HOP Count Based Energy Efficient Cluster Head Selection Using Leveling and Sectoring

HOP Count Based Energy Efficient Cluster Head Selection Using Leveling and Sectoring | 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 HOP Count Based Energy Efficient Cluster Head Selection Using Leveling and Sectoring Mohd Nazeer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3183581/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 WSN are mostly deployed in sensitive location where battery replacement is impossible, which lead to implementation of an energy efficient protocol to extend network life time. In unsupervised machine learning, clustering based hierarchical routing protocols the dissemination of energy occurs mostly due to local communication and cluster head selection, roles of the nodes are changed as cluster member or cluster head to solve fusion and localization problem and manage energy dissemination and increasing the network life time. Nodes can communicate with each other directly or indirectly manner. In this paper the proposed LS-LEACH algorithm solved the problem of optimized cluster head selection by using leveling and sectoring protocol. In LS-LEACH the deployment area is divided into levels and sectors, and selection of the cluster head is done by using hop count matrix. LS-LEACH has been evaluated by performing simulation in OMNET + + simulator and equated with existing EERMS algorithm by considering different metric such as last node death, first node death and network life time. The proposed algorithm can be utilized in various smart IoT based application for optimal cluster head selection. Clustering cluster head selection network life time fusion localization Figures Figure 1 Figure 2 Figure 3 1. Introduction Wireless sensor network is projected to be most research oriented technology. In such communication networks, sensor collect, process and transmit/receive the sensed reading of physical variables such as temperature in many applications of WSNs manual battery replacement is impossible or totally not feasible. Hence researches were motivated to design energy efficient communication protocols. One such early protocol, namely (LEACH) is based on the fact that the sensors which are close by record/measure similar sensed reading [ 1 ]. Thus, it is enough for one leader node to collect process and transmit/receive the sensed reading technically the group of nodes is called a cluster and the leader node called “cluster head” [ 2 ]. With such motivating idea, various related protocols like PEGASUS, LEACH, and LEACH Energy Aware were proposed and implemented [ 3 ]. These protocols implicitly assumed (in the early versions) that the sensors in a cluster are one-hop from each other. In updated versions of these protocols, such an assumption is relaxed. Early on the authors realized that there are many real world WSNs, the sensor nodes are single hop or multiple hops from one and another, with such motivation, the Euclidean distance between sensors and cluster head was utilized to formulate and solve the cluster head placement problem. But, from practical considerations, it is clear that the wireless hops between sensor nodes and cluster head must be taken into account rather than the Euclidean distance. Also, the energy savings achieved over the entire network was never estimated. Furthermore the author proposed an energy efficient algorithm for routing, localization and fusion, called leveling and sectoring algorithm. This research paper combines clustering based on wireless hop-count and leveling sectoring over the entire sensor field. Since due to the increasing in the number of sensor nodes with in a cluster considering single hop communication consumes more energy as the distance between cluster member and cluster head increases, therefore instead of considering the distance for the selection of the cluster head in this proposed mechanism we will consider number of hops with in the cluster using hop count matrix. The proposed algorithm is evaluated mathematically(quantification) in [ 7 ]. In this research article we are evaluating it by performing simulation using various constraints and it is shown through simulations that the energy saved in transmissions/receptions is significant. 2. Literature Survey [ 4 ] It uses greedy algorithm to form the clusters and cluster head, dividing into sectors by using sensor nodes are homogenous. It considers both static and mobile nodes. The simulated is done by using MAT lab. [ 5 ] Each node can reduce energy consumption, the Location Sensitive Routing Protocol (PRRP) is suggested the sensor node listening is reduced by idle listening. It is providing the CH selecting mechanism which increases network throughput on packet basis and it is a primary contribution of PRRP. [ 6 ] The network model for the execution of PRRP is an architecture where it considers a uniform randomly distributed sensor nodes where nodes are randomly distributed based on a grid format in a sensor region. The sink static is situated in middle of their network, and on both sides of the sink, nodes in the network is split into separate clusters of groups. Normally, depending on network size, the levels is specified in radius across the base station with tier D 0 , D 1 .. etc. Each tier covers more than one grid (partially or fully) and has a logical range in which, depending on the distribution of the nodes, the number of nodes lies. Compared to current routing protocols such as PRRP, CELRP and LEACH and its efficiency display better results with outstanding network lifetime improving and network throughput. [ 7 ] The rectangular regions is divided the network in the proposed work and each region consists of one cluster head (CH). The firefly optimization algorithm inspired by nature is used to choose cluster heads where the decisive parameters of the process are residual energy, average node to node distance and distance from the node to sink. After estimating the centroid position of the CHs, the sink moves into the observing area. Using the Mat lab simulation platform, the output of the implemented protocol is compared with the existing protocols. [ 8 ] This paper illustrates and addresses the design challenges for cluster-based systems, the significant parameters of cluster creation, and the classification of protocols for hierarchical clustering. In addition, by considering those criteria, current cluster-based and grid-based techniques are evaluated to assist users in choosing suitable techniques. In addition, in specific situations, a comprehensive overview of these protocols is provided with their benefits, drawbacks, and applicability. [ 9 ] The leveling and sectoring algorithm is used to divide the rectangular region into level and sector by using signal strength or hop count based method and unidirectional antenna. It will apply leveling and sectoring constraints to stop the reverse propagation of the packet during the broadcast and collectcast. Exponential amount of energy is saved by reducing the number of packet transmission and reception; it is compared with various data centric algorithm such as gossip and direct diffusion. [ 10 ] In this paper its compared various hierarchical routing protocols such as PEGASIS,COSEN and IECBSN by consider the sink as mobile node and the mobility of the sink is considered under various scenarios such as circle, rectangle and random). Whenever the sink changes its position it will inform to its neighboring nodes by broadcasting message. The various protocols are compared by using performance metric such as average energy consumption and number of rounds. It has been observed EAPHRN perform with number of rounds and less average energy consumption. The above existing cluster head selection algorithm has been implemented by considering the communication between the cluster members and cluster head at single hop but if the distance between cluster members and cluster head increases with in the cluster then it consumes more energy so instead of going for single hop communication between cluster head and cluster members we will perform multi hop communication in the proposed algorithm. 3. Proposed system Clustering based upon HOP Count considering Leveling and sectoring : In traditional clustering algorithms such as LEACH and its variants it is implicitly assumed that the sensor nodes are one wireless hop from each other (at a certain power level utilized by the sensor nodes). Thus, in all such hierarchical routing algorithms, the physical distance or the wireless distance (i.e. number of hops at a between sensors fixed power level) is not considered to determine the cluster head in recent research works [ 2 ][ 3 ][ 4 ], the authors reasoned that taking the Euclidean distance or hop count between the sensor nodes in a cluster, definitely results in energy efficient clustering there by the life time of network is prolonged. In the proposed algorithm the network is divide into level and sectors by using L & S algorithm [ 13 ], once L & S is done successfully in every sector, the cluster formation will occur by using the setup and steady phases. During the setup phases the hop count matrix is used for the selection of the cluster head, in the hop count matrix process it count the number of hops needed to reach from one sensor node to another sensor node, the node with minimum hop count value to reach all the nodes in the cluster and having energy level above threshold value will be selected as the CH. Once CH selection is done its complete the setup phase in the steady phase the cluster members send the sensed value to the cluster head and cluster head will forward the value to the sink it completes the round in the next round again it performs setup phase for the selection of the cluster head based upon hop count matrix and remaining energy of the node. The quantification evaluation of the proposed leveling and sectoring algorithm and hop cunt matrix has been done in the previous research papers [ 11 ] [ 13 ]. Consider 8*8 grid as shown in the figure above, each lattice point containing a sensor node. Divide it into levels[L 1 ,L 2 ,L 3 ,…..L 8 ] and sectors [S 1 ,S 2 ,S 3 ,…..S 8 ] as shown in figure. Broadcast constraint: The packet has to be transmitted from lower level to higher level reverse transmission of the packet has to be dropped. Collect cast constraint: The packet has to be transmitted from higher level to lower level reverse transmission of the packet has to be dropped. The various steps performed in the setup phase are as shown below in the algorithm. Step-1: Divide the network area into level and sector by using L&S algorithm. Step-2: Apply the L & S constraints for broadcast and collectcast Step-3: Forms clusters with in the sector and the sensor nodes are multi sensor nodes from one and another. Step-4: Perform row sum hop count matrix for each node in the cluster. Step-5: Select the node with hop count minimum and maximum energy value as the CH. Step-6: Now cluster head will forward their value to the sink. Step-7: This complete a round in the next round again it will perform setup and steady phases. Algorithm-1 of LS-Leach Step-1: consider the case where sensor nodes are multiple wireless hops from each other Step-2: let all the sensor nodes initially have same energy Step-3: In wireless hops utilize the concept of center of graph to locate the optimal Location. Step4: determining the center of associated graph utilizing the hop count matrix. Step-5: Consider the hop count matrix and determine the row sums i.e. the sum of hop counts of all nodes from the jth wireless sensor node. Step-6: Declare the cluster head as the wireless node for which the row sum of hop count matrix is minimum among all rows. Algorithm-2 HOP count matrix algorithm The above two algorithms are utilized for the formation of the clusters and selection of the cluster head. The cluster head selection is done by using algorithm-2. It is utilized were the communication between the cluster members and cluster head is at multiple hops. LS-Leach in IoT: The proposed algorithm can be utilized in the IoT based application for optimal cluster head selection as the number of nodes with in the cluster will be at multiple hops. It will provide significant reduction of the energy utilization since cluster head is located at minimum hop count cluster members when compared to the other existing algorithm. System model and simulation environment The deployment of the existing and LS-Leach is shown in the figure below Simulation results The OMNET + + simulator is used for simulation by considering different parameters as shown in the table below. The simulation has been done by changing the coverage region of the sensor nodes as mentioned in the table such as 100,150,200 and 250 meters, the default energy and depletion in the energy formula is also mentioned in the table. Table 1 Parameters Of Simulation The dissemination of the nodes energy is done due to the following operation Parameter Values R E 50 nJ/bit A E 100 pJ/bit/m 2 C E 5 nJ/bit/message Packet Size 2000 bit Sensor nodes (N) 30 Initial energy 6 Jouls Coverage region 200,500,700 and 1000 m 2 The dissemination of the nodes energy is done due to the following operation Energy Dissemination Formula Transmission energy of n bit R E * n + A E * n * pow(d,2) Reception energy of n bit R E * n Aggregating k messages of n bits C E * n*k R E : energy reduction for radio operation A E : energy reduction for radio amplifier C E : energy reduction for message aggregation The result of the simulation obtained by varying coverage region is mentioned in the table below. It is clearly observed that the death of the first node occurs after more number of rounds in the proposed system when compared to the existing, even the sensor node area of coverage is increased the number of rounds is more in the LS-Leach compare to the existing approach. The another performance metric used for evaluation is the end time and it is observed that simulation run for more time with more number of rounds in the proposed compared to existing, therefore this increase the network life time. The simulation of the network has been done for both homogeneous and heterogeneous network by considering energy. The result obtained is provided in the tables Table 2 Result of different Leach Protocols for homogenous sensor nodes Edges Metrics Leach-LS EERMS 200 First node 942 495 End node 1531 972 Rounds 1165 608 500 First node 177 92 End node 269.58 174.42 Rounds 205 109 700 First node 90 48 End node 142 96.02 Rounds 108 60 1000 First node 46 21 End node 74.95 56.2 Rounds 57 32 Table 3 Result of different Leach Protocols for Heterogeneous sensor nodes (3.0–6.0 Joules) Edges Metrics Leach-LS EERMS Direct 200 First node 579 309 196 End node 1349 856 584 Rounds 1026 535 365 500 First node 87 43 29 End node 239 156 94 Rounds 182 98 59 700 First node 53 27 15 End node 119 76.84 46.42 Rounds 91 48 29 1000 First node 24 11 7 End node 61.8 40.02 22.82 Rounds 47 25 14 The graph has been plotted based upon result values mentioned in the table. It can be clearly observed that the proposed system increases the network life, number of rounds by utilizing energy in efficient manner compared to the existing system. 4. Conclusion In the proposed algorithm we apply leveling and sectoring to divide the network area into level and sector, and then in each sector consist of clusters. The cluster head is selected in each cluster is by considering hop count matrix which provide the node with minimum hop count value in the cluster. The proposed cluster head selection mechanism is compared with EERMS by considering different performance metric number of rounds and death of last, first node. We observed that the proposed mechanism provide death of last and first sensor node occurs after more time for both homogenous and heterogeneous network when compared with EERMS. The simulation has been carry out by varying the coverage region of the sensor nodes and same results has been observed that the dead of the first node and last node occurs after more time and more number of rounds. Since we are reducing the energy consumption in communicating with the cluster head by selecting optimized cluster head it increasing the life span of the sensor nodes and the network. LS-LEACH can be utilized in IoT based applications were we required the dying of the nodes should occur after more number of rounds or time such as smart health care system [ 14 ]. In future we wanted to utilized the proposed algorithm along with bio sensors for smart health care monitoring system using deep learning model on larger real time dataset to monitor health condition specifically mental health for stress detection using CCTV video, pulse rate, blood pressure and cortisol level of the patient remotely. References R. Ramya, Dr. T. Brindha,A Comprehensive Review on Optimal Cluster Head Selection in WSN-IoT, Advances in Engineering Software, Volume 171,2022,103170, ISSN 0965–9978, https://doi.org/10.1016/j.advengsoft.2022.103170 . Tay, M., Senturk, A. A New Energy-Aware Cluster Head Selection Algorithm for Wireless Sensor Networks. Wireless Pers Commun 122, 2235–2251 (2022). https://doi.org/10.1007/s11277-021-08990-3 Jayaraman, G., Dhulipala, V.R.S. FEECS: Fuzzy-Based Energy-Efficient Cluster Head Selection Algorithm for Lifetime Enhancement of Wireless Sensor Networks. Arab J Sci Eng 47, 1631–1641 (2022). https://doi.org/10.1007/s13369-021-06030-7 Jin Wang 1,2,3, Yu Gao 2, Wei Liu 2, Arun Kumar Sangaiah 4 and Hye-Jin Kim 5,* “Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks” Sensors 2019, 19, 1494; doi: 10.3390/s19071494 , March 2019 Garimella Rama Murthy 1 Mohammed Nazeer 2, Padmalaya Nayak 3, “Energy Efficient Design Of Mobile Wireless Sensor Networks: Constrained Clustering”. International Journal of Innovative Technology and Exploring Engineering (IJITEE), Scopus, May2019 Mohammed Nazeer 1 *, Garimella Rama Murthy 2, Aishwarya Jain3 “Energy Efficient Clustering In WSN Using Weighted Centriod)” International Conference on Soft Computing and Signal Processing Aug:21–22 Springer, goggle scholar, Scopus, dblp. Garimella Rama Murthy 1, Mohammed Nazeer 2, Dr. Tata Jagannadha Swamy “Energy Efficient Clustering in Real World Wireless Sensor Networks Implementation” International Conference on Soft Computing and Signal Processing Aug:21–22 Springer, google scholar, Scopus, dblp. “Energy Efficient Hierarchical Clustering Approaches in Wireless Sensor Networks: A Survey”,Volume 17-Bilal Ja,Haleem Farman,2 HumaJaved ,2 BartolomeoMontrucchioMurad Khan and Shaukat Ali(HINDAWI-2017) “Retracted: Enhancing energy efficiency of WSN through the Design of Energy efficient routing protocol” Noor Zaman,1 Low Tang Jung,2 and Muhammad Mehboob Yasin1, Volume 2019, Issue No 3486036, (HINDAWI-2019) “Mobile sink–based energy efficient cluster head selection strategy for wireless sensor networks”, Vinith Chauhan, Surender Soni (SPRINGER – 2019). Mohammed Nazeer 1 *, Garimella Rama Murthy “Energy Efficient, Data Centric Routing Algorithm in Mobile Wireless Sensor Nodes” International Journal of Computer Sciences and Engineering, Volume-6, Issue-10, Oct 2018. Ahmed Salim 1 , Asma Ahmed Badran 2 “Impact of using Mobile Sink on Hierarchical Routing Protocols for Wireless Sensor Networks” International Journal of Advanced Science and TechnologyVol.77(2015), pp.37–48 http://dx.doi.org/10.14257/ijast.2015.77.04 . Mohammed Nazeer, G.Rama Murthy, RPratap singh, “Leveling and Sectoring Algorithm: Lattice Point Problem (Quantification of Energy Savings)”. ACM IML conference, United Kingdom 2017, (2017). Mohd, N., Sharma, K., Salagrama, S., Agrawal, R., Patil, H. (2023). Life span improvement of bio sensors using unsupervised machine learning for wireless body area sensor network. Revue d'Intelligence Artificielle, Vol. 37, No. 1, pp. 7–14. https://doi.org/10.18280/ria.370102 Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWireless sensor network is projected to be most research oriented technology. In such communication networks, sensor collect, process and transmit/receive the sensed reading of physical variables such as temperature in many applications of WSNs manual battery replacement is impossible or totally not feasible. Hence researches were motivated to design energy efficient communication protocols. One such early protocol, namely (LEACH) is based on the fact that the sensors which are close by record/measure similar sensed reading [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Thus, it is enough for one leader node to collect process and transmit/receive the sensed reading technically the group of nodes is called a cluster and the leader node called \u0026ldquo;cluster head\u0026rdquo; [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With such motivating idea, various related protocols like PEGASUS, LEACH, and LEACH Energy Aware were proposed and implemented [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These protocols implicitly assumed (in the early versions) that the sensors in a cluster are one-hop from each other. In updated versions of these protocols, such an assumption is relaxed.\u003c/p\u003e \u003cp\u003eEarly on the authors realized that there are many real world WSNs, the sensor nodes are single hop or multiple hops from one and another, with such motivation, the Euclidean distance between sensors and cluster head was utilized to formulate and solve the cluster head placement problem. But, from practical considerations, it is clear that the wireless hops between sensor nodes and cluster head must be taken into account rather than the Euclidean distance. Also, the energy savings achieved over the entire network was never estimated. Furthermore the author proposed an energy efficient algorithm for routing, localization and fusion, called leveling and sectoring algorithm.\u003c/p\u003e \u003cp\u003eThis research paper combines clustering based on wireless hop-count and leveling sectoring over the entire sensor field. Since due to the increasing in the number of sensor nodes with in a cluster considering single hop communication consumes more energy as the distance between cluster member and cluster head increases, therefore instead of considering the distance for the selection of the cluster head in this proposed mechanism we will consider number of hops with in the cluster using hop count matrix. The proposed algorithm is evaluated mathematically(quantification) in [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this research article we are evaluating it by performing simulation using various constraints and it is shown through simulations that the energy saved in transmissions/receptions is significant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Literature Survey","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] It uses greedy algorithm to form the clusters and cluster head, dividing into sectors by using sensor nodes are homogenous. It considers both static and mobile nodes. The simulated is done by using MAT lab.\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Each node can reduce energy consumption, the Location Sensitive Routing Protocol (PRRP) is suggested the sensor node listening is reduced by idle listening. It is providing the CH selecting mechanism which increases network throughput on packet basis and it is a primary contribution of PRRP.\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] The network model for the execution of PRRP is an architecture where it considers a uniform randomly distributed sensor nodes where nodes are randomly distributed based on a grid format in a sensor region. The sink static is situated in middle of their network, and on both sides of the sink, nodes in the network is split into separate clusters of groups. Normally, depending on network size, the levels is specified in radius across the base station with tier D\u003csub\u003e0\u003c/sub\u003e, D\u003csub\u003e1\u003c/sub\u003e.. etc. Each tier covers more than one grid (partially or fully) and has a logical range in which, depending on the distribution of the nodes, the number of nodes lies. Compared to current routing protocols such as PRRP, CELRP and LEACH and its efficiency display better results with outstanding network lifetime improving and network throughput.\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] The rectangular regions is divided the network in the proposed work and each region consists of one cluster head (CH). The firefly optimization algorithm inspired by nature is used to choose cluster heads where the decisive parameters of the process are residual energy, average node to node distance and distance from the node to sink. After estimating the centroid position of the CHs, the sink moves into the observing area. Using the Mat lab simulation platform, the output of the implemented protocol is compared with the existing protocols.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] This paper illustrates and addresses the design challenges for cluster-based systems, the significant parameters of cluster creation, and the classification of protocols for hierarchical clustering. In addition, by considering those criteria, current cluster-based and grid-based techniques are evaluated to assist users in choosing suitable techniques. In addition, in specific situations, a comprehensive overview of these protocols is provided with their benefits, drawbacks, and applicability.\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] The leveling and sectoring algorithm is used to divide the rectangular region into level and sector by using signal strength or hop count based method and unidirectional antenna. It will apply leveling and sectoring constraints to stop the reverse propagation of the packet during the broadcast and collectcast. Exponential amount of energy is saved by reducing the number of packet transmission and reception; it is compared with various data centric algorithm such as gossip and direct diffusion.\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] In this paper its compared various hierarchical routing protocols such as PEGASIS,COSEN and IECBSN by consider the sink as mobile node and the mobility of the sink is considered under various scenarios such as circle, rectangle and random). Whenever the sink changes its position it will inform to its neighboring nodes by broadcasting message. The various protocols are compared by using performance metric such as average energy consumption and number of rounds. It has been observed EAPHRN perform with number of rounds and less average energy consumption.\u003c/p\u003e \u003cp\u003eThe above existing cluster head selection algorithm has been implemented by considering the communication between the cluster members and cluster head at single hop but if the distance between cluster members and cluster head increases with in the cluster then it consumes more energy so instead of going for single hop communication between cluster head and cluster members we will perform multi hop communication in the proposed algorithm.\u003c/p\u003e"},{"header":"3. Proposed system","content":"\u003cp\u003e \u003col style=\"list-style-type: lower-roman;\"\u003e\u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eClustering based upon HOP Count considering Leveling and sectoring\u003c/b\u003e:\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn traditional clustering algorithms such as LEACH and its variants it is implicitly assumed that the sensor nodes are one wireless hop from each other (at a certain power level utilized by the sensor nodes). Thus, in all such hierarchical routing algorithms, the physical distance or the wireless distance (i.e. number of hops at a between sensors fixed power level) is not considered to determine the cluster head in recent research works [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], the authors reasoned that taking the Euclidean distance or hop count between the sensor nodes in a cluster, definitely results in energy efficient clustering there by the life time of network is prolonged.\u003c/p\u003e \u003cp\u003eIn the proposed algorithm the network is divide into level and sectors by using L \u0026amp; S algorithm [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], once L \u0026amp; S is done successfully in every sector, the cluster formation will occur by using the setup and steady phases. During the setup phases the hop count matrix is used for the selection of the cluster head, in the hop count matrix process it count the number of hops needed to reach from one sensor node to another sensor node, the node with minimum hop count value to reach all the nodes in the cluster and having energy level above threshold value will be selected as the CH. Once CH selection is done its complete the setup phase in the steady phase the cluster members send the sensed value to the cluster head and cluster head will forward the value to the sink it completes the round in the next round again it performs setup phase for the selection of the cluster head based upon hop count matrix and remaining energy of the node.\u003c/p\u003e \u003cp\u003eThe quantification evaluation of the proposed leveling and sectoring algorithm and hop cunt matrix has been done in the previous research papers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eConsider 8*8 grid as shown in the figure above, each lattice point containing a sensor node. Divide it into levels[L\u003csub\u003e1\u003c/sub\u003e,L\u003csub\u003e2\u003c/sub\u003e,L\u003csub\u003e3\u003c/sub\u003e,\u0026hellip;..L\u003csub\u003e8\u003c/sub\u003e] and sectors [S\u003csub\u003e1\u003c/sub\u003e,S\u003csub\u003e2\u003c/sub\u003e,S\u003csub\u003e3\u003c/sub\u003e,\u0026hellip;..S\u003csub\u003e8\u003c/sub\u003e] as shown in figure.\u003c/p\u003e \u003cp\u003eBroadcast constraint: The packet has to be transmitted from lower level to higher level reverse transmission of the packet has to be dropped.\u003c/p\u003e \u003cp\u003eCollect cast constraint: The packet has to be transmitted from higher level to lower level reverse transmission of the packet has to be dropped.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe various steps performed in the setup phase are as shown below in the algorithm.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eStep-1: Divide the network area into level and sector by using L\u0026amp;S algorithm.\u003c/p\u003e\u003cp\u003eStep-2: Apply the L \u0026amp; S constraints for broadcast and collectcast\u003c/p\u003e\u003cp\u003eStep-3: Forms clusters with in the sector and the sensor nodes are multi sensor nodes from one and another.\u003c/p\u003e\u003cp\u003eStep-4: Perform row sum hop count matrix for each node in the cluster.\u003c/p\u003e\u003cp\u003eStep-5: Select the node with hop count minimum and maximum energy value as the CH.\u003c/p\u003e\u003cp\u003eStep-6: Now cluster head will forward their value to the sink.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eStep-7: This complete a round in the next round again it will perform setup and steady phases.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAlgorithm-1 of LS-Leach\u003c/span\u003e \u003c/p\u003e \u003cp\u003eStep-1: consider the case where sensor nodes are multiple wireless hops from each other\u003c/p\u003e \u003cp\u003eStep-2: let all the sensor nodes initially have same energy\u003c/p\u003e \u003cp\u003eStep-3: In wireless hops utilize the concept of center of graph to locate the optimal Location.\u003c/p\u003e \u003cp\u003eStep4: determining the center of associated graph utilizing the hop count matrix.\u003c/p\u003e \u003cp\u003eStep-5: Consider the hop count matrix and determine the row sums i.e. the sum of hop counts of all nodes from the jth wireless sensor node.\u003c/p\u003e \u003cp\u003eStep-6: Declare the cluster head as the wireless node for which the row sum of hop count matrix is minimum among all rows.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAlgorithm-2 HOP count matrix algorithm\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe above two algorithms are utilized for the formation of the clusters and selection of the cluster head. The cluster head selection is done by using algorithm-2. It is utilized were the communication between the cluster members and cluster head is at multiple hops.\u003c/p\u003e \u003cp\u003eLS-Leach in IoT: The proposed algorithm can be utilized in the IoT based application for optimal cluster head selection as the number of nodes with in the cluster will be at multiple hops. It will provide significant reduction of the energy utilization since cluster head is located at minimum hop count cluster members when compared to the other existing algorithm.\u003c/p\u003e \u003cp\u003e \u003col style=\"list-style-type: lower-roman;\" start=2\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSystem model and simulation environment\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe deployment of the existing and LS-Leach is shown in the figure below\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSimulation results\u003c/strong\u003e \u003cp\u003eThe OMNET\u0026thinsp;+\u0026thinsp;+\u0026thinsp;simulator is used for simulation by considering different parameters as shown in the table below. The simulation has been done by changing the coverage region of the sensor nodes as mentioned in the table such as 100,150,200 and 250 meters, the default energy and depletion in the energy formula is also mentioned in the table.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eParameters Of Simulation\u003c/span\u003e The dissemination of the nodes energy is done due to the following operation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csub\u003eE\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 nJ/bit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003csub\u003eE\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 pJ/bit/m 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003csub\u003eE\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 nJ/bit/message\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePacket Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000 bit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensor nodes (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInitial energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 Jouls\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoverage region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200,500,700 and 1000 m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eThe dissemination of the nodes energy is done due to the following operation\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy Dissemination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransmission energy of n bit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csub\u003eE\u003c/sub\u003e * n\u0026thinsp;+\u0026thinsp;A\u003csub\u003eE\u003c/sub\u003e * n * pow(d,2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReception energy of n bit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csub\u003eE\u003c/sub\u003e * n\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAggregating k messages of n bits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003csub\u003eE\u003c/sub\u003e * n*k\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eR\u003csub\u003eE\u003c/sub\u003e : energy reduction for radio operation A\u003csub\u003eE\u003c/sub\u003e : energy reduction for radio amplifier\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eC\u003csub\u003eE\u003c/sub\u003e : energy reduction for message aggregation\u003c/p\u003e \u003cp\u003eThe result of the simulation obtained by varying coverage region is mentioned in the table below.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIt is clearly observed that the death of the first node occurs after more number of rounds in the proposed system when compared to the existing, even the sensor node area of coverage is increased the number of rounds is more in the LS-Leach compare to the existing approach. The another performance metric used for evaluation is the end time and it is observed that simulation run for more time with more number of rounds in the proposed compared to existing, therefore this increase the network life time.\u003c/p\u003e\u003cp\u003eThe simulation of the network has been done for both homogeneous and heterogeneous network by considering energy. The result obtained is provided in the tables\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of different Leach Protocols for homogenous sensor nodes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdges\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeach-LS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEERMS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnd node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnd node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnd node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnd node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResult of different Leach Protocols for Heterogeneous sensor nodes (3.0\u0026ndash;6.0 Joules)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdges\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetrics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeach-LS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEERMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnd node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnd node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnd node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnd node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe graph has been plotted based upon result values mentioned in the table. It can be clearly observed that the proposed system increases the network life, number of rounds by utilizing energy in efficient manner compared to the existing system.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the proposed algorithm we apply leveling and sectoring to divide the network area into level and sector, and then in each sector consist of clusters. The cluster head is selected in each cluster is by considering hop count matrix which provide the node with minimum hop count value in the cluster. The proposed cluster head selection mechanism is compared with EERMS by considering different performance metric number of rounds and death of last, first node. We observed that the proposed mechanism provide death of last and first sensor node occurs after more time for both homogenous and heterogeneous network when compared with EERMS. The simulation has been carry out by varying the coverage region of the sensor nodes and same results has been observed that the dead of the first node and last node occurs after more time and more number of rounds. Since we are reducing the energy consumption in communicating with the cluster head by selecting optimized cluster head it increasing the life span of the sensor nodes and the network. LS-LEACH can be utilized in IoT based applications were we required the dying of the nodes should occur after more number of rounds or time such as smart health care system [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In future we wanted to utilized the proposed algorithm along with bio sensors for smart health care monitoring system using deep learning model on larger real time dataset to monitor health condition specifically mental health for stress detection using CCTV video, pulse rate, blood pressure and cortisol level of the patient remotely.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eR. Ramya, Dr. T. Brindha,A Comprehensive Review on Optimal Cluster Head Selection in WSN-IoT, Advances in Engineering Software, Volume\u0026nbsp;171,2022,103170, ISSN 0965\u0026ndash;9978, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.advengsoft.2022.103170\u003c/span\u003e\u003cspan address=\"10.1016/j.advengsoft.2022.103170\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTay, M., Senturk, A. A New Energy-Aware Cluster Head Selection Algorithm for Wireless Sensor Networks. Wireless Pers Commun 122, 2235\u0026ndash;2251 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11277-021-08990-3\u003c/span\u003e\u003cspan address=\"10.1007/s11277-021-08990-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJayaraman, G., Dhulipala, V.R.S. FEECS: Fuzzy-Based Energy-Efficient Cluster Head Selection Algorithm for Lifetime Enhancement of Wireless Sensor Networks. Arab J Sci Eng 47, 1631\u0026ndash;1641 (2022). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13369-021-06030-7\u003c/span\u003e\u003cspan address=\"10.1007/s13369-021-06030-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin Wang 1,2,3, Yu Gao 2, Wei Liu 2, Arun Kumar Sangaiah 4 and Hye-Jin Kim 5,* \u0026ldquo;Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks\u0026rdquo; Sensors 2019, 19, 1494; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/s19071494\u003c/span\u003e\u003cspan address=\"10.3390/s19071494\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003c/span\u003e\u003cspan address=\"http://www.mdpi.com/journal/sensors,PP1-19\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, March 2019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarimella Rama Murthy 1 Mohammed Nazeer 2, Padmalaya Nayak 3, \u0026ldquo;Energy Efficient Design Of Mobile Wireless Sensor Networks: Constrained Clustering\u0026rdquo;. International Journal of Innovative Technology and Exploring Engineering (IJITEE), Scopus, May2019\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammed Nazeer 1 *, Garimella Rama Murthy 2, Aishwarya Jain3 \u0026ldquo;Energy Efficient Clustering In WSN Using Weighted Centriod)\u0026rdquo; International Conference on Soft Computing and Signal Processing Aug:21\u0026ndash;22 Springer, goggle scholar, Scopus, dblp.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarimella Rama Murthy 1, Mohammed Nazeer 2, Dr. Tata Jagannadha Swamy \u0026ldquo;Energy Efficient Clustering in Real World Wireless Sensor Networks Implementation\u0026rdquo; International Conference on Soft Computing and Signal Processing Aug:21\u0026ndash;22 Springer, google scholar, Scopus, dblp.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026ldquo;Energy Efficient Hierarchical Clustering Approaches in Wireless Sensor Networks: A Survey\u0026rdquo;,Volume 17-Bilal Ja,Haleem Farman,2 HumaJaved ,2 BartolomeoMontrucchioMurad Khan and Shaukat Ali(HINDAWI-2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026ldquo;Retracted: Enhancing energy efficiency of WSN through the Design of Energy efficient routing protocol\u0026rdquo; Noor Zaman,1 Low Tang Jung,2 and Muhammad Mehboob Yasin1, Volume\u0026nbsp;2019, Issue No 3486036, (HINDAWI-2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026ldquo;Mobile sink\u0026ndash;based energy efficient cluster head selection strategy for wireless sensor networks\u0026rdquo;, Vinith Chauhan, Surender Soni (SPRINGER \u0026ndash; 2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammed Nazeer 1 *, Garimella Rama Murthy \u0026ldquo;Energy Efficient, Data Centric Routing Algorithm in Mobile Wireless Sensor Nodes\u0026rdquo; International Journal of Computer Sciences and Engineering, Volume-6, Issue-10, Oct 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed Salim\u003csup\u003e1\u003c/sup\u003e, Asma Ahmed Badran\u003csup\u003e2\u003c/sup\u003e \u0026ldquo;Impact of using Mobile Sink on Hierarchical Routing Protocols for Wireless Sensor Networks\u0026rdquo; International Journal of Advanced Science and TechnologyVol.77(2015), pp.37\u0026ndash;48 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.14257/ijast.2015.77.04\u003c/span\u003e\u003cspan address=\"10.14257/ijast.2015.77.04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammed Nazeer, G.Rama Murthy, RPratap singh, \u0026ldquo;Leveling and Sectoring Algorithm: Lattice Point Problem (Quantification of Energy Savings)\u0026rdquo;. ACM IML conference, United Kingdom 2017, (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohd, N., Sharma, K., Salagrama, S., Agrawal, R., Patil, H. (2023). Life span improvement of bio sensors using unsupervised machine learning for wireless body area sensor network. Revue d'Intelligence Artificielle, Vol.\u0026nbsp;37, No. 1, pp.\u0026nbsp;7\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18280/ria.370102\u003c/span\u003e\u003cspan address=\"10.18280/ria.370102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"Clustering, cluster head selection, network life time, fusion, localization","lastPublishedDoi":"10.21203/rs.3.rs-3183581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3183581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWSN are mostly deployed in sensitive location where battery replacement is impossible, which lead to implementation of an energy efficient protocol to extend network life time. In unsupervised machine learning, clustering based hierarchical routing protocols the dissemination of energy occurs mostly due to local communication and cluster head selection, roles of the nodes are changed as cluster member or cluster head to solve fusion and localization problem and manage energy dissemination and increasing the network life time. Nodes can communicate with each other directly or indirectly manner. In this paper the proposed LS-LEACH algorithm solved the problem of optimized cluster head selection by using leveling and sectoring protocol. In LS-LEACH the deployment area is divided into levels and sectors, and selection of the cluster head is done by using hop count matrix. LS-LEACH has been evaluated by performing simulation in OMNET\u0026thinsp;+\u0026thinsp;+\u0026thinsp;simulator and equated with existing EERMS algorithm by considering different metric such as last node death, first node death and network life time. The proposed algorithm can be utilized in various smart IoT based application for optimal cluster head selection.\u003c/p\u003e","manuscriptTitle":"HOP Count Based Energy Efficient Cluster Head Selection Using Leveling and Sectoring","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-26 13:54:43","doi":"10.21203/rs.3.rs-3183581/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":"f33ae757-0426-4a46-ad85-e749e1f04142","owner":[],"postedDate":"July 26th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-21T22:59:14+00:00","versionOfRecord":[],"versionCreatedAt":"2023-07-26 13:54:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3183581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3183581","identity":"rs-3183581","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}