z}^{*}P\) Step7: Compute \({Y}_{d}=\left({{Y}_{a}}^{{Y}_{g}} mod pq\right)\) Step8: Compute \({Y}_{C}=\left({{Y}_{d}}^{{Y}_{b}} mod \left(p-1\right)\right)\) Step9: Compute \({Y}_{f}=({{Y}_{c}}^{{Y}_{d}} mod {Y}_{b}\) ) Return Public P u K= {Y c , Y f } The generated public key is transmitted to the all the designated nodes in the network by means of secured multiparty key exchange. The proposed protocol employs the generated pair of Asymmetric keys for encryption and decryption purpose secured data transmission in WSNs. 5.2 Intelligent rule based fuzzy clustering phase The next phase of the proposed system is intelligent rule based fuzzy clustering phase. This phase which sensor nodes have more remaining energy and less hop distance is elected as cluster head (CH) and other remaining nodes acts cluster member (CM). In the proposed protocol the inter and intra-cluster communication are most preferred modes of communication for the nodes of WSNs. in intra-clustering, communication takes place between CM to CH via single hop, and in inter-cluster, the communication takes place between CH to BS via multi-hop. The proposed intelligent rule based fuzzy clustering, works on two phases namely cluster head selection by rule based fuzzy algorithm and cluster formation phase. 5.2.1 Cluster head selection by rule based fuzzy algorithm In the proposed protocol, for clustering of nodes it considers three input factor parameters such as remaining energy of node, degree of nodes, and distance from nodes to base station are the input variable to generate the fuzzy rules. For lingual version of node residual power has 3 levels namely low, medium, High and for node degree it represented as three levels namely large, moderate, small respectively, and for node distance to BS it is represented as close, approximate and far, respectively. The fuzzy rules outcome of node which is elected as a cluster-head is represented as 7 levels namely very small, small, rather minor, medium, rather heavy, large, and very large. Table 2 gives the output of fuzzy rules for the nodes to elect as the cluster head. Algorithm3 Election of Cluster head Input Fuzzy input variable (R.E, node_degree, dist_to_BS) Output Election of efficient CH based on chance value (CH 1 , CH 2 , CH 3 , ……….CH n ) 1: { List_CH = 0, CV = 0, count_CH = 0, CM = NULL} //Parameter initialization 2: i = 0,1,2…..n //number of sensor devices in the network 3: vice_CH = FALSE 4: for each node do 5: \(\mu =rand\left(\text{0,1}\right)\) 6: Node_state = CM 7: if ( \(\mu <th) then\) 8: vice_CH = TRUE 9: CV = fuzzy(R.E,node_degree,dist_to_BS) //calculate CV using fuzzy if rule. 10: End if. 11: End for. 12: Broadcast CH_msg (id,CV,R.E) to its neighbors 13: Each node j receives the CH_msg from node i 14: If( i(R.E) > j(R.E)) then 15: Vice_CH = FALSE 16: Quit the broadcast CH_msg. 17: End if. 18: If(vice_CH = = TRUE) then 19: Node_state = CH 20: ADD i node as cluster member list (CH_list) 21: CH_list = CH_list + 1 22: End if 23: If(node_state = = CM) then 24: If (CH = = TRUE) 25: Broadcast CH_msg with ID. 26: Node_status = CH 27: CH broadcast node status with ID 28: C i ={CM 1 , CM 2 , ………CM n } 29: Else 30: On receiving all CH_msg 31: My_CH = nearest_CH 32: Send join CH_msg(id) to nearest CH 33: End if 34: Exit. Based on the fuzzy rule generated, the node which has low residual energy, high degree of connectivity, and minimum distance between BS to CM elected as CH. In Algorithm 3 gives the intelligent rule based fuzzy clustering process in the proposed protocol. Table 2 Chance value for Fuzzy rule based algorithm Residual Energy Node degree Dist. BS to CH Chance value Low Small Close Small Low Small Approximate Small Low Small Far Very small Low Moderate Close Small Low Moderate Approximate Small Low Moderate Far Small Low Large Close Rather minor Low Large Approximate Small Low Large Far Very small Medium Small Close Rather minor Medium Small Approximate Medium Medium Small Far Small Medium Moderate Close Large Medium Moderate Approximate Medium Medium Moderate Far Rather minor Medium Large Close Large Medium Large Approximate Rather minor Medium Large Far Rather minor High Small Close Rather heavy High Small Approximate Medium High Small Far Rather low High Moderate Close Large High Moderate Approximate Rather heavy High Moderate Far Medium High Large Close Very large High Large Approximate Rather heavy High Large Far Medium 5.2.2 Cluster Formation Phase Once the cluster head has been elected in the network, the next phase is cluster formation phase. In this phase, each node communicates to small set of nodes within its transmission range to form a clustering. BS randomly selects the CH by applying the rule based fuzzy algorithm, to broadcasts the CH_msg with the nodes within the transmission range. Which id’s to all the members for example if the nodes j wants to join the node i cluster member. The node j receives more CH_msg with its id connecting the recently joined ids. Only the remaining nodes that received CH_msg are removed from the node j Cluster member. Once the cluster was formed each CM transmit the sensed information to CH via multi-hop communication by TDMA (Time Division Multiple Access) schedule for transmitting the sensed data. In the proposed system, CH is rotated by applying the intelligent rule based fuzzy clustering algorithm by selecting threshold TH value with time T exceeds then the reduction of cluster head had take place by following algorithm3. 5.3 Trust computation. The next module of the proposed system is trust computation phase. The proposed protocol employs modified Sun flower optimization algorithm for routing and the trust parameters are include in MSFO. The modified SFO algorithm includes the trust parameters for discovering the optimal path during data transmission. In the proposed protocol the trust model has been coagulated with SFO algorithm to prevent the intruders to perform the various malicious attacks during optimal route discovery process. In the proposed system, for inter-cluster communication MSFO algorithm is employs for discovering the optimal path between CH and BS via multi-hop secure communication. 5.3.1 Point to point trust Every node x directly interacts with the other node y, by calculating the direct trust by Eq. ( 7 ) where PT stands for point to point trust, \({P}_{rv}\left(x\right)\) represent node y that receives packets from node x and \({P}_{fd}\left(y\right)\) represents node y forwarding packets to other node. $$PT=\frac{{P}_{rv}\left(x\right)}{{P}_{fd}\left(y\right)}$$ 7 5.3.2 Indirect trust Node x must determine the trustworthiness of node y in order to relay a packet, but it has no prior contact history of node y. In this example, node x obtains node y's trust degree from its neighbours, which is determined using Eq. ( 8 ) $$IT=\frac{1}{K}\underset{m=1}{\overset{k}{?}}PT \left(m\right)$$ 8 Where IT is the indirect trust, PT(m) is the point to point trust for the message m. 5.3.3 Packet drop trust The packet drop trust by the way of previous packet drop history with neighboring nodes is computed by employing the Eq. ( 9 ) $$PDT=\frac{1}{k}\underset{m=1}{\overset{k}{?}}\frac{{PD}_{k}}{{PS}_{i,k}}$$ 9 Where PDT is the packet drop trust, PD k packet drop in k number of nodes, PS i,k Packet successful ratio in between the node i to k. 5.3.4 Total trust In the proposed protocol the overall trust score is computed by using Eq. ( 10 ). The trust score is 0 to 0.5 indicates low trust score and the trust score in between 0.6 to 1 indicates high trust score. Where ( \(\alpha +\beta +\gamma )=1\) $$Tot Trust=\alpha PT+\beta IT+\gamma PDT$$ 10 5.3.5 Malicious node detection and isolation The proposed protocol computes three level of trust namely point to point trust, indirect trust, and packet drop trust. Based on their computed trust scores, the total trust score is computed by combining the point to point trust score, indirect trust score, and packet drop trust score by Eq. ( 10 ). 5.4 Intelligent optimal route discovery MSFO algorithm phase In the proposed system for an efficient inter-cluster communication, the MSFO is employed to discover the optimal path between CH to BS via multi-hop communication. The MSFO is swarm intelligence algorithm, all the moment are based on the orientation towards the sun, where the sun is the BS. The MSFO algorithm includes the trust value in order to locate the network's rogue node Algorithm 4 MSFO based optimal route discovery. Input Total_trust,dist_to_BS, R.E. Output Optimal secure Path selection (CH to BS) 1: Start 2: Parameter initialization 3: {Init_value = 0, X * =0, t = 0, n, max} 4: Generate population 5: X i (t) i = 0,1,2,3……..n 6: f(X i (t) ) = high(Total_trust) + close(dist_to_BS) + max(R.E) 7: if(Total_trust = = 0) then 8: Find out malicious node inform to BS 9: BS broadcast the malicious node id break the connection from safe node to antinode. 10: End if 11: Evaluate new CH or Vice_CH fitness function. 12: X * = f(X i (t) ). 13: update the new CH route if their fitness are better than the current values 14: If( t > max) then 15: Repeat calculate fitness function 16: Until condition satisfied 17: t = t + 1 18: End if. 19: Exit. During the optimal route discovery process, the proposed MSFO identifies the node which has low trust score between 0 to 0.5 and it immediately report to the BS. In return the BS black list the corresponding nodes based on their nodes ids and prevent them to participate in the packet routing process during inter-cluster communication. Then it broadcast the information of the compromised nodes ids to all the legitimate nodes in the network. By doing so, the nodes communicate only with the other nodes which have low trust score are separated from the nodes which has high trust score for efficient identification of malicious network nodes. The proposed Modified SFO protocol chooses alternate optimal path from the malicious nodes for providing secure data transmission by detecting the malicious nodes during the route discovery process. 5.5 Modified Elgammal DS Data transmission The next method of the proposed algorithm is modified elgammal based digital signature data transmission phase. Moreover, the digital signature algorithm employed provides high security during data transmission phase. Here, the original message (M) digest with hash function using H(M) and encrypts the hash message with sender private key as signature and the transmit the cipher text to receiver. Algorithm 4 gives modified elgammal based digital signature algorithm gives the steps Algorithm 4 Modified elgammal based digital signature algorithm Step 1 Begin Step 2 Choose plain text message M Step 3 Apply hash function using SHA1 algorithm H(M) Step 4 select {q, α, X A , Y A } Step 5 select (M) 0 < M < q-1 Step 6 calculate {K,K -1 } Step 7 Compute gcd(K,q-1) = 1 Step 8 Compute S 1 & S 2 $${S}_{1}={a}^{K} mod q$$ Step 9 : \({K}^{-1}= {K}^{-1} mod (q-1)\) $${S}_{2}={K}^{-1}\left(M-{{X}_{A}}^{S1}\right)mod (q-1)$$ Step 10 signature pair as (S 1 , S 2 ) 5.6 Data Reception and verification The next module of the proposed system is information reception and verification, in this phase once receiver receives the cipher text it performs decryption by senders public key, then apply hash algorithm to separate the signature and H(M). Then the receiver verifies the packets by validating the sender digital signature and by verification algorithm. When both hash messages are same, then the message is valid otherwise sender discards the message from the network. Step 1 Choose the prime number {α, q} Step 2 Compute {V1,V2} using public key of Y A Step 3 : \({V}_{1}= {a}^{M} mod q\) $${V}_{2}= {\left({Y}_{A}\right)}^{{S}_{1}}{\left({S}_{1}\right)}^{{S}_{2}} mod q$$ Step 4 V1 = V2 message is verified If(V1 = = V2) then Accept the message else Ignore the message Step 5 end 6. Simulation Setup And Performance Evaluation Parameters. In the proposed protocol, the assumption is that the sink node is the high resourceful device which consists of continuous power supply and cannot be composed by the intruders. The deployment BS and sensor devices are static in nature. The sensor nodes initial energy is considered as 1 joules and BS initial energy as considered 100 joules. The energy is consumed by the nodes is determined by the amount of energy on spent transmitting and receiving packets. Table 3 gives the network simulator parameters employed in the proposed system. During their lifetime each sensor node is capable of performing fundamental cryptographic operations. The proposed FMSFO model is implemented by using NS3 simulator tool. The suggested procedure was assessed using performance indicators, energy consumption analysis, PDR ratio, network lifetime, and to end delay analysis, residual energy analysis, malicious nodes detection accuracy. Table 3 Network simulation parameters Parameters Value Network Area 1000 * 1000 m 2 No. of nodes 500 Initial energy of nodes 1 J CH threshold value 0.05 Number of malicious node % 10–50 nodes Size of the packet 4096 bits E elec 100 nJ/bit ε fs 20 pJ/ bit/m 2 ε mp 0.0026 pJ/bit/m 4 Transmission energy 0.48 J Receiving energy 0.12 J Communication range of CH 50 m Communication range of CM 30 m 6.1 Simulation results The proposed FMSFO protocol is compared with the existing protocol namely CSDP [ 23 ], ERF [ 24 ] and RSA [ 25 ]. Figure 3 gives the energy consumption analysis of FMSFO protocol then that is compared with other existing protocols namely CSDP [ 23 ], ERF [ 24 ] and RSA [ 25 ]. In that below graph, it is understandable that the proposed FMSFO algorithm has better energy consumption then that is compared with other existing protocols. The reasons for the improvement is that the proposed protocol employs intelligent rule based fuzzy clustering algorithm and discovers the optimal routing path by employing trust based modified sunflower optimization algorithm. Moreover, the proposed protocol employs modified elgammal digital signature for ensuring the inputs of the data then that is transmit to network. In the proposed protocol provides efficient clustering efficient trust computation and discovery optimal routing path by detecting the rough nodes are blacklisting then during the routing process. Hence, the proposed protocol improves the energy consumption upto 3 to 6% then that is compared with other former protocols. Figure 4 shows that consumption of energy in the presence of malicious nodes. Even in the presence of malicious nodes the proposed FMSFO protocol perform better with optimized energy consumption when it is compared with existing algorithms. In Fig. 5 depicts the analysis of packet delivery ratio of the proposed protocols in terms of packet delivery ratio when it’s compared with other existing protocols namely RSA, ERF, and CSDP by varying number of nodes in the network. The proposed FMSFO algorithm has better packet delivery ratio then that is compared to other existing protocols. The main goal for that improvement is that discovers the optimal path by employing MSFO algorithm along with trust factor to find out the malicious nodes in network. Moreover, the proposed protocol prevents the nodes to drop the packets by find out the malicious nodes and prevents them taking path in the routing process. Hence, the proposed algorithm gives better packet delivery ratio, when it is compared with other former protocols. Figure 6 depicts the analysis of packet delivery ratio by varying the number of malicious nodes in the network. Even in the presence of malicious nodes the proposed method obtain better packet delivery ratio compare to all other existing methods. Figure 13 gives the average throughput analysis of the proposed protocol with other existing protocols. From the graph it is clear that the proposed protocol has better throughput since it employs modified sunflower optimization algorithm to perform efficient routing. Figure 14 shows the average throughput analysis of the network by varying number of malicious nodes, even in the presence of malicious nodes the proposed protocol achieves better throughput. Table 4 Performance analysis of proposed protocol and existing protocol referred by time and overhead Protocol name Network Stability Time (sec) System Overhead (%) CH formation time (ms) CH existence time (sec) RSA [ 25 ] 240 10 5 25 ERF [ 24 ] 220 13 7 21 CSDP [ 23 ] 300 5 3 30 FMSFO 350 4 2 35 Table 4 depicts the performance variation between the existing protocols RSA, ERF, CSDP when it is compared with the proposed protocol FMSFO. The proposed protocol reduces the system overhead by 2–6 percent and formation of cluster time as 1–5 ms and also improves the stability time of the network and also CH existence time in the network. Table 5 Time Space and Crypt Complexity Analysis Methods Time Complexity Space Complexity Crypt analysis Complexity RSA [ 25 ] O(n 3 ) O(n 3 ) Discrete logarithmic Santhosh [ 26 ] O(n 2 ) O(n 3 ) Polynomial Time CSDP [ 23 ] O(n log n) O(n 2 ) Discrete logarithmic FMSFO log (n) log (n) Discrete logarithmic Table 5 compares the time and space complexity of existing protocols RSA, ERF, and CSDP when it is compared with the proposed protocol in terms of computation time, memory space, and the analysis of crypt analysis complexity. Based on analysis, the proposed protocol has better time and space complexity and has better crypt analysis complexity when it is compared with other existing protocols. 7. Conclusions And Future Work The proposed protocol focused on trust score secure data communication with efficient clustering and optimal path finding using optimization algorithm in order to achieve better network performance. To detect the malicious nodes from the normal nodes the proposed system employs MSFO proposed algorithm with better secure data communication by using Elgamal digital signature algorithm. Furthermore, for data transfer, a reliable and secure optimum routing method is selected to decrease latency while providing strong authenticity across numerous adversaries. The outcomes of this study demonstrated that the proposed FMSFO outperforms other existing protocols in terms of security by employing effective and convenient encryption and decryption scheme, a trust-based malicious node detection scheme, and effective node authentication, as well as energy consumption, reduced delay, increased packet delivery ratio, high throughput, and enhance the network lifetime. The proposed protocol may be improved further by using meta-heuristic optimization protocol to provide better communication and reduce complexity in WSNs. Declarations Ethical Approval and Consent to participate: All the authors has given ethical approval and consent to participate in publishing for this journal Human and Animal Ethics : This article does not contain any studies with human or animal subjects performed by any of the authors Consent for publication : All the authors have given consent for the publication Availability of supporting data : Data sharing is not applicable to this article as no new data were created or analysed in this study. Competing Interests : There is no competing interests among authors and co-authors of the given Manuscript. Funding : Funding not applicable Authors' contributions : The first author Dinesh K has carried out simulations and literature survey and Santhosh Kumar SVN has carried out problem formation and algorithm design Acknowledgements : Not Applicable Author Information : Can be included at the later stage References Mohamed, R. E., Saleh, A. I., Abdelrazzak, M., &Samra, A. S. (2018). 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node\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-1741631/v1/cfedd9dc972dca9d31291a4a.png"},{"id":22897835,"identity":"16fcf5c6-2fdd-4587-b877-61c4105b73a7","added_by":"auto","created_at":"2022-06-21 16:16:46","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":22795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResidual Energy\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-1741631/v1/e84c23044d267f9b4c413ae1.png"},{"id":22898385,"identity":"890153d0-63c9-449f-8883-4774035dab13","added_by":"auto","created_at":"2022-06-21 16:21:45","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":23155,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResidual Energy with malicious nodes\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-1741631/v1/cbb62d923a6591018f9bf7ca.png"},{"id":22897831,"identity":"889cd33b-ef95-44c1-acc3-5a33d50864e9","added_by":"auto","created_at":"2022-06-21 16:16:45","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":24151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage Throughput Analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-1741631/v1/5ed554a43cd1ca7a6b6513e3.png"},{"id":22897836,"identity":"fefed0f0-f1fe-4c36-8330-a68b966d7781","added_by":"auto","created_at":"2022-06-21 16:16:46","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":24410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage Throughput Analysis with malicious nodes\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-1741631/v1/5b9337d7da0ac2ca9465d699.png"},{"id":29058390,"identity":"d4137b52-3ad5-4e0d-bc72-8fa2086e6fe1","added_by":"auto","created_at":"2022-11-15 03:59:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1212498,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1741631/v1/3a94e439-41dc-42c5-b809-e6b25032d605.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trust Aware Secured Energy Efficient Rule based Fuzzy Clustering Protocol with modified Sun Flower optimization Algorithm in Wireless Sensor Networks","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWSNs is consists of low power autonomous sensor devices that are deployed in ad-hoc way. WSNs are used in military and civilian activities, environmental monitoring and responding to events in an applied inaccessible environment [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Smart agriculture, healthcare, cities, transportation, smart homes, and other real-time applications have benefited greatly from IoT in conjunction with wireless sensor networks [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. WSNs seem to be the most recent frequency technologies that are being used in a wide range of critical systems around the world. [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. The sensor device forwarded sensed information to the Base Station by using multi-hop communication to conserve energy during data transmission. In WSNs, there are various issues with the adaptive and optimal communication protocol for WSNs namely inconsistency of limited-power wireless networks and the limited resources in terms of QoS requirements [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. WSNs are subjected to various challenges like security attacks, reliability, and optimal Selection of cluster heads and secure communication path, scalability, data aggregation [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]. Clustering and Data aggregation is among the most widely used technology to increase and optimising power consumption in WSN nodes. In cluster analysis, the sensor devices with the maximum remaining energy and the shortest hop distance is designated as the CH, while the remaining nodes are designated as the CM. In WSNs, two types of clustering techniques are predominantly used namely equal Clustering and un-equal Clustering. In these techniques, each one has its own CH interface between its own CMs and Base Station. Inter and Intra -cluster communications are the two most important types of communication that takes place in WSNs. The intra-cluster communication takes place between CM to its own CH via single hop and inter-cluster communication takes place between CH to BS by using multi-hop via hierarchical clustering [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. Formation of clusters in WSNs supports to improve the nodes energy and improves the QoS through aggregation of data and employing the communication using multi-hop improves scalability and reliability in WSNs. [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. In WSNs, employing the single hop clustering results in data loss to increase in the distance of data transmission between sensor node and base station which consumes more energy. In WSNs employing the multi-hop clustering with optimal routing increases energy efficiency across the nodes in the network, hence the network life time of the nodes are prolonged. [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e] In various clustering protocols such as LEACH, EC, HEED [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e] the sensor nodes forms a group as clustering and elect the cluster head which improves the network throughput and scalability. The CH Selection by using various approaches by data aggregation, re-scheduling [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e], LEACH [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] and different fuzzy approaches such as LEACH-FC [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e], MOFCA [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] protocol to elect efficient and robust CH perform an equal load balance to the words in the cluster. The sensor nodes sensed information is routed to BS by using various routing protocols like AODV [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e] DSDV, DSR [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] by using low power wireless links. Each SN\u0026apos;s deployed senses the data and it is communicated to its BS by using hop-by-hop or multi-hop communication. A number of different of optimization techniques can be used in WSNs to determine the routing path from a source to the intended recipient node. Numerous investigation has been performed in recent years to explore the best path from source to intended recipient nodes for the purpose of maximize the network\u0026apos;s sensor node lifetime [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. There are numerous swarm intelligence based cluster routing protocols namely EELTM [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e], P-WWO [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] for the purpose of maximize the network\u0026apos;s sensor node lifetime. In WSNs, access control of key, identity verification, and trust management are the important security features in the WSNs. [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] In WSNs, to offer efficient security and improves dependability, the various trust management schemes has been proposed to compute the overall trust value in nodes related to observed actions, give the right judgment (truthful) and update trust score for providing efficient security. [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e] In WSNs, providing an efficient data communication, with efficient security is very crucial in WSNs. Since the nodes of WSNs are deployed in unfriendly environment and its resource constrained nature during data transmission, it is vulnerable to various types of attacks.. Hence, a novel energy efficient trust based security mechanism is required which provides an efficient security by incorporating data authentication, data confidentiality and data integrity during the data transmission in WSNs [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Encouraged by all of these occurrences, in order to provide better clustering of nodes and discovering the optimal path during the data transmission and providing efficient security by employing trust based secure authentication a novel Fuzzy Modified Sun Flower optimization (FMSFO) algorithm has been proposed in this work. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e gives the Acronyms and Abbreviation used in this paper. The Major Contribution of this paper includes:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eTo provide efficient cluster formation and election of CH to enhance the lifetime of network by employing rule based fuzzy temporal algorithm.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTo provide the efficient computation of trust score value with various trust factors to detect the malicious node in order to provide efficient security and reliability.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTo discover the optimal data transmission path by using modified Sun Flower Optimization algorithm has been proposed in this work.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTo provide efficient security by employing modified Elgamal based Digital Signature to reduce the impact of various attacks during data transmission.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Acronym and Abbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcronym\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eIoT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eInternet of Things\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eWSNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eWireless Sensor Networks\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eFMSFO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eFuzzy Modified Sun Flower Optimization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eQoS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eQuality of Service\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eBase Station\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eCluster Head\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eCluster Member\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eLEACH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eLow Energy Adaptive Clustering Hierarchy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eHEED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eHybrid Energy Efficient Distributed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eLEACH-FC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eLow Energy Adaptive Clustering Hierarchy- Fuzzy Clustering\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eMOFCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eMulti-objective fuzzy clustering algorithm for wireless sensor networks.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eAODV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eAd-hoc On Demand Vector\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eDSDV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eDestination Sequenced Distance Vector\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eDSR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eDynamic Source Routing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eEELTM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eEnergy Efficient Life Time Maximization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eP-WWO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eParticle Swarm Optimization-Water Wave Optimization\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eEAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eEnergy Aware Path Construction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eBEEMH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eBalanced Energy Efficient Multi Hop\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eAZ-SEP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eAdvanced Zonal Stable Election Protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eGRMRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eGrid based Reliable Multi hop Routing Protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eGH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eGrid Head\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eTDMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eTwo Dimension Mobile Sink\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eGECR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eGenetic Energy efficient Clustering and Routing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eROR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eReal Or Random\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eCLS-FTCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eCross Layer Security based Fuzzy Trust Calculation Mechanism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eECNN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eEnhanced Conventional Neural Network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eETERS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eEnergy Trust based efficient secure Routing Scheme\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eECNN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eEnhanced Conventional Neural Network\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eNEHCP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eNovel Energy Harvesting Clustering Protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eEH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eEnergy harvesting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eNPPRN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eNumber of Preferred Probable Relay Nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eEHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eElephant herding optimization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003eOTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eOne Time Pad\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20.342205323193916%\"\u003e\n \u003cp\u003ePSO-ECSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"79.65779467680608%\"\u003e\n \u003cp\u003eParticle Swarm Optimization- Energy efficient Clustering Sink Mobility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"2.\tLiterature Survey","content":"\u003cp\u003eDifferent researchers have developed numerous mechanisms on provide energy efficient clustering and secure data transmission in WSNs. Among them Wen, W et. al. [11] has proposed EAPC algorithm, which provides the energy aware data collection path to collect information from the single collection point and it\u0026rsquo;s forwarded to the next collection point through mobile sink. This proposed method reduces the consumption of energy and extends the lifetime of the sensor node in the Wireless sensor networks. Moreover this algorithm helps to reduce the traffic of the network and improves load balances. The limitation of this method is it takes more time to compute and construct the path by using loop free spanning tree algorithm. Dipak Kumar Sah et. al. [42] have have proposed the NEHCP algorithm for EH based on the EH rate of the sensor nodes, which selects the CH. This has data communication in between CH and BS using a single hop or several hops. Sethi, D [12] have proposed a framework related to mobile Base Station (BS). It employs 4 or 8 sojourn location path with one centralized static sink node, which is applied for static sensor nodes of both homogeneous and heterogeneous protocols to compare the energy consumption in WSNs. Compared with static sink, mobile sink is used to reduce the power consumption of sensors and extend their lifetime of WSNs.. The limitation of this system is reliability it has very less since mobile sink is prone to battle ad by the intruders. Shokair, M. et .al. [13] have proposed BEEMH protocol, especially designed for multi hop data transmission to optimize the energy constraint, extend their lifetime of the sensors in WSNs single hop information sharing between sensors to BS consumes high energy due to high distance. In order to avoid these issues, energy efficient multi hop communication protocol introduced with optimal route path selection. Moreover this BEEMH algorithm finds shortest path with minimum hop count by using dijkstra algorithm and calculate the weight of the path between nodes by using the nodes which has higher residual energy. Such nodes are act as relay node. Khan, F. A et. al. [14] have proposed AZ-SEP, which is designed for heterogeneous sensor nodes and routing protocols. In their protocol, the normal sensor nodes are directly communicated and data is sent to the sink. The nodes are grouped by clustering with multi hop communication from CH to BS using of hierarchical routing protocol. The advantages of this protocol is by reducing the nodes death, to increase the network stability, to increase the packet delivery ratio, improve the power consumption by prolonging the nodes lifespan in WSNs. The limitations are it has communication overhead during the routing process to transmit the sensed data. Chen, Z. et.al. [15] have proposed Protocol (GRMRP), which is used to optimize the consumption of energy using of multi hop communication and data aggregation method. In Their protocol network is partitioned into several virtual grids and one node is active that called as GH and remaining all nodes are keep in sleep made to save the consumption of energy in WSNs. In their system, the GH communicates to BS via multi-hop communication in hierarchical structure. Moreover, limitation in this protocol as grid supports only for small scale environment and provides less scalability. Basumatary, H et. al. [16] have proposed TDMS protocol, which is used to improve the energy efficiency by employing clustering method to enhance the life span of nodes in WSNs. In their protocol CHs are chosen randomly with threshold value, newly elected CHs broadcast the remaining energy to sink node. The sink node compares the CHs residual energy with threshold value and find out the nodes which has least energy of CH and the location with the help of GPS. Once they are detected least residual energy CH, the sink node is moved near to the least residual energy CH position to provide efficient communication among the nodes. Noureddine Moussa et. al. [45] have proposed ECRP-UCA which is unequal clustering, algorithm to handle the problems of uneven congestion control and optimum path finding in WSNs. In CH selection is based on remaining energy, frequency of surrounding nodes, sink distance, with a new feature called backward relay nodes which is introduced to balance load among CHs. The ant finds the ideal path based on the likelihood of nodes to optimise the ant\u0026apos;s seeking direction using the parameter namely NPPRN. Deepa, K. et. al. [17] have proposed Density based Fuzzy C Means \u0026nbsp;clustering protocol to improve the energy efficiency and optimize the lifetime of the WSNs. In their protocol, the CH are chosen by centroids of the grid environment. In their protocol, the sink node initiate the data communication by anywhere and anytime by using beacon signal broadcast to each sensor node and it is in the network. Whenever, the sensor nodes receives beacon signal, It became an active mode from drowsy mode and ready to sent the data with help of leader node. In this protocol the leaders are elected based on density of the network. The limitations are the message overhead due to broadcast the beacon message and lack of discovering optimal routing path in the network which leads for high delay. Wang, T. et. al. [18] has proposed GECR algorithm to discover optimal path between CM to the CH then BS. This protocol is employs fitness function to reduce the communication overhead in the network. The limitations are this protocol lacks reliability and security during the data transmission. Ivana Strumberger et. al. [44] has proposed protocol EHO to solve the challenge of locating sensor nodes in WSNs. It deals with optimization and NP-hard problems. There are tactics used by the clan\u0026apos;s leader female elephant. Others are usually females and babies, with male elephants leaving the settlement to live alone after they reach full maturity. Male elephants interact with the rest of the family using sound waves vibrations, despite the fact that they live individually. The limitation of this paper as coverage and energy efficiency in WSNs. Maurya, A et. al. [19] have proposed ROR model in order to improve the reliability, and to provide better security and user authentication against various types of attacks in the network. The advantages of this protocol are its enhanced security and the limitations are its overhead and lack of optimal route path discovery which consumes more energy in the nodes of WSNs. Khalid Haseeb et. al. [43] has proposed LSDAR method to enhance the efficiency of nodes and provides the better security against various malicious activities by utilising the XoR operation. In their protocol, nodes are divided into multiple uneven clustering sizes by using various radiuses. For non-loop routing paths, an A-star optimization technique is designed. Furthermore, employing OTP for node-to-node data transfer guarantees strict security against multiple attackers. The advantages of this protocol is its enhance in packet delivery ratio. The limitation of its protocol is its scalability. Sumalatha, M. S. et. al. [20] have proposed CLS-FTCM to extract the multiple parameters for trust computation to provide better safe guard against various attackers in WSNs. The proposed protocol employs ECNN classifier is to detect the malicious node and monitoring the fault in the network. The advantage of this protocol provides highly better security, and reduced overhead in the nodes of WSNs. Biswa Mohan Sahoo et. al. [46] have proposed a PSO-ECSM to address both the cluster head election and sink node mobility issues. In their protocol, for CH selection, five parameters are considered namely remaining energy, degree of node, nodes average energy, distance, and power consumption rate. The proposed protocol find the best score for those variables. Moreover, this algorithm introduces sink mobility to handle the issue of forwarding transmitted data in a multi-hop network. To make the network more cost effective, sink mobility is explored. Khan t. et. al. \u0026nbsp;[21] have proposed ETERS algorithm to provide the better shortest optimal secure route with low latency in the nodes of WSNs. The proposed protocol employs computed trust score value is to discover the optimal secure routing for data communication. Trust revealing algorithm is used to notice the internal hit of sensor node which improves the reliability and throughput. Moreover, the proposed protocol reduces the packet loss in the network.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe above survey depicts the various researcher\u0026rsquo;s contributions towards security, reliability, scalability, energy efficiency for the purpose of enhance the lifespan of the WSNs. Even though, there are several methodologies that are able to solve different problems it is evident that data transfer in terms of single-hop for long distance communication potential has been a drawback. Hence multi-hop based communication is preferred. Moreover, the majority of previous cluster-based secured protocol has significant computation and communication overhead, which is a serious drawback to enhance and optimize the lifetime of the nodes in WSNs.\u003c/p\u003e"},{"header":"3. Energy Model And Network Assumption","content":"\u003cp\u003eIn the proposed system, the propagation model is described in [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The sensor nodes sense the information that is send to base station by direct or hop by hop communication. Long distance with direct communication method consumes more energy. Hence for reducing the consumption of energy the multi hop communication method is preferred. During transceiving of data, sensor nodes consume energy. Measuring the distance between nodes and base station is very essential part for energy calculation. The proposed system employs Euclidean\u0026rsquo;s distance measurements for the two points (P,Q) in the network by using Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$d\\left(P,Q\\right)=\\sqrt{{({Q}_{x}-{P}_{x})}^{2}+{({Q}_{y}-{P}_{y})}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTo transmit n bit message from transmit node, it requires\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{trans}\\)\u003c/span\u003e\u003c/span\u003eenergy which is calculated by using Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{el}\\)\u003c/span\u003e\u003c/span\u003e represent the request of energy to operate the node to transmit and receive the n bit message, d is a Euclidean\u0026rsquo;s distance, d\u003csub\u003e0\u003c/sub\u003e is the threshold value TH, fs denotes free space, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{init}\\)\u003c/span\u003e\u003c/span\u003e is the initial energy of the nodes, mp denotes multipath.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${E}_{trans}=\\left\\{\\begin{array}{c}{E}_{el}*n+n* {e}_{fs}* {d}^{2} d\u0026lt;{d}_{0}\\\\ {E}_{el}*n+n* {e}_{mp}* {d}^{4} d={d}_{0}\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({E}_{rece}\\)\u003c/span\u003e \u003c/span\u003eReceive the n bit message which is computed by using the Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{usage}\\)\u003c/span\u003e\u003c/span\u003e is the total usage energy by a node and its computed by equation. (\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ E}_{res}\\)\u003c/span\u003e\u003c/span\u003e Where is the remaining energy of sensor node That is computed by using Eq.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${E}_{rece}={E}_{el}*n$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${E}_{usage}={E}_{trans}*{E}_{rece}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${E}_{res}={E}_{init}*{E}_{usage}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn every round, CH is selected by comparing threshold value\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(T\\left(n\\right)\\)\u003c/span\u003e\u003c/span\u003e with the nodes which has less residual energy than current CH and its computed by using Eq.\u0026nbsp;(\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$T\\left(n\\right)=\\left\\{\\begin{array}{c}\\frac{P}{1-P*\\left(r mod \\frac{1}{P}\\right)}* \\frac{{E}_{res}}{{E}_{init}} if n?S\\\\ 0 Otherwise\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe Assumption that is considered to designing the proposed protocol are first, the proposed protocol employs random deployment of sensor nodes during pre-deployment phase. Second, the deployment location of nodes and BS are static. Third, all deployed nodes have similar initial energy and nodes are homogenous in nature. Fourth, the proposed protocol employs symmetric radio links where one link is used for transmission and another link is used for reception. The last assumption is sensor devices must include enough computational power to carry out the fundamental cryptography operation, to perform encryption, key generation, decryption of sensed data.\u003c/p\u003e"},{"header":"4. Proposed System Architecture","content":"\u003cp\u003eThe architecture of this paper work is depicted the given Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is architecture of this proposed method have four major stages such as initialization of system and key generation phase, clustering phase, intelligent trust based discovery phase, final phase as data transmission and reception. In first phase, initially the sensor devices are distributed in the chaotic way in the distributed environment. The major purpose of this phase to create the public keys, private keys are generated by the BS. It is implanted to the respective sensor devices in pre-deployment phase. The proposed system employs the keys for secure data transmission in the network. In that clustering phase, CHs are elected based on Fuzzy temporal rules. Once the CH is elected, by using remaining energy, The clustering is created in try to reduce the number of redundant and unnecessary transmissions in the network. The next phase of the proposed system trust based optimal route discovery phase. Here, initially the trust values are computed for every nodes in the network. Based on the computed, the optimized route is discovered by employing modified sunflower optimization algorithm. The last phase of the proposed system is data transmission and data reception phase.\u003c/p\u003e \u003cp\u003eThe major aim is to afford efficient, secured data transmission. The proposed system employs multi party key exchange along with modified Elgammal digital signature to make sure the information integrity in the network. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e gives flowchart of the proposed protocol.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Fmsfo (Fuzzy Modified Sun Flower Optimization Method)","content":"\u003cp\u003eThe novel FMSFO protocol has been proposed in this paper to give better energy consumption and secured transmission of data in WSNs by means of efficient clustering method. The proposed FMSFO algorithm includes five major modules such as initialization of system and key generation phase, intelligent rule based fuzzy clustering phase, trust calculation phase, intelligent optimal route discovery phase by MSFO, Data transmission and reception phase by modified elgammal digital signature.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e5.1 System Initialization and Key generation phase\u003c/h2\u003e\n \u003cp\u003eThe first phase of the proposed system is system initialization and key generation phase. The major goal of this phase to make the public key and private key for all legitimate deployed nodes in the network. The generated public key and private key are used for encryption and decryption of transmitted data which ensures the integrity of the data which is transmitted from sensor node to BS. In this proposed protocol, BS generates the keys and these keys are installed into corresponding sensor nodes during pre-deployment phase. Algorithm1 gives the steps to be followed for the making of the private key by BS.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAlgorithm1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGeneration of Private keys\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eStep1: Begin\u003c/p\u003e\n \u003cp\u003eStep2: Choose the random prime numbers p,q,\u0026alpha; by using Pseudo random integer\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003efunction. Compute\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(?=\\left(p-1\\right)(q-1)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eStep3: Compute X\u003csub\u003eA\u003c/sub\u003e= (P \u003csup\u003e\u0026alpha;\u003c/sup\u003e) mod q\u003c/p\u003e\n \u003cp\u003eStep4: Compute X\u003csub\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/sub\u003e \u003cstrong\u003e= (\u003c/strong\u003eq \u003csup\u003e\u0026alpha;\u003c/sup\u003e) mod\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(⋋\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eStep5: Compute X\u003csub\u003eC\u003c/sub\u003e = (pq) \u003csup\u003e\u0026alpha;\u003c/sup\u003e mod pq\u003c/p\u003e\n \u003cp\u003eStep6: choose two random integer prime number X\u003csub\u003eg\u003c/sub\u003e, X\u003csub\u003ef\u003c/sub\u003e from z*(P)\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003ewhere z*(p) is a cyclic Group\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eStep7: Compute\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{d}={\\left({X}_{A}\\right)}^{{X}^{g}}mod p\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eStep8: Compute\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{C}={{(X}_{B})}^{{X}_{f}} mod pq\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eStep9: Compute\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{f}={\\left({X}_{d}{.X}_{C}\\right)}^{?} mod p\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eReturn Private key\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left\\{ {X}_{C},{X}_{f}\\right\\}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAANoAAAAlCAYAAAAk2fcXAAALzUlEQVR4nO2be3BTdRbHv2mBlvdL6IIVocmKsoBaFAqmFQt46yqITIGG5Y2KtyIMAkVFRQRdE1gHsPJyHFeHacJOYRUFU0GFJgtFLK3l2ZabtrRFoA1taekrbc7+EW/Iu2mSFpb9fWbuTJJ7fud3fr+Zc8/jdyMhIgKDwWhTgm63AQzG/wPM0RiMdoA5GoPRDjBHY/gO3UBOTg5OX7yKhttti68016DwfCZycgpQ34bTMEdj+ERNwS/QvJsAxZJNOPiffFTfboN8xWRE5k/7sHrhTKzceRCFtW0zjYR1HRmt5zq+W6PAW5lD8FnKDozpc7vt8R+T8C1enLccvRYcgHLRUIQGWD+LaIxWQ8XHoTsDjJq57K5wMgDoKJ2M2fJ+OKnbj9yawOvvEHiVjLudqtISGIqaMHpoX49yDdfykWWohERiBgB07jEII6TdUJB3DmU3AbPZjOCwBzE6orePljThinAGReW3KsSw8JEY3LMC2XklaDARGiQhCJcNQ0SfTi3oIvQOD4cp9woqagF089EkN/gV0eLi4iCRSFxeMpnMaz2JiYmQSCTQ6/X+mBMwbNel0WjafD53eyiRSJCYmOiVDr1e79Pe+0KzGQBVobqm0aNc/aVjSJo1GlFRURj37IvYdKQIElMtzu5bj2ejojDuJRV+zjf6YUkjCg4lY9YzUYiKisKU5dtw6FItcLMQB5Tz8MQT0ZiyLhV5ZZ7tFOkY2gVNN2pwvcoPk9zgl6NptVrodDrrd0EQQETgOA6CIEAikcBgMLSoR5QpKSnxxxwnVCqVT+O0Wm1A7WgJIoJSqQQASKVSEBHE0nn79u1eOY5cLrfqaGu69eiGrt0Bo/GmR7mej83G2hcnWILDfRPAz4oCuvZE504dYI6YCNXHW7Ca8+eh0AVj5yzHqudHAgCGjJmOOWP7At17oWPnXug7/lXsT16DuKHehCczaozXUNOtC3oHOJoBbVSjbdu2zfo5NTW1RXmtVgsiQkJCQsBs0Ov12LVrV8D03Q7UajUAywPsTon2ABAycAgGDuyASyVlaPYoGQz5jBWY+nAQzLnpOHG2ANl71uK1nUYsVH6CVU8P8t+YrsMQN/kZjOgPnPvxAE5dLUJK0hJ8XjQW23d8iJghnb1U1IDi4krcP+AhhIf5b5Yjd2UzxGAwIDo6+nabcffSZxD+EtYFxoKSFs/PQqSRmPocB9Rl4e9vzce6TfmYodyFTfEPBsgYCQbLJ2D88KGozlUjceYC7M17BO/8Yw2mybx1MgBmE4qEq+jT/wEMaAOvaBNHs60rkpKSoFKprPWDSqWyfo+Li7PWZ2I9pNFo7GoUjUZjV3+IaaZjLSPWUgaDAVKpFACs6auYQhoMBrsxrUEmk1nHidHFlT5bW23nFsd7W3MBgEKhAABwHAe5XN4qewHY7WVcXJzTfdu9d3e5pDIXJ/KbMHbscLTUYoAkDPInJ2LcvSG4lp6F0HnvYt2Mh1q9Fo/0H4eZsZHojSqcuWBC7PoNmB3puVHjRFAIIqNHo6koD2WBtc4C+YlOpyMAThfHcXZyHMdZ7ymVSuI4ziojlUoJAKnVaiIiUqvVTjqUSiUJgkBERDzPW+Vt5yciEgTBOl4qlTrZKerkOM7uviOiTtEmpVLptT7xnqN+nufdzqdUKl3uo1KptNsTT/ss6rCd13YNgaDm0vf09pRppHgvlUobvBtzVaukUf06EDr2pTnbs93KmW9k047ExbRo0SJavDiRtmpzvbSqltLfi6euQaDQ+2Jp54kat5LNVzLpi/cX08KFS2ht6m9290yVFyhl1TyKm76RTl03eTm3dwQ0oonNECJy21DgeR5JSUnQarVuZRISEiCVSpGWlmaNHoWFhYiIiABgqQHJTU0nyjiSkpICAIiNjbXKeVv7iFHo4sWLXumTy+XgeR6CIFgjmkqlsqtd3WHbDCEiJCUlAbDsidh4UqvVICLodDqkpaW57IyK0dbdPvlKp54PYNzEEegj6JF+3oiW3nYo/uljLFt/HkOiH0MfMuLsjz8jq855lPnMHiyc/ip25w3A9AXxeMB4Aceyy1vUD9RCv3kV3k/rhWeeDkN9sQ7HT+TghsuBRuxeOw9bskMhDWtEcbnt+yyEkpOHoC/vgZjnojGwc4CTPX891TaiiBHHFWJEE5/QtjhGNKJbT2ee50mn05FOp3Ma5xgFxPldRTTbiGp7uXvaO8p5itCu9AmCYBdpXa3b1Vo8RVlxr8U5xO/i3riKiq72TUTMDDxdrjHQjrlymvKOluo9rOnGuX/T6zHjadb6w1R1+V80Kbwrod+j9OY3xfaC9QLtfjmWeoc9T/sKLD8Je7+kvRlFZPagn6iJTn2xkiaPfIG2/HCRDNqV1B2g4EdepkOF9hGpuaaUjqduJK5/MI2ao6JjhWVUZ2q6JWCuos9mP05TXj9I1R7n9I07thkSHx8PwNLePnbsmF2NItZBhYWFdscLnhAjHc/zdhGjpae9Wq0Gz/NIS0uzOy5oSV9ERAR4ngdgqc/E9QQChUIBiUSCDRs2gIic6jepVApBEADAY1NIzAw8XS5pakBjJ0J59U23EafBcADrlq7F77HrsPPtCegxgMP8pwajQ1kWMnUnUW4zsObUQXyly8SfFyTihcGW3yKmzcW0MYMgAXDz0q/Q7tuHzNI6uzlyU9/Ams0nMOajT7B0khRDIp/H32RAc/YB7Dl61k7WXHMJ6Xu/xvHGcIQYs5CdV4nQDsG3BAiorqlAcHAz7GcJDHeso0VERIDjOADAoEH2beD58+cDAGJiYlo8e9Pr9dBoNIiJiQFgcVyxoaJSqbxKHbdt2wae57F69WprGumNvpUrVwKwOJq7lNYX1Gq1NbV2d04pprIAvD7P9Jorhci/3IyIYVKEON2sw+lvt2PR3New5cgFmIM7ggDUnU3Hkd+vQQLgSMoaLN18CJUmADDjSkERzp8PwlMxw11Odz1rLz5XKqH+7Y+T5KYiHNi6AnOXbMLR600I/cNf8vSHkW2WACjFHlUSPj5cYNXRISwK3JP90X9kLJZtTQE/yeH8LigUw0YMxuXfT6O0LV4s9iccukqfXBX8jimNbTrjmL7Ypp9iCuiI7Rjbz7Zpl5iO2qZ8jg0Fd+mc47oEQbDqs9XpjT6O4zymb0TOaSrcpLS2qaOYmoq2ODaleJ53sq8lO7yl6sQuSoh8ij44ctnFXRNdyz9FGRkZlJGRQdk5xVRHRKZruXTylxPW3/U5BVTXRETUQJnJL9EARNOuc9ctKhpMVF97q9NS/PWbFD9uBu06XWX5wVxF+b8es+q6UFRJRERGIZMyMixzHD9+nLIKK26ZZb5Bn74wgIZPXUFHr7le1+kvFtCYSa/QNwZ/d8gZv2s0hmdaqs1ag2ONJj7AAtlV9IaqjB00/ZHx9GH6lQBoa6TMT+fT/cFP0JclREQ36bu16ylZnUNNRNRYUUpHP3mVlr2/n8qaPVdsnmiuOkzz/zSEpr+hJaNLNc10cus0GjzxZdrfBo52x6aO/8vIZDLIZDLo9XqntNdXNBqNtd5SKBTQaDRISkqCVCqFQqFo1fmcv/SQDsWIoSHIzCwOgLaOkMXOxIqF3fH5W+9hw/K52PDLGZjuuxfBAJorKhEUOQ0r3pyMe4Jad/ZpCxXm4kyP3gga0Bd9XKoJQml+AWSySMja4M0QFtHaADH1dOxU3j3UUPrGeHp8/GRKyakNiMbqvJ8pOTmZNm7cSDv26qi8qjkgekXO75xJI8Y9SWt/MLq8X/LTZuLGjKDlXxkosCdoFpijMXzCVHGZvv9oKg0fxdEbyYfo6u02yC1mqqvIofWxj9KkxTtJcDxkr71IX374CskffpCW/jOTrgTmueEE+4c1w3eoHmXl1WgO6ox7+na7Q//cWI/fdm/Ehh03sECzHn8Nd/jvtLkRFcYyNCEEvfvd02ZrYI7GYLQDrBnCYLQDzNEYjHaAORqD0Q4wR2Mw2gHmaAxGO8AcjcFoB5ijMRjtAHM0BqMdYI7GYLQDzNEYjHaAORqD0Q78F86PI+vCHRXcAAAAAElFTkSuQmCC\"\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAlgorithm2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGeneration of Public keys\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eStep1: Begin\u003c/p\u003e\n \u003cp\u003eStep2: Choose the random prime integer numbers \u0026beta;,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{C},{X}_{f}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eStep3: Compute\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{a}= {P}^{\\beta }. {X}_{C} mod P\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eStep4: Compute\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{b}={q}^{p}.{X}_{f} mod q\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eStep5: Compute\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{C}= {Y}_{b}^{p}.{Y}_{a}^{q} mod pq\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eStep6: choose the two random prime integer\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{g},{Y}_{f} from {Z}^{*}P\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eStep7: Compute\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{d}=\\left({{Y}_{a}}^{{Y}_{g}} mod pq\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eStep8: Compute\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{C}=\\left({{Y}_{d}}^{{Y}_{b}} mod \\left(p-1\\right)\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eStep9: Compute \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{f}=({{Y}_{c}}^{{Y}_{d}} mod {Y}_{b}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eReturn Public P\u003csub\u003eu\u003c/sub\u003eK= {Y\u003csub\u003ec\u003c/sub\u003e, Y\u003csub\u003ef\u003c/sub\u003e }\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eThe generated public key is transmitted to the all the designated nodes in the network by means of secured multiparty key exchange. The proposed protocol employs the generated pair of Asymmetric keys for encryption and decryption purpose secured data transmission in WSNs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003e5.2 Intelligent rule based fuzzy clustering phase\u003c/h2\u003e\n \u003cp\u003eThe next phase of the proposed system is intelligent rule based fuzzy clustering phase. This phase which sensor nodes have more remaining energy and less hop distance is elected as cluster head (CH) and other remaining nodes acts cluster member (CM). In the proposed protocol the inter and intra-cluster communication are most preferred modes of communication for the nodes of WSNs. in intra-clustering, communication takes place between CM to CH via single hop, and in inter-cluster, the communication takes place between CH to BS via multi-hop. The proposed intelligent rule based fuzzy clustering, works on two phases namely cluster head selection by rule based fuzzy algorithm and cluster formation phase.\u003c/p\u003e\n \u003cdiv class=\"Section3\" id=\"Sec7\"\u003e\n \u003ch2\u003e5.2.1 Cluster head selection by rule based fuzzy algorithm\u003c/h2\u003e\n \u003cp\u003eIn the proposed protocol, for clustering of nodes it considers three input factor parameters such as remaining energy of node, degree of nodes, and distance from nodes to base station are the input variable to generate the fuzzy rules. For lingual version of node residual power has 3 levels namely low, medium, High and for node degree it represented as three levels namely large, moderate, small respectively, and for node distance to BS it is represented as close, approximate and far, respectively. The fuzzy rules outcome of node which is elected as a cluster-head is represented as 7 levels namely very small, small, rather minor, medium, rather heavy, large, and very large. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e gives the output of fuzzy rules for the nodes to elect as the cluster head.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAlgorithm3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eElection of Cluster head\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInput\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFuzzy input variable (R.E, node_degree, dist_to_BS)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOutput\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eElection of efficient CH based on chance value (CH\u003csub\u003e1\u003c/sub\u003e, CH\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e3\u003c/sub\u003e, \u0026hellip;\u0026hellip;\u0026hellip;.CH\u003csub\u003en\u003c/sub\u003e)\u003c/p\u003e\n \u003cp\u003e1: { List_CH\u0026thinsp;=\u0026thinsp;0, CV\u0026thinsp;=\u0026thinsp;0, count_CH\u0026thinsp;=\u0026thinsp;0, CM\u0026thinsp;=\u0026thinsp;NULL} //Parameter initialization\u003c/p\u003e\n \u003cp\u003e2: i\u0026thinsp;=\u0026thinsp;0,1,2\u0026hellip;..n //number of sensor devices in the network\u003c/p\u003e\n \u003cp\u003e3: vice_CH\u0026thinsp;=\u0026thinsp;FALSE\u003c/p\u003e\n \u003cp\u003e4: for each node do\u003c/p\u003e\n \u003cp\u003e5:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu =rand\\left(\\text{0,1}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e6: Node_state\u0026thinsp;=\u0026thinsp;CM\u003c/p\u003e\n \u003cp\u003e7: if (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu \u0026lt;th) then\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e8: vice_CH\u0026thinsp;=\u0026thinsp;TRUE\u003c/p\u003e\n \u003cp\u003e9: CV\u0026thinsp;=\u0026thinsp;fuzzy(R.E,node_degree,dist_to_BS) //calculate CV using fuzzy if rule.\u003c/p\u003e\n \u003cp\u003e10: End if.\u003c/p\u003e\n \u003cp\u003e11: End for.\u003c/p\u003e\n \u003cp\u003e12: Broadcast CH_msg (id,CV,R.E) to its neighbors\u003c/p\u003e\n \u003cp\u003e13: Each node j receives the CH_msg from node i\u003c/p\u003e\n \u003cp\u003e14: If( i(R.E)\u0026thinsp;\u0026gt;\u0026thinsp;j(R.E)) then\u003c/p\u003e\n \u003cp\u003e15: Vice_CH\u0026thinsp;=\u0026thinsp;FALSE\u003c/p\u003e\n \u003cp\u003e16: Quit the broadcast CH_msg.\u003c/p\u003e\n \u003cp\u003e17: End if.\u003c/p\u003e\n \u003cp\u003e18: If(vice_CH\u0026thinsp;=\u0026thinsp;=\u0026thinsp;TRUE) then\u003c/p\u003e\n \u003cp\u003e19: Node_state\u0026thinsp;=\u0026thinsp;CH\u003c/p\u003e\n \u003cp\u003e20: ADD i node as cluster member list (CH_list)\u003c/p\u003e\n \u003cp\u003e21: CH_list\u0026thinsp;=\u0026thinsp;CH_list\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e22: End if\u003c/p\u003e\n \u003cp\u003e23: If(node_state\u0026thinsp;=\u0026thinsp;=\u0026thinsp;CM) then\u003c/p\u003e\n \u003cp\u003e24: If (CH\u0026thinsp;=\u0026thinsp;=\u0026thinsp;TRUE)\u003c/p\u003e\n \u003cp\u003e25: Broadcast CH_msg with ID.\u003c/p\u003e\n \u003cp\u003e26: Node_status\u0026thinsp;=\u0026thinsp;CH\u003c/p\u003e\n \u003cp\u003e27: CH broadcast node status with ID\u003c/p\u003e\n \u003cp\u003e28: C\u003csub\u003ei\u003c/sub\u003e={CM\u003csub\u003e1\u003c/sub\u003e, CM\u003csub\u003e2\u003c/sub\u003e, \u0026hellip;\u0026hellip;\u0026hellip;CM\u003csub\u003en\u003c/sub\u003e}\u003c/p\u003e\n \u003cp\u003e29: Else\u003c/p\u003e\n \u003cp\u003e30: On receiving all CH_msg\u003c/p\u003e\n \u003cp\u003e31: My_CH\u0026thinsp;=\u0026thinsp;nearest_CH\u003c/p\u003e\n \u003cp\u003e32: Send join CH_msg(id) to nearest CH\u003c/p\u003e\n \u003cp\u003e33: End if\u003c/p\u003e\n \u003cp\u003e34: Exit.\u003c/p\u003e\n \u003cp\u003eBased on the fuzzy rule generated, the node which has low residual energy, high degree of connectivity, and minimum distance between BS to CM elected as CH. In Algorithm 3 gives the intelligent rule based fuzzy clustering process in the proposed protocol.\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eChance value for Fuzzy rule based algorithm\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResidual Energy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNode degree\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDist. BS to CH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChance value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproximate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery small\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproximate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRather minor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproximate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery small\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRather minor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproximate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproximate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRather minor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproximate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRather minor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRather minor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRather heavy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproximate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRather low\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproximate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRather heavy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery large\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApproximate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRather heavy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec8\"\u003e\n \u003ch2\u003e5.2.2 Cluster Formation Phase\u003c/h2\u003e\n \u003cp\u003eOnce the cluster head has been elected in the network, the next phase is cluster formation phase. In this phase, each node communicates to small set of nodes within its transmission range to form a clustering. BS randomly selects the CH by applying the rule based fuzzy algorithm, to broadcasts the CH_msg with the nodes within the transmission range. Which id\u0026rsquo;s to all the members for example if the nodes j wants to join the node i cluster member. The node j receives more CH_msg with its id connecting the recently joined ids. Only the remaining nodes that received CH_msg are removed from the node j Cluster member. Once the cluster was formed each CM transmit the sensed information to CH via multi-hop communication by TDMA (Time Division Multiple Access) schedule for transmitting the sensed data. In the proposed system, CH is rotated by applying the intelligent rule based fuzzy clustering algorithm by selecting threshold TH value with time T exceeds then the reduction of cluster head had take place by following algorithm3.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e5.3 Trust computation.\u003c/h2\u003e\n \u003cp\u003eThe next module of the proposed system is trust computation phase. The proposed protocol employs modified Sun flower optimization algorithm for routing and the trust parameters are include in MSFO. The modified SFO algorithm includes the trust parameters for discovering the optimal path during data transmission. In the proposed protocol the trust model has been coagulated with SFO algorithm to prevent the intruders to perform the various malicious attacks during optimal route discovery process. In the proposed system, for inter-cluster communication MSFO algorithm is employs for discovering the optimal path between CH and BS via multi-hop secure communication.\u003c/p\u003e\n \u003cdiv class=\"Section3\" id=\"Sec10\"\u003e\n \u003ch2\u003e5.3.1 Point to point trust\u003c/h2\u003e\n \u003cp\u003eEvery node x directly interacts with the other node y, by calculating the direct trust by Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) where PT stands for point to point trust, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{rv}\\left(x\\right)\\)\u003c/span\u003e\u003c/span\u003e represent node y that receives packets from node x and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({P}_{fd}\\left(y\\right)\\)\u003c/span\u003e\u003c/span\u003e represents node y forwarding packets to other node.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ7\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e$$PT=\\frac{{P}_{rv}\\left(x\\right)}{{P}_{fd}\\left(y\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"Section3\" id=\"Sec11\"\u003e\u003ch2\u003e5.3.2 Indirect trust\u003c/h2\u003e\u003cp\u003eNode x must determine the trustworthiness of node y in order to relay a packet, but it has no prior contact history of node y. In this example, node x obtains node y\u0026apos;s trust degree from its neighbours, which is determined using Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e\u003cdiv class=\"Equation\" id=\"Equ8\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e$$IT=\\frac{1}{K}\\underset{m=1}{\\overset{k}{?}}PT \\left(m\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere IT is the indirect trust, PT(m) is the point to point trust for the message m.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec12\"\u003e\n \u003ch2\u003e5.3.3 Packet drop trust\u003c/h2\u003e\n \u003cp\u003eThe packet drop trust by the way of previous packet drop history with neighboring nodes is computed by employing the Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ9\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e$$PDT=\\frac{1}{k}\\underset{m=1}{\\overset{k}{?}}\\frac{{PD}_{k}}{{PS}_{i,k}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere PDT is the packet drop trust, PD\u003csub\u003ek\u003c/sub\u003e packet drop in k number of nodes, PS\u003csub\u003ei,k\u003c/sub\u003e Packet successful ratio in between the node i to k.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec13\"\u003e\n \u003ch2\u003e5.3.4 Total trust\u003c/h2\u003e\n \u003cp\u003eIn the proposed protocol the overall trust score is computed by using Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). The trust score is 0 to 0.5 indicates low trust score and the trust score in between 0.6 to 1 indicates high trust score. Where (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha +\\beta +\\gamma )=1\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ10\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e$$Tot Trust=\\alpha PT+\\beta IT+\\gamma PDT$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"Section3\" id=\"Sec14\"\u003e\n \u003ch2\u003e5.3.5 Malicious node detection and isolation\u003c/h2\u003e\n \u003cp\u003eThe proposed protocol computes three level of trust namely point to point trust, indirect trust, and packet drop trust. Based on their computed trust scores, the total trust score is computed by combining the point to point trust score, indirect trust score, and packet drop trust score by Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec15\"\u003e\n \u003ch2\u003e5.4 Intelligent optimal route discovery MSFO algorithm phase\u003c/h2\u003e\n \u003cp\u003eIn the proposed system for an efficient inter-cluster communication, the MSFO is employed to discover the optimal path between CH to BS via multi-hop communication. The MSFO is swarm intelligence algorithm, all the moment are based on the orientation towards the sun, where the sun is the BS. The MSFO algorithm includes the trust value in order to locate the network\u0026apos;s rogue node\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAlgorithm 4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMSFO based optimal route discovery.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInput\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTotal_trust,dist_to_BS, R.E.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOutput\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOptimal secure Path selection (CH to BS)\u003c/p\u003e\n \u003cp\u003e1: Start\u003c/p\u003e\n \u003cp\u003e2: Parameter initialization\u003c/p\u003e\n \u003cp\u003e3: {Init_value\u0026thinsp;=\u0026thinsp;0, X\u003csup\u003e*\u003c/sup\u003e =0, t\u0026thinsp;=\u0026thinsp;0, n, max}\u003c/p\u003e\n \u003cp\u003e4: Generate population\u003c/p\u003e\n \u003cp\u003e5: X\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(t)\u003c/sup\u003e i\u0026thinsp;=\u0026thinsp;0,1,2,3\u0026hellip;\u0026hellip;..n\u003c/p\u003e\n \u003cp\u003e6: f(X\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(t)\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;high(Total_trust)\u0026thinsp;+\u0026thinsp;close(dist_to_BS)\u0026thinsp;+\u0026thinsp;max(R.E)\u003c/p\u003e\n \u003cp\u003e7: if(Total_trust\u0026thinsp;=\u0026thinsp;=\u0026thinsp;0) then\u003c/p\u003e\n \u003cp\u003e8: Find out malicious node inform to BS\u003c/p\u003e\n \u003cp\u003e9: BS broadcast the malicious node id break the connection from safe node to\u003c/p\u003e\n \u003cp\u003eantinode.\u003c/p\u003e\n \u003cp\u003e10: End if\u003c/p\u003e\n \u003cp\u003e11: Evaluate new CH or Vice_CH fitness function.\u003c/p\u003e\n \u003cp\u003e12: X\u003csup\u003e*\u003c/sup\u003e= f(X\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e(t)\u003c/sup\u003e).\u003c/p\u003e\n \u003cp\u003e13: update the new CH route if their fitness are better than the current values\u003c/p\u003e\n \u003cp\u003e14: If( t\u0026thinsp;\u0026gt;\u0026thinsp;max) then\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e15: Repeat calculate fitness function\u003c/p\u003e\n \u003cp\u003e16: Until condition satisfied\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e17: t\u0026thinsp;=\u0026thinsp;t\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e18: End if.\u003c/p\u003e\n \u003cp\u003e19: Exit.\u003c/p\u003e\n \u003cp\u003eDuring the optimal route discovery process, the proposed MSFO identifies the node which has low trust score between 0 to 0.5 and it immediately report to the BS. In return the BS black list the corresponding nodes based on their nodes ids and prevent them to participate in the packet routing process during inter-cluster communication. Then it broadcast the information of the compromised nodes ids to all the legitimate nodes in the network. By doing so, the nodes communicate only with the other nodes which have low trust score are separated from the nodes which has high trust score for efficient identification of malicious network nodes. The proposed Modified SFO protocol chooses alternate optimal path from the malicious nodes for providing secure data transmission by detecting the malicious nodes during the route discovery process.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec16\"\u003e\n \u003ch2\u003e5.5 Modified Elgammal DS Data transmission\u003c/h2\u003e\n \u003cp\u003eThe next method of the proposed algorithm is modified elgammal based digital signature data transmission phase. Moreover, the digital signature algorithm employed provides high security during data transmission phase. Here, the original message (M) digest with hash function using H(M) and encrypts the hash message with sender private key as signature and the transmit the cipher text to receiver. Algorithm \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e gives modified elgammal based digital signature algorithm gives the steps\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAlgorithm 4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eModified elgammal based digital signature algorithm\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eBegin\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eChoose plain text message M\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eApply hash function using SHA1 algorithm H(M)\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eselect {q, \u0026alpha;, X\u003csub\u003eA\u003c/sub\u003e, Y\u003csub\u003eA\u003c/sub\u003e}\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eselect (M) 0\u0026thinsp;\u0026lt;\u0026thinsp;M\u0026thinsp;\u0026lt;\u0026thinsp;q-1\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 6\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ecalculate {K,K\u003csup\u003e-1\u003c/sup\u003e}\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 7\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCompute gcd(K,q-1)\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 8\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCompute S\u003csub\u003e1\u003c/sub\u003e\u0026amp; S\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$${S}_{1}={a}^{K} mod q$$\u003c/div\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 9\u003c/strong\u003e:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({K}^{-1}= {K}^{-1} mod (q-1)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equb\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$${S}_{2}={K}^{-1}\\left(M-{{X}_{A}}^{S1}\\right)mod (q-1)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eStep 10\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003esignature pair as (S\u003csub\u003e1\u003c/sub\u003e, S\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec17\"\u003e\n \u003ch2\u003e5.6 Data Reception and verification\u003c/h2\u003e\n \u003cp\u003eThe next module of the proposed system is information reception and verification, in this phase once receiver receives the cipher text it performs decryption by senders public key, then apply hash algorithm to separate the signature and H(M). Then the receiver verifies the packets by validating the sender digital signature and by verification algorithm. When both hash messages are same, then the message is valid otherwise sender discards the message from the network.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eChoose the prime number {\u0026alpha;, q}\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCompute {V1,V2} using public key of Y\u003csub\u003eA\u003c/sub\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 3\u003c/strong\u003e:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({V}_{1}= {a}^{M} mod q\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equc\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$${V}_{2}= {\\left({Y}_{A}\\right)}^{{S}_{1}}{\\left({S}_{1}\\right)}^{{S}_{2}} mod q$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eStep 4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eV1\u0026thinsp;=\u0026thinsp;V2 message is verified\u003c/p\u003e\n \u003cp\u003eIf(V1\u0026thinsp;=\u0026thinsp;=\u0026thinsp;V2) then\u003c/p\u003e\n \u003cp\u003eAccept the message\u003c/p\u003e\n \u003cp\u003eelse\u003c/p\u003e\n \u003cp\u003eIgnore the message\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStep 5\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eend\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"6. Simulation Setup And Performance Evaluation Parameters.","content":"\u003cp\u003eIn the proposed protocol, the assumption is that the sink node is the high resourceful device which consists of continuous power supply and cannot be composed by the intruders. The deployment BS and sensor devices are static in nature. The sensor nodes initial energy is considered as 1 joules and BS initial energy as considered 100 joules. The energy is consumed by the nodes is determined by the amount of energy on spent transmitting and receiving packets. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003egives the network simulator parameters employed in the proposed system. During their lifetime each sensor node is capable of performing fundamental cryptographic operations. The proposed FMSFO model is implemented by using NS3 simulator tool. The suggested procedure was assessed using performance indicators, energy consumption analysis, PDR ratio, network lifetime, and to end delay analysis, residual energy analysis, malicious nodes detection accuracy.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNetwork simulation parameters\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNetwork Area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000 * 1000 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. of nodes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInitial energy of nodes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 J\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCH threshold value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of malicious node %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;50 nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize of the packet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4096 bits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003eelec\u003c/strong\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 nJ/bit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026epsilon;\u003csub\u003efs\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 pJ/ bit/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026epsilon;\u003csub\u003emp\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0026 pJ/bit/m\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransmission energy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48 J\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eReceiving energy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12 J\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunication range of CH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCommunication range of CM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec19\"\u003e\n \u003ch2\u003e6.1 Simulation results\u003c/h2\u003e\n \u003cp\u003eThe proposed FMSFO protocol is compared with the existing protocol namely CSDP [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], ERF [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] and RSA [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e gives the energy consumption analysis of FMSFO protocol then that is compared with other existing protocols namely CSDP [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], ERF [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] and RSA [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. In that below graph, it is understandable that the proposed FMSFO algorithm has better energy consumption then that is compared with other existing protocols. The reasons for the improvement is that the proposed protocol employs intelligent rule based fuzzy clustering algorithm and discovers the optimal routing path by employing trust based modified sunflower optimization algorithm. Moreover, the proposed protocol employs modified elgammal digital signature for ensuring the inputs of the data then that is transmit to network. In the proposed protocol provides efficient clustering efficient trust computation and discovery optimal routing path by detecting the rough nodes are blacklisting then during the routing process. Hence, the proposed protocol improves the energy consumption upto 3 to 6% then that is compared with other former protocols. Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows that consumption of energy in the presence of malicious nodes. Even in the presence of malicious nodes the proposed FMSFO protocol perform better with optimized energy consumption when it is compared with existing algorithms.\u003c/p\u003e\n \u003cp\u003eIn Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e depicts the analysis of packet delivery ratio of the proposed protocols in terms of packet delivery ratio when it\u0026rsquo;s compared with other existing protocols namely RSA, ERF, and CSDP by varying number of nodes in the network. The proposed FMSFO algorithm has better packet delivery ratio then that is compared to other existing protocols. The main goal for that improvement is that discovers the optimal path by employing MSFO algorithm along with trust factor to find out the malicious nodes in network. Moreover, the proposed protocol prevents the nodes to drop the packets by find out the malicious nodes and prevents them taking path in the routing process. Hence, the proposed algorithm gives better packet delivery ratio, when it is compared with other former protocols. Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the analysis of packet delivery ratio by varying the number of malicious nodes in the network. Even in the presence of malicious nodes the proposed method obtain better packet delivery ratio compare to all other existing methods.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e gives the average throughput analysis of the proposed protocol with other existing protocols. From the graph it is clear that the proposed protocol has better throughput since it employs modified sunflower optimization algorithm to perform efficient routing.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e shows the average throughput analysis of the network by varying number of malicious nodes, even in the presence of malicious nodes the proposed protocol achieves better throughput.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePerformance analysis of proposed protocol and existing protocol referred by time and overhead\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProtocol name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNetwork Stability Time (sec)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSystem Overhead (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCH formation time (ms)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCH existence time (sec)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRSA\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eERF\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCSDP\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFMSFO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e depicts the performance variation between the existing protocols RSA, ERF, CSDP when it is compared with the proposed protocol FMSFO. The proposed protocol reduces the system overhead by 2\u0026ndash;6 percent and formation of cluster time as 1\u0026ndash;5 ms and also improves the stability time of the network and also CH existence time in the network.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTime Space and Crypt Complexity Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime Complexity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpace Complexity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrypt analysis Complexity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRSA\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO(n\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO(n\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiscrete logarithmic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSanthosh\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO(n\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO(n\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolynomial Time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCSDP\u003c/strong\u003e [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO(n log n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eO(n\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiscrete logarithmic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFMSFO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiscrete logarithmic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e compares the time and space complexity of existing protocols RSA, ERF, and CSDP when it is compared with the proposed protocol in terms of computation time, memory space, and the analysis of crypt analysis complexity. Based on analysis, the proposed protocol has better time and space complexity and has better crypt analysis complexity when it is compared with other existing protocols.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"7. Conclusions And Future Work","content":"\u003cp\u003eThe proposed protocol focused on trust score secure data communication with efficient clustering and optimal path finding using optimization algorithm in order to achieve better network performance. To detect the malicious nodes from the normal nodes the proposed system employs MSFO proposed algorithm with better secure data communication by using Elgamal digital signature algorithm. Furthermore, for data transfer, a reliable and secure optimum routing method is selected to decrease latency while providing strong authenticity across numerous adversaries. The outcomes of this study demonstrated that the proposed FMSFO outperforms other existing protocols in terms of security by employing effective and convenient encryption and decryption scheme, a trust-based malicious node detection scheme, and effective node authentication, as well as energy consumption, reduced delay, increased packet delivery ratio, high throughput, and enhance the network lifetime. The proposed protocol may be improved further by using meta-heuristic optimization protocol to provide better communication and reduce complexity in WSNs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to participate:\u0026nbsp;\u003c/strong\u003eAll the authors has given ethical approval and consent to participate in publishing for this journal\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman and Animal Ethics\u003c/strong\u003e: \u0026nbsp;This article does not contain any studies with human or animal subjects performed by any of the authors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e : All the authors have given consent for the publication\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of supporting data\u003c/strong\u003e : Data sharing is not applicable to this article as no new data were created or analysed in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e: There is no competing interests among authors and co-authors of the given Manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e: Funding not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: The first author Dinesh K has carried out simulations and literature survey and Santhosh Kumar SVN has carried out problem formation and algorithm design\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e: Not Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Information\u003c/strong\u003e : Can be included at the later stage\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMohamed, R. 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A novel efficient clustering protocol for energy harvesting in wireless sensor networks. Wireless Networks, 26(6), 4723-4737.\u003c/li\u003e\n \u003cli\u003eHaseeb, K., Islam, N., Saba, T., Rehman, A., \u0026amp; Mehmood, Z. (2020). LSDAR: A light-weight structure based data aggregation routing protocol with secure internet of things integrated next-generation sensor networks. Sustainable Cities and Society, 54, 101995.\u003c/li\u003e\n \u003cli\u003eStrumberger, I., Bacanin, N., \u0026amp; Tuba, M. (2017, December). Hybridized elephant herding optimization algorithm for constrained optimization. In International Conference on Hybrid Intelligent Systems (pp. 158-166). Springer, Cham.\u003c/li\u003e\n \u003cli\u003eMoussa, N., \u0026amp; El Belrhiti El Alaoui, A. (2021). An energy-efficient cluster-based routing protocol using unequal clustering and improved ACO techniques for WSNs. Peer-to-Peer Networking and Applications, 14(3), 1334-1347.\u003c/li\u003e\n \u003cli\u003eSahoo, B. M., Amgoth, T., \u0026amp; Pandey, H. M. (2020). Particle swarm optimization based energy efficient clustering and sink mobility in heterogeneous wireless sensor network. Ad Hoc Networks, 106, 102237.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Internet of Things (IoT), Wireless Sensor Networks (WSNs), Clustering, Security, Modified Sun Flower Optimization (MSFO), Elgamal Digital Signature","lastPublishedDoi":"10.21203/rs.3.rs-1741631/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1741631/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInternet of Things (IoT) is a superset of Wireless Sensor Networks (WSNs). Due to its distributed nature, it can be employed across different hazardous environments. In WSNs, devices are treated as various resource factors such as computing resources, sources of energy, storage systems, and security resources, necessitating the use of robust dynamic routing protocol. In most significant breakthrough of wireless data transmission the devices are capable of transmitting data with low cost and very minimal power supply in order to enhance network lifetime and optimization of energy consumption in the network. Any intrusion to data and network security might have a real and physical impact on network quality and affects the security to the network will deployed by the sensor nodes for remote monitoring in unauthorized environment. Pertaining to the geographical location, WSNs plays a wide open role in addressing the modern day challenges all around the world. However in WSNs, the selection of the optimal path with reduced energy and providing optimized security still poses a major challenge. To address this challenge, a methodology in this work has been proposed by choosing multiple trust factors to detect the malicious node, thereby electing a efficient cluster head by using Fuzzy temporal rules with Modified Sun Flower Optimization (FMSFO) algorithm. Moreover, the proposed system produces high reliability and security by employing modified Elgamal Digital Signature algorithm for secured data transmission for the nodes of WSNs. The proposed system provides high potentiality against various types of attacks by reducing the packet loss in indentify the malicious node, resulting in a better QoS and enhance the lifetime of the sensor nodes. The proposed protocol is implemented in NS3 simulator by using realistic simulation parameters. The simulation shows that the proposed method is efficient in terms of energy efficiency, throughput, PDR ratio, delay and provides better security when it is compared with the other existing state of art systems.\u003c/p\u003e","manuscriptTitle":"Trust Aware Secured Energy Efficient Rule based Fuzzy Clustering Protocol with modified Sun Flower optimization Algorithm in Wireless Sensor Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-21 16:16:43","doi":"10.21203/rs.3.rs-1741631/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf6f72df-48e0-4780-a75d-1681ffe91eeb","owner":[],"postedDate":"June 21st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-11-15T03:59:21+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-21 16:16:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1741631","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1741631","identity":"rs-1741631","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}