Energy-Efficient Intelligent Ant Colony Optimization for Route Detection in Healthcare Using Wireless Body Sensor Networks

Energy-Efficient Intelligent Ant Colony Optimization for Route Detection in Healthcare Using Wireless Body 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 Energy-Efficient Intelligent Ant Colony Optimization for Route Detection in Healthcare Using Wireless Body Sensor Networks Ashima ., Amit Kishor This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7532533/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 Delay in the transmission of patient data is a major issue in healthcare for both patients and end-users with the use of static sink nodes in Wireless Body Sensor Networks (WBSNs). Reduction in the network lifetime, early death of sensor nodes, and hot spot problems are the main issues in healthcare data transmission. A body sensor node (BSN) collects the real time data therefore agile approaches must be adopted. Data collecting efficiency can be increased with mobile sinks. In WBSN, an artificial intelligence-based approach for route selection in healthcare plays a significant role in delay sensitive scenarios. An Intelligent Ant Colony Optimization based route detection (IACO-RD) is proposed to overcome the aforementioned issues. The performance is verified through the simulation and the result is compared with previous works. The simulation result shows the proposed algorithm performance that considerably enhances the lifetime of network, lifespan of sensor nodes, and reduces the data loss. Body Sensor Node Intelligent System Mobile Sink Healthcare Data Transmission Wireless Body Sensor Networks Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION In recent scenarios, a sensor is very important and plays a significant role in healthcare 4.0. Generally, sensors are used for real-time environment data transmission [ 1 ]. In healthcare 4.0, sensors are deployed on the body and surrounding of the patient to receive the patient’s information in real-time. Sensors are small and they are wearable and implanted into the body of the patient. In general, sensors are not very much expensive and they have very low power consumption. Sensors are responsible for storing, processing, and transmission the patient's health data to the base station. Implanted, wearable, and surrounding deployed sensors to the patient form a network called wireless body sensor network (WBSN) [ 2 ]. Sensors collect the patient information in analog form and convert it to digital form using any converter. All the collected information from the sensor reaches the base station through a several hops. Generally, there is a limited amount of battery resources to operate these sensors, and increase the power of battery resources is very difficult when it is used in any operation of work. Sometimes, when sensors are implanted in the patient's body then it is implanted with some minor or major surgical operation. So either change or recharging of battery resource at run time (when it is busy in some work) of work is a very tedious and time-consuming process. Thus limited energy of the battery is a major issue in WBSN [ 3 ].So the proper use of energy is a major concern in sensor nodes is very important in healthcare. The information flows from sensor nodes to mobile sinks using different hops and they need to maintain their energy for more life of sensors. Thus the energy of sensors nodes can be used for longer by selecting the best path for data transmission with the shortest distance between sensors and mobile sinks. It can also increase the network lifetime. Sensor nodes located next to the sink is receiving data from it and sensors are also used as forwarding node for nodes located next to it. Thus, in the network the sensor nodes will quickly lose energy and die. This makes it very difficult to transfer data from different nodes to the base station and guides to network separation issues, degraded quality of service, and shortened network life [ 4 – 5 ]. Some of the sensor nodes that are near to movable sinks lose energy at an early stage in comparison to other sensor nodes which are far from mobile sinks. This is happening due to the extra usage of these nodes in data transmission. These particular nodes become dead faster than other distant located sensor nodes in the network because their availability was near to sink [ 6 ]. The main issues to be addressed are multi-hop paths and the congestion of data travel to the sink. This issue is known as the hotspot problem. In recent years, movement in sink has been used to increase the life of wireless sensor networks (WSNs). Unlike wireless static receiver sensor networks, mobile sink based approaches can reduce hotspot problems and balance power consumption between sensor nodes. The mobile sink moves across the reading field to gather information over a comparatively small communication range. Significant energy savings have been reported that will extend the life of the network. Finding the optimal route for a movable sink to visit specific encounter points is an important topic, and the previous work shows that the ant colony optimization (ACO) algorithm is proven to be a fruitful resolution for calculating a movable sink route. ACO algorithm is used to solve computational problems based on a probabilistic approach. The algorithm is inspired by the real ant behavior when ants are searching for food based on artificial intelligence approach with swarm intelligence technique. At beginning, ant starts moving randomly to search food. In their movement, the ants deposited a number of pheromones on the way. When the ants reach the food center, several paths become available and these paths have a different intensity of pheromone. After that remaining ants follow the path having higher intensity. In the end, all ants start moving with the same path, and this path is considered the best path for food searching. This process of food searching by ants produced the ACO algorithm. The ACO algorithm is used to discover the optimal path among different available paths [ 7 – 8 ]. This is known as the traditional ACO algorithm. There are some issues like the slow rate of convergence and preferred local optima rather than global optima. These drawbacks have a negative impact on the latency and convergence of solutions on networks. A tiny delay in healthcare can be a reason for the death of a patient. To resolve the aforementioned issues, we proposed an Intelligent ACO-Route Detection (IACO-RD) algorithm in healthcare using WBSN with a mobile sink. In the proposed work, three kinds of sensors have been attached to the patient to collect the health data. First is an implanted sensor on the body to collect the internal information of the patient. Second, wearable sensors are attached to the skin of the patient to collect patient-external information and third are an environmental sensor to collect the environmental information of the surrounding of the patient. These sensors form a network known as WBSN. The sensor nodes are considered as BSN. Each BSN is attached with a BSN Coordinator (BSNC) to send the data. Then mobile sink (Ms) is used to discover a best path using the proposed IACO-RD algorithm to collect patient data from BSNC. In the proposed work, global search ability increased with the consideration of a new heuristic distance factor. This also improves the rate of convergence. The major contribution of the proposed work is as Presenting the IACO-RD algorithm to find the shortest path between the mobile sink and BSNC. Enhance the lifetime of the network and reduction in packet loss using the IACO-RD algorithm. Improving the data collection by the introduction of the mobile sink. Reducing the consumption of energy to enhance the lifetime of sensor nodes. Comparing the performance of proposed work with similar existing prior works. The remaining paper is structured as follows. Second section representing the prior work and third section is presenting the proposed work. Fourth section is used for result and discussion and finally fifth section is concluding the work. 2. RELATED WORK The low-energy adaptive clustering-hierarchy (LEACH) algorithm [ 9 – 10 ] is a traditional clustering process for WSNs and used to divide the clusters into various sub-regions. Then the optimal path between Ms and cluster head (CH) is obtained by applying a routing algorithm. When there are several Ms are available then CHs are grouped together to make subsets and it is assigned to one Ms. Ms traverse all the CHs as assigned in a subset and return to the initial location. Mottaghi and Zahabi [ 11 ] presented an improved LEACH algorithm with Ms and rendezvous nodes. They used to exchange the process of selection of CH and also preserve the advantages of the LEACH algorithm. In the case of large networks, the given algorithm reduces the consumption of energy in comparison to the traditional LEACH algorithm. Wang et al. [ 12 ] presented an energy-efficient dynamic routing approach for managing the movable sinks. They manage the reconstruction of routes by setting some communication rules. They resolved the hotspot problem by rotation of a cluster head (CH). The issues in the collection of data through mobile sinks are highlighted by Deng et al. [ 13 ]. To resolve the issue, they designed a primal-dual online approach with less prior knowledge. Zhang et al [ 14 ] highlighted the issues created in maximization of network utilization when mobile sinks collected data in the network and also maintaining the objectivity of the network. They presented a distributed-data collection method to resolve the aforementioned issue. Sharma et al. [ 15 ] presented a rendezvous based routing protocol (RRP). They highlighted the issues of energy and delay requirements. The data transmission is done with two different modes. The proposed protocol has better results in the consumption of energy, delay, and a lifetime of a network in comparison with prior work. Wang et al. [ 16 ] presented an algorithm based on an energy-aware approach for the relocation of the sink. They used a maximized capacity path protocol for designing an energy-aware algorithm. They have shown the movement of mobile sinks whenever it meets relocation conditions and the new place will be given with the highest weight value. The algorithm has taken a very elongated time to get the condition of relocation and also it isn’t able to increase the lifespan of the network. Fadel et al. [ 17 ] highlighted the use of sensors and the sensor technologies are used in different sectors such as smart healthcare, monitoring of the environment, security, and military. Li et al. [ 18 ] presented the clustering approach to increasing the lifetime of the network in WSNs. They presented the process of clustering that use to divide the entire regions into smaller sub-regions. The partitioned sub-regions are called clusters. A cluster is a set of sensors called cluster members. The CH is one of them having higher energy. A CH is liable for assembling the data from different cluster members and these members are receiving the data from sensors. The CHs are being changed during the completion of operations. Hence it can improve the lifetime of the sensors [ 19 ]. Khalily-Dermany et al. [ 20 ] introduced the mobile sink in the network to get a reduction in hop counting traveled by data packets. They highlighted the demerits of the static sink and the merits of the mobile sink. The mobile sinks are used to gather the data for CHs in a single hop and after collection of all data; mobile sinks sent the data to the base station for further execution. Gupta and Jha [ 21 ] highlighted the path for the collection of data by mobile sinks from CHs having low energy consumptions. They simulated the insect's behavior for searching for the optimal path. Kaur and Grewal [ 22 ] presented particle swarm optimization (PSO) for design the optimal path for mobile sink with effective energy. The author presented the Ant Colony Optimization algorithm to find a solution for food searching. The algorithm is motivated by the performance of natural ants. They proposed linear programming for routing and also particle encoding scheme. They find the local and global best solution by using Pareto dominance. Boyineni et al. [ 23 ] proposed a protocol for energy-effective data reporting. They proposed a novel logical coordinate system for traversing the path and forwarding the packets of data. By the use of SinkTrail and sojourn location of the movable sink sensor nodes for further selecting the next hop. The shortest distance path is established between sensor modes and mobile sinks. The used algorithm has reduced the control overheads. Wang et al. [ 24 ] proposed an Improved ACO with mobile sinks (IACO-MS) for WSNs to find the optimal route using CHs. They divided the network into clusters by assigning a CH among them. They find the optimal distance between the mobile sink and CH, then the mobile sink traverse on the route and collect the data from CHs. The author highlighted the global search ability of the IACO-MS. Liu et al [ 25 ] presented a design of protocol transmission with multiple load-balancing schemes (TMLBS) for information delivery. The key contribution of the protocol is to maximize the lifetime of WSNs. They establish the trajectory path through the use of the ACO algorithm. In recent research, it is reported that moving mobile elements can significantly increase the performance of WSNs [ 26 ]. Vehicles or robots can be used as mobile elements [ 27 ]. These mobile elements behave like as edge of nodes [ 28 ]. 3. PROPOSED WORK 3.1 Network Model In a region, Implanted and wearable both types of sensors are randomly deployed. Sensors are consistent and static in character. All sensors having equal energy and during the complete operation the batteries of sensors can’t be replaced and recharged. The data transmission is performed at different transmission rate. Multi-hop meshes topologies are used to inter connect the sensors. It is considered that there is a number of BSN. The BSN is generating the patient data and BSNC is working as a forwarding node to send the data to one of the nearest mobile sinks. The mobile sink is collecting the data by BSNC and forwarding the data on a priority basis to the fog layer for further processing. On priority basis indicates that they are forwarding the critical data before the non-critical data. Here base station is used to maintain all the details of the route to the mobile sink. Figure 1 represents the scenario of the proposed network model. In this network scenario, it is assumed that there is a connected undirected graph CUG =(S, L, Ms), where S as a set of sensor nodes and sensor coordinators, here sensor nodes are collection of BSN (s 1 , s 2, …..s n ) and sensor coordinators are collection of BSNC (c 1 c 2 ,…..c n ), L as set of link (l 1 , l 2 , …l n ) edges between S and Ms, and Ms is the set of mobile sink (Ms 1 , Ms 2 ,…Ms n ). The main aim is minimize the distance S to Ms so that S can send the data to Ms with minimum distance and time. In Fig. 1 the white circles are representing the BSNs, gray circle are representing the BSNCs, black circle are representing the mobile sinks. 3.2 Energy Model In this proposed work we considered basic radio model for energy measurement. The energy required in the data transmission (sending and receiving) is computed with basic radio model [ 5 ]. The consumption of energy is divided as required for transmitted part and required for receiver part. The energy required to transmit data from a sensor node to a particular node having y bits of data can be calculated as $$\:{E}_{Tx}\left(y,d\right)=\:{E}_{el}*y+\left\{\left({Є}_{Fs}\right)\:or\left({Є}_{mpf}\right)\right\}*y*{d}^{n}$$ 1 Where \(\:{E}_{el}\) is used as energy for radio model, \(\:{Є}_{Fs}\) is consumption of energy for free space system, \(\:{Є}_{mpf}\) is consumption of energy for multi-path fading system, and d n is d 2 and d 4 power losses for free space system and multi-path fading system. The energy required for receiving the y bits data by a sensor node is calculated as $$\:{E}_{Rx}=\:{E}_{el}*y$$ 2 3.3 Strategy for BSN formation For the formation of BSN, it is distributed over entire network. Here, each BSN has a member of one BSNC. Therefore the BSN can send their patient data to the BSNC. 3.4 Strategy for selecting BSN coordinator BSNC is selected on the basis of two factors. First factor is residual energy of the BSN and another factor is distance from neighbor BSNs. Preferably the centre located nodes becomes BSN coordinator (BSNC). BSNC is located at the center location. BSN those who are near to BSNC can send their patient data with minimum distance and time. The BSNC is not fixed for complete operation. The BSNC will be changed if their residual energy becomes lower than pre-decided threshold value and another node same from group become new BSNC if they have higher residual energy and also satisfy the distance condition. 3.5 Procedure for route formation The patient’s data is collected by BSN and these nodes are forwarding the data to the BSNC then after these data is collected by mobile sink. The aim of the propose work is to select the optimal and adaptive route for transmission of data from BSNC to Ms with no loss of data by using artificial intelligence technique in minimum distance and time. Here, Ms collects the data in similar fashion as a travelling salesman problem. The nature of the problem is NP-hard and by the use of heuristic function an optimal solution is achieved. In the proposed work, the mobile sink is treated as salesman and BSNC is treated as different location that needs to be visited by salesman. Therefore Ms collects the data by visiting all the BSNC by implementing intelligent ACO algorithm. Here each Ms is assigned with an average number of BSNC and it can be obtained by A distance matrix between Ms and BSNC is designed for computation purpose. Where, Ms is represented by row of the matrix and BSNC is represented by column of the matrix. 3.6 Intelligent ACO-RD implementation In traditional ACO algorithm, it consist a team of ants and known as ant system. In traditional ACO algorithm, ants work in as a team to optimally complete a difficult task to search food for survival. The team of ants can provide solution for any NP hard problem ant it is based on metaheuristic approach. The metaheuristic approach is known to be reliable and adaptable to solve a variety of combinatorial optimization issues. In traditional ACO-based algorithms, artificial ants are fashioned to imitator the doings of real ants in an organization to see the best track. After the position use, a piece ant moves from one BSNC to a new BSNC and deposits its pheromone. Decision paths are created based on their path and pheromones are saved and renewed. When the process is complete, the solution paths are evaluated and select the best path where contains the most pheromone. $$\:{{P}^{k}}_{ij}\left(t\right)\:=\:\left\{\begin{array}{c}\frac{{\left({\tau\:}_{ik}\left(t\right)\right)}^{\alpha\:}{\left({\eta\:}_{ik}\left(t\right)\right)}^{\beta\:}}{\sum\:_{kЄ{permitted}_{k}}{\left({\tau\:}_{ik}\left(t\right)\right)}^{\alpha\:}{\left({\eta\:}_{ik}\left(t\right)\right)}^{\beta\:}}\:\:\:\:\:\:\:\:if\:jЄ\:{permitted}_{k}\:\\\:0\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:otherwise\end{array}\right.\:,$$ 5 The basic ACO algorithms, to create a viable solution, the ants use the probabilistic rule to select the next city to visit. Eq. 1 representing the probability of path movement from BSNC (i) to BSNC(j), where τ ij (t) is information about pheromone in the used path from BSNC(i) to BSNC(j) and η ij = 1/d ij , where d ij is the distance from BSNC (i) to BSNC (j), permitted k signify the BSNC that permitted to visit by ants, α and β are used as a constant, and its values regulate the action of pheromones. However there are some issues with tradition ACO like slow convergence rate and easily adopting the solution which is locally optimal not globally. Now we proposed the novel intelligent ACO and enhancing the capability to adopt solution which is globally optimal by modifying the distance factor. The minimum distance is considered by ant from node i to target node n through node j as hop. η ij = \(\:\frac{1}{\text{min}\left[dis\left(i,n\right),\:dis\left[(i,j\right)+dis(j,n)\right]}\) (6) Where η ij is the heuristic value, \(\:dis\left(i,n\right)\) , \(\:dis(i,j)\) , and \(\:dis(j,n)\) are the distances from BSNC(i) to BSNC(n), BSNC(i) to BSNC(j), and BSNC(j) to BSNC(n). After substituting the Eq. 6 into Eq. 5 . $$\:{{P}^{k}}_{ij}\left(t\right)\:=\:\left\{\begin{array}{c}\frac{{\left({\tau\:}_{ik}\left(t\right)\right)}^{\alpha\:}{\left(\:\frac{1}{\text{min}\left[dis\left(i,n\right),\:dis\left[(i,j\right)+dis(j,n)\right]]}\right)}^{\beta\:}}{\sum\:_{kЄ{permitted}_{k}}{\left({\tau\:}_{ik}\left(t\right)\right)}^{\alpha\:}{\left(\:\frac{1}{\text{min}\left[dis\left(i,n\right),\:dis\left[(i,j\right)+dis(j,n)]\right]}\right)}^{\beta\:}}\:,\:\:\:\:\:if\:jЄ{permitted}_{k}\\\:\:\:\:\:\:\:\:\:\:0\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:otherwise\:\end{array}\right.\:\:$$ 7 For best results, the ant pheromone trace values are updated with each iterations. This makes it possible to show the performance of the ants and to evaluate the worth of the solution. The process of updating is based on unsupervised learning approach of ACO which helps to get better subsequent decisions. The pheromone updating execution includes localized and globalized updates. The amount of pheromone evaporate with respect to time and after some time traces are updated as $$\:{\tau\:}_{ij}\left(t+m\right)=\:\left(1-\:{\rho\:}\right){\tau\:}_{ij}\left(t\right)+\:\varDelta\:{\tau\:}_{ij}$$ 8 Where ρ is a evaporation coefficient between time t and t + m, 0 ≤ ρ ≤ 1. \(\:\varDelta\:{\tau\:}_{ij}\) is current iteration value of pheromone deposited in time between t and t + m, on the edges made by (i, j) by ant k. The \(\:\varDelta\:{\tau\:}_{ij}\) can be obtained by $$\:\varDelta\:{\tau\:}_{ij}=\:\sum\:_{K=1}^{N}\varDelta\:{\tau\:}_{ij}^{k}$$ 9 Where total quantity of ants represented by n. The quantity of pheromone left by each ant, when it travels from node i to node j is define as $$\:\varDelta\:{\tau\:}_{ij\:}^{k}=\:\left\{\:\:\:\begin{array}{c}\frac{Q}{{L}_{ij}}\:\:\:\:\:edge\:\left(i,j\right)\:Є\:best\:route\:\\\:0\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:otherwise\end{array}\right.$$ 10 Where Q represents a constant parameter and L ij is a distance parameter from BSNC i to BSNC j . The updated approach will work in number of iteration until unless it gets the optimal path for travelling of mobile sinks among BSNC. Algorithm1 represents an algorithm to find optimal and best route using IACO-RD from mobile sink to BSNC. Algorithm1 Intelligent ACO-Route Discovery Calculate Average for BSNC assignment to Ms Construct a distance matrix as per Eq. 4. Apply ACO for route formation between Ms and BSNC Assign BSNC to nearest Ms For i = 1 to Avg(Ms) For j = 1 to Ms Assign Ms[j] := BSNC[i] End for loop If (Avg(Ms) * Ms ≤ n(BSNC)) Randomly assign remaining BSNC[i] to Ms[j] End if Start data collection at Ms Repeat process till last BSNC. 4. RESULT AND DISCUSSION The proposed IACO algorithm is used to increase the lifetime of the network and collects the health data from BSNC through mobile sinks in WBSN environment to increase the efficiency of routing. Mobile sinks are used to collect data from sensor nodes by establishing the shortest path between BSNC and itself using proposed IACO algorithm. The performance of proposed algorithm is evaluated through simulations. The proposed algorithm is compared with ACO-M [ 23 ], PSO [ 22 ], and IACO-MS [ 24 ]. Matlab R2018b is carried out the simulation iterations. Simulations of proposed algorithms are performed with parameter listed in Table 1 . The sensor nodes and mobile sinks are deployed with an area 300 x 300 m 2 and network size (100–500 nodes). Table 1 Simulation parameters Parameters Values Target Size [ 2 , 2 ]m 2 Number of Sensor nodes [10–50] Sensor type Static Sensor nodes initial energy 0.5 J Communication radius 10 m Simulation rounds 2000–5000 Packet length 500 bytes E Tx 3.405 µJ/bit E Rx 1.64 µJ/bi E el 50 nJ/bit Є fs 10 pJ/bit/m2 Є mpf 0.0013 pJ/bit/m4 Number of Ms [ 1 , 3 ] The performance of network is evaluated with a metric called lifetime and Figs. 2 and 3 shows the measured network lifetime comparison at first and last node death. Energy consumption can be reduced in the data forwarding from BSNC to mobile sink using mobile sink nodes. The proposed work is compared with aforementioned work and it can be seen in Fig. 2 and Fig. 3 , the network life times of presented work are higher than the existing work. In proposed work BSNC is selected among BSNs having higher residual energy. The rotation of BSNC is performed when the existing BSNC have less residual energy as compared to threshold energy. The average energy consumption is shown by Fig. 4 . It has been clearly observed that the proposed algorithm has lower consumption of energy as compared to existing works and thus proposed algorithm performance in terms of consumption of energy is good. Figure 5 shows that average loss of packets in different network sizes. It is clearly observed that in Fig. 4 that the proposed algorithm has the minimum packet loss as compared to existing work. Figure 6 presenting the average loss of packets with different numbers if mobile sinks. It can be seen in Fig. 6 when number of mobile sink increases the packet losses are decreases. The rate of decrease in packet loss is lower in proposed IACO-RD algorithm as compared to existing work. Figure 7 presenting the data collected by mobile sink with different number of rounds. It is clearly seen in Fig. 7 that the presented algorithm collecting more data in comparison to other existing works. Figure 8 shows the comparative view of all existing work and the proposed algorithm with respect to alive sensor node after number of rounds. It is clearly notice that in Fig. 8 number of alive nodes decrease as the number of rounds increases and the proposed algorithm results has better than as compared to the other existing work. 5. CONCLUSION We proposed a novel IACO-RD algorithm to increases the efficiency of data collection, lifetime of network, lifespan of BSN and reduction in data loss of WBSN in healthcare. Highly energetic BSN is elected as a BSNC of the BSNs. Mobile sinks are used to collect data through BSNC which covers minimum distance to get data. The minimum distance coverage increases the lifespan of BSN and improves the lifetime of network with the use of ACO based routing scheme. The simulation results verified the proposed route detection scheme that reduces the travelled distances as compared to the prior existing works and collects the data from BSNC with minimum losses of data. In future, bio-inspired algorithm can be implemented through the mobile sinks to increases the effectiveness and to reduce the delay in healthcare. Declarations Conflicts of interest/Competing interests – There are no conflicts of interests. Funding details: There no funding details. Author Contribution Amit Kishor wrote the main manuscript text and Ashima prepared all figures used in the manuscript. Acknowledgment The authors would like to thanks to Department of Computer Science & Engineering, Subharti Institute of Engineering and Technology, Swami Vivekanand Subharti University, Meerut, India to give this platform to work. References J. Roselin, P. Latha, and S. 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Ren, "Delay-aware grid-based geographic routing in urban VANETs: A backbone approach," IEEE/ACM Transactions on Networking, vol. 27, no. 6, pp. 2324-2337, 2019. S.N. Shirazi, A. Gouglidis, A. Farshad, and D. Hutchison, "The extended cloud: Review and analysis of mobile edge computing and fog from a security and resilience perspective," IEEE Journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2586-2595, 2017. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7532533","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524980189,"identity":"6fb6bf58-5ad9-425f-802b-dc66c065ec49","order_by":0,"name":"Ashima .","email":"","orcid":"","institution":"Swami Vivekananad Subharti University, Meerut","correspondingAuthor":false,"prefix":"","firstName":"Ashima","middleName":"","lastName":".","suffix":""},{"id":524980194,"identity":"963074fe-e258-41ff-a7fb-d93c50ec25b6","order_by":1,"name":"Amit 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1","display":"","copyAsset":false,"role":"figure","size":74773,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork model\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7532533/v1/3e816c7057ff47279535454b.jpeg"},{"id":93022364,"identity":"a32c24de-5bb4-4ca9-813a-37f9688ec066","added_by":"auto","created_at":"2025-10-08 09:02:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32105,"visible":true,"origin":"","legend":"\u003cp\u003eFirst node death\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7532533/v1/10acd00421a21e05e59ce517.png"},{"id":93022366,"identity":"624f84aa-bda2-45e3-8da1-9ed0e8e0c98e","added_by":"auto","created_at":"2025-10-08 09:02:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37553,"visible":true,"origin":"","legend":"\u003cp\u003eLast node death\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7532533/v1/4d0e31c54288ae6f84ca6b33.png"},{"id":93022367,"identity":"3a06b28d-c1df-436a-96c2-6172d7304fd5","added_by":"auto","created_at":"2025-10-08 09:02:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32566,"visible":true,"origin":"","legend":"\u003cp\u003eAverage consumption of energy (in J)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7532533/v1/2ce2bcb092357ac56f128e87.png"},{"id":93023702,"identity":"8f93f390-4e51-40c6-a63b-5ecd6e750cf2","added_by":"auto","created_at":"2025-10-08 09:10:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":18198,"visible":true,"origin":"","legend":"\u003cp\u003eAverage loss of packets (%) w.r.t. number of rounds\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7532533/v1/cec78d80fc2d0d7cb6513c60.png"},{"id":93022369,"identity":"882d5c13-0a86-452e-bef2-7d505a05f464","added_by":"auto","created_at":"2025-10-08 09:02:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":25465,"visible":true,"origin":"","legend":"\u003cp\u003eAverage loss of packets (%) w.r.t. mobile sink\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7532533/v1/002fa854579f041fd80d2253.png"},{"id":93023705,"identity":"d4fe70e2-6f98-4c25-b17f-82eff59e4004","added_by":"auto","created_at":"2025-10-08 09:10:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":18198,"visible":true,"origin":"","legend":"\u003cp\u003eAverage loss of packets (%) w.r.t. number of rounds\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7532533/v1/fb9eb0b1f931b1645ac42d17.png"},{"id":93022376,"identity":"45a53674-e9f6-46f9-a21a-387b9f5c7b15","added_by":"auto","created_at":"2025-10-08 09:02:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":19466,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of alive nodes\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7532533/v1/3ac394ced1dc3f7f03f5270e.png"},{"id":98607868,"identity":"5467b4da-faba-4f3f-8844-0985bd9d4dba","added_by":"auto","created_at":"2025-12-19 13:54:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":742507,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7532533/v1/8aa5b67a-d734-4b5a-a4be-3deaa8a9b39e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Energy-Efficient Intelligent Ant Colony Optimization for Route Detection in Healthcare Using Wireless Body Sensor Networks","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn recent scenarios, a sensor is very important and plays a significant role in healthcare 4.0. Generally, sensors are used for real-time environment data transmission [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In healthcare 4.0, sensors are deployed on the body and surrounding of the patient to receive the patient\u0026rsquo;s information in real-time. Sensors are small and they are wearable and implanted into the body of the patient. In general, sensors are not very much expensive and they have very low power consumption. Sensors are responsible for storing, processing, and transmission the patient's health data to the base station. Implanted, wearable, and surrounding deployed sensors to the patient form a network called wireless body sensor network (WBSN) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Sensors collect the patient information in analog form and convert it to digital form using any converter. All the collected information from the sensor reaches the base station through a several hops. Generally, there is a limited amount of battery resources to operate these sensors, and increase the power of battery resources is very difficult when it is used in any operation of work. Sometimes, when sensors are implanted in the patient's body then it is implanted with some minor or major surgical operation. So either change or recharging of battery resource at run time (when it is busy in some work) of work is a very tedious and time-consuming process. Thus limited energy of the battery is a major issue in WBSN [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].So the proper use of energy is a major concern in sensor nodes is very important in healthcare.\u003c/p\u003e\u003cp\u003eThe information flows from sensor nodes to mobile sinks using different hops and they need to maintain their energy for more life of sensors. Thus the energy of sensors nodes can be used for longer by selecting the best path for data transmission with the shortest distance between sensors and mobile sinks. It can also increase the network lifetime.\u003c/p\u003e\u003cp\u003eSensor nodes located next to the sink is receiving data from it and sensors are also used as forwarding node for nodes located next to it. Thus, in the network the sensor nodes will quickly lose energy and die. This makes it very difficult to transfer data from different nodes to the base station and guides to network separation issues, degraded quality of service, and shortened network life [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSome of the sensor nodes that are near to movable sinks lose energy at an early stage in comparison to other sensor nodes which are far from mobile sinks. This is happening due to the extra usage of these nodes in data transmission. These particular nodes become dead faster than other distant located sensor nodes in the network because their availability was near to sink [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The main issues to be addressed are multi-hop paths and the congestion of data travel to the sink. This issue is known as the hotspot problem.\u003c/p\u003e\u003cp\u003eIn recent years, movement in sink has been used to increase the life of wireless sensor networks (WSNs). Unlike wireless static receiver sensor networks, mobile sink based approaches can reduce hotspot problems and balance power consumption between sensor nodes. The mobile sink moves across the reading field to gather information over a comparatively small communication range. Significant energy savings have been reported that will extend the life of the network. Finding the optimal route for a movable sink to visit specific encounter points is an important topic, and the previous work shows that the ant colony optimization (ACO) algorithm is proven to be a fruitful resolution for calculating a movable sink route.\u003c/p\u003e\u003cp\u003eACO algorithm is used to solve computational problems based on a probabilistic approach. The algorithm is inspired by the real ant behavior when ants are searching for food based on artificial intelligence approach with swarm intelligence technique. At beginning, ant starts moving randomly to search food. In their movement, the ants deposited a number of pheromones on the way. When the ants reach the food center, several paths become available and these paths have a different intensity of pheromone. After that remaining ants follow the path having higher intensity. In the end, all ants start moving with the same path, and this path is considered the best path for food searching. This process of food searching by ants produced the ACO algorithm. The ACO algorithm is used to discover the optimal path among different available paths [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This is known as the traditional ACO algorithm. There are some issues like the slow rate of convergence and preferred local optima rather than global optima. These drawbacks have a negative impact on the latency and convergence of solutions on networks. A tiny delay in healthcare can be a reason for the death of a patient.\u003c/p\u003e\u003cp\u003eTo resolve the aforementioned issues, we proposed an Intelligent ACO-Route Detection (IACO-RD) algorithm in healthcare using WBSN with a mobile sink. In the proposed work, three kinds of sensors have been attached to the patient to collect the health data. First is an implanted sensor on the body to collect the internal information of the patient. Second, wearable sensors are attached to the skin of the patient to collect patient-external information and third are an environmental sensor to collect the environmental information of the surrounding of the patient. These sensors form a network known as WBSN. The sensor nodes are considered as BSN. Each BSN is attached with a BSN Coordinator (BSNC) to send the data. Then mobile sink (Ms) is used to discover a best path using the proposed IACO-RD algorithm to collect patient data from BSNC. In the proposed work, global search ability increased with the consideration of a new heuristic distance factor. This also improves the rate of convergence. The major contribution of the proposed work is as\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePresenting the IACO-RD algorithm to find the shortest path between the mobile sink and BSNC.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eEnhance the lifetime of the network and reduction in packet loss using the IACO-RD algorithm.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eImproving the data collection by the introduction of the mobile sink.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eReducing the consumption of energy to enhance the lifetime of sensor nodes.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eComparing the performance of proposed work with similar existing prior works.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe remaining paper is structured as follows. Second section representing the prior work and third section is presenting the proposed work. Fourth section is used for result and discussion and finally fifth section is concluding the work.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. RELATED WORK","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe low-energy adaptive clustering-hierarchy (LEACH) algorithm [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] is a traditional clustering process for WSNs and used to divide the clusters into various sub-regions. Then the optimal path between Ms and cluster head (CH) is obtained by applying a routing algorithm. When there are several Ms are available then CHs are grouped together to make subsets and it is assigned to one Ms. Ms traverse all the CHs as assigned in a subset and return to the initial location. Mottaghi and Zahabi [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] presented an improved LEACH algorithm with Ms and rendezvous nodes. They used to exchange the process of selection of CH and also preserve the advantages of the LEACH algorithm. In the case of large networks, the given algorithm reduces the consumption of energy in comparison to the traditional LEACH algorithm. Wang et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] presented an energy-efficient dynamic routing approach for managing the movable sinks. They manage the reconstruction of routes by setting some communication rules. They resolved the hotspot problem by rotation of a cluster head (CH). The issues in the collection of data through mobile sinks are highlighted by Deng et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To resolve the issue, they designed a primal-dual online approach with less prior knowledge. Zhang et al [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] highlighted the issues created in maximization of network utilization when mobile sinks collected data in the network and also maintaining the objectivity of the network. They presented a distributed-data collection method to resolve the aforementioned issue. Sharma et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] presented a rendezvous based routing protocol (RRP). They highlighted the issues of energy and delay requirements. The data transmission is done with two different modes. The proposed protocol has better results in the consumption of energy, delay, and a lifetime of a network in comparison with prior work. Wang et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] presented an algorithm based on an energy-aware approach for the relocation of the sink. They used a maximized capacity path protocol for designing an energy-aware algorithm. They have shown the movement of mobile sinks whenever it meets relocation conditions and the new place will be given with the highest weight value. The algorithm has taken a very elongated time to get the condition of relocation and also it isn\u0026rsquo;t able to increase the lifespan of the network.\u003c/p\u003e\u003cp\u003eFadel et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] highlighted the use of sensors and the sensor technologies are used in different sectors such as smart healthcare, monitoring of the environment, security, and military. Li et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] presented the clustering approach to increasing the lifetime of the network in WSNs. They presented the process of clustering that use to divide the entire regions into smaller sub-regions. The partitioned sub-regions are called clusters. A cluster is a set of sensors called cluster members. The CH is one of them having higher energy. A CH is liable for assembling the data from different cluster members and these members are receiving the data from sensors. The CHs are being changed during the completion of operations. Hence it can improve the lifetime of the sensors [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Khalily-Dermany et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] introduced the mobile sink in the network to get a reduction in hop counting traveled by data packets. They highlighted the demerits of the static sink and the merits of the mobile sink. The mobile sinks are used to gather the data for CHs in a single hop and after collection of all data; mobile sinks sent the data to the base station for further execution. Gupta and Jha [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] highlighted the path for the collection of data by mobile sinks from CHs having low energy consumptions. They simulated the insect's behavior for searching for the optimal path. Kaur and Grewal [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] presented particle swarm optimization (PSO) for design the optimal path for mobile sink with effective energy. The author presented the Ant Colony Optimization algorithm to find a solution for food searching. The algorithm is motivated by the performance of natural ants. They proposed linear programming for routing and also particle encoding scheme. They find the local and global best solution by using Pareto dominance.\u003c/p\u003e\u003cp\u003eBoyineni et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] proposed a protocol for energy-effective data reporting. They proposed a novel logical coordinate system for traversing the path and forwarding the packets of data. By the use of SinkTrail and sojourn location of the movable sink sensor nodes for further selecting the next hop. The shortest distance path is established between sensor modes and mobile sinks. The used algorithm has reduced the control overheads. Wang et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] proposed an Improved ACO with mobile sinks (IACO-MS) for WSNs to find the optimal route using CHs. They divided the network into clusters by assigning a CH among them. They find the optimal distance between the mobile sink and CH, then the mobile sink traverse on the route and collect the data from CHs. The author highlighted the global search ability of the IACO-MS. Liu et al [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] presented a design of protocol transmission with multiple load-balancing schemes (TMLBS) for information delivery. The key contribution of the protocol is to maximize the lifetime of WSNs. They establish the trajectory path through the use of the ACO algorithm. In recent research, it is reported that moving mobile elements can significantly increase the performance of WSNs [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Vehicles or robots can be used as mobile elements [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These mobile elements behave like as edge of nodes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"3. PROPOSED WORK","content":"\u003ch2\u003e3.1 Network Model\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn a region, Implanted and wearable both types of sensors are randomly deployed. Sensors are consistent and static in character. All sensors having equal energy and during the complete operation the batteries of sensors can\u0026rsquo;t be replaced and recharged. The data transmission is performed at different transmission rate. Multi-hop meshes topologies are used to inter connect the sensors. It is considered that there is a number of BSN. The BSN is generating the patient data and BSNC is working as a forwarding node to send the data to one of the nearest mobile sinks. The mobile sink is collecting the data by BSNC and forwarding the data on a priority basis to the fog layer for further processing. On priority basis indicates that they are forwarding the critical data before the non-critical data. Here base station is used to maintain all the details of the route to the mobile sink.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represents the scenario of the proposed network model. In this network scenario, it is assumed that there is a connected undirected graph CUG =(S, L, Ms), where S as a set of sensor nodes and sensor coordinators, here sensor nodes are collection of BSN (s\u003csub\u003e1\u003c/sub\u003e, s\u003csub\u003e2,\u003c/sub\u003e\u0026hellip;..s\u003csub\u003en\u003c/sub\u003e) and sensor coordinators are collection of BSNC (c\u003csub\u003e1\u003c/sub\u003e c\u003csub\u003e2\u003c/sub\u003e,\u0026hellip;..c\u003csub\u003en\u003c/sub\u003e), L as set of link (l\u003csub\u003e1\u003c/sub\u003e, l\u003csub\u003e2\u003c/sub\u003e, \u0026hellip;l\u003csub\u003en\u003c/sub\u003e) edges between S and Ms, and Ms is the set of mobile sink (Ms\u003csub\u003e1\u003c/sub\u003e, Ms\u003csub\u003e2\u003c/sub\u003e,\u0026hellip;Ms\u003csub\u003en\u003c/sub\u003e). The main aim is minimize the distance S to Ms so that S can send the data to Ms with minimum distance and time. In Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e the white circles are representing the BSNs, gray circle are representing the BSNCs, black circle are representing the mobile sinks.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Energy Model\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn this proposed work we considered basic radio model for energy measurement. The energy required in the data transmission (sending and receiving) is computed with basic radio model [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The consumption of energy is divided as required for transmitted part and required for receiver part. The energy required to transmit data from a sensor node to a particular node having y bits of data can be calculated as\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{E}_{Tx}\\left(y,d\\right)=\\:{E}_{el}*y+\\left\\{\\left({Є}_{Fs}\\right)\\:or\\left({Є}_{mpf}\\right)\\right\\}*y*{d}^{n}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{el}\\)\u003c/span\u003e\u003c/span\u003e is used as energy for radio model, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Є}_{Fs}\\)\u003c/span\u003e\u003c/span\u003e is consumption of energy for free space system, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Є}_{mpf}\\)\u003c/span\u003e\u003c/span\u003e is consumption of energy for multi-path fading system, and d\u003csup\u003en\u003c/sup\u003e is d\u003csup\u003e2\u003c/sup\u003e and d\u003csup\u003e4\u003c/sup\u003e power losses for free space system and multi-path fading system.\u003c/p\u003e\u003cp\u003eThe energy required for receiving the y bits data by a sensor node is calculated as\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{E}_{Rx}=\\:{E}_{el}*y$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Strategy for BSN formation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFor the formation of BSN, it is distributed over entire network. Here, each BSN has a member of one BSNC. Therefore the BSN can send their patient data to the BSNC.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Strategy for selecting BSN coordinator\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBSNC is selected on the basis of two factors. First factor is residual energy of the BSN and another factor is distance from neighbor BSNs. Preferably the centre located nodes becomes BSN coordinator (BSNC). BSNC is located at the center location. BSN those who are near to BSNC can send their patient data with minimum distance and time. The BSNC is not fixed for complete operation. The BSNC will be changed if their residual energy becomes lower than pre-decided threshold value and another node same from group become new BSNC if they have higher residual energy and also satisfy the distance condition.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Procedure for route formation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe patient\u0026rsquo;s data is collected by BSN and these nodes are forwarding the data to the BSNC then after these data is collected by mobile sink. The aim of the propose work is to select the optimal and adaptive route for transmission of data from BSNC to Ms with no loss of data by using artificial intelligence technique in minimum distance and time.\u003c/p\u003e\u003cp\u003eHere, Ms collects the data in similar fashion as a travelling salesman problem. The nature of the problem is NP-hard and by the use of heuristic function an optimal solution is achieved. In the proposed work, the mobile sink is treated as salesman and BSNC is treated as different location that needs to be visited by salesman. Therefore Ms collects the data by visiting all the BSNC by implementing intelligent ACO algorithm. Here each Ms is assigned with an average number of BSNC and it can be obtained by\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"622\" height=\"51\"\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA distance matrix between Ms and BSNC is designed for computation purpose.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"603\" height=\"208\"\u003e\u003c/p\u003e\u003cp\u003eWhere, Ms is represented by row of the matrix and BSNC is represented by column of the matrix.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Intelligent ACO-RD implementation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIn traditional ACO algorithm, it consist a team of ants and known as ant system. In traditional ACO algorithm, ants work in as a team to optimally complete a difficult task to search food for survival. The team of ants can provide solution for any NP hard problem ant it is based on metaheuristic approach. The metaheuristic approach is known to be reliable and adaptable to solve a variety of combinatorial optimization issues. In traditional ACO-based algorithms, artificial ants are fashioned to imitator the doings of real ants in an organization to see the best track. After the position use, a piece ant moves from one BSNC to a new BSNC and deposits its pheromone. Decision paths are created based on their path and pheromones are saved and renewed. When the process is complete, the solution paths are evaluated and select the best path where contains the most pheromone.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{{P}^{k}}_{ij}\\left(t\\right)\\:=\\:\\left\\{\\begin{array}{c}\\frac{{\\left({\\tau\\:}_{ik}\\left(t\\right)\\right)}^{\\alpha\\:}{\\left({\\eta\\:}_{ik}\\left(t\\right)\\right)}^{\\beta\\:}}{\\sum\\:_{kЄ{permitted}_{k}}{\\left({\\tau\\:}_{ik}\\left(t\\right)\\right)}^{\\alpha\\:}{\\left({\\eta\\:}_{ik}\\left(t\\right)\\right)}^{\\beta\\:}}\\:\\:\\:\\:\\:\\:\\:\\:if\\:jЄ\\:{permitted}_{k}\\:\\\\\\:0\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:otherwise\\end{array}\\right.\\:,$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe basic ACO algorithms, to create a viable solution, the ants use the probabilistic rule to select the next city to visit. Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e representing the probability of path movement from BSNC (i) to BSNC(j), where τ\u003csub\u003eij\u003c/sub\u003e(t) is information about pheromone in the used path from BSNC(i) to BSNC(j) and η\u003csub\u003eij\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1/d\u003csub\u003eij\u003c/sub\u003e, where d\u003csub\u003eij\u003c/sub\u003e is the distance from BSNC (i) to BSNC (j), permitted\u003csub\u003ek\u003c/sub\u003e signify the BSNC that permitted to visit by ants, α and β are used as a constant, and its values regulate the action of pheromones.\u003c/p\u003e\u003cp\u003eHowever there are some issues with tradition ACO like slow convergence rate and easily adopting the solution which is locally optimal not globally. Now we proposed the novel intelligent ACO and enhancing the capability to adopt solution which is globally optimal by modifying the distance factor. The minimum distance is considered by ant from node i to target node n through node j as hop.\u003c/p\u003e\u003cp\u003eη\u003csub\u003eij\u003c/sub\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{\\text{min}\\left[dis\\left(i,n\\right),\\:dis\\left[(i,j\\right)+dis(j,n)\\right]}\\)\u003c/span\u003e\u003c/span\u003e (6)\u003c/p\u003e\u003cp\u003eWhere η\u003csub\u003eij\u003c/sub\u003e is the heuristic value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:dis\\left(i,n\\right)\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:dis(i,j)\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:dis(j,n)\\)\u003c/span\u003e\u003c/span\u003e are the distances from BSNC(i) to BSNC(n), BSNC(i) to BSNC(j), and BSNC(j) to BSNC(n). After substituting the Eq.\u0026nbsp;6 into Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:{{P}^{k}}_{ij}\\left(t\\right)\\:=\\:\\left\\{\\begin{array}{c}\\frac{{\\left({\\tau\\:}_{ik}\\left(t\\right)\\right)}^{\\alpha\\:}{\\left(\\:\\frac{1}{\\text{min}\\left[dis\\left(i,n\\right),\\:dis\\left[(i,j\\right)+dis(j,n)\\right]]}\\right)}^{\\beta\\:}}{\\sum\\:_{kЄ{permitted}_{k}}{\\left({\\tau\\:}_{ik}\\left(t\\right)\\right)}^{\\alpha\\:}{\\left(\\:\\frac{1}{\\text{min}\\left[dis\\left(i,n\\right),\\:dis\\left[(i,j\\right)+dis(j,n)]\\right]}\\right)}^{\\beta\\:}}\\:,\\:\\:\\:\\:\\:if\\:jЄ{permitted}_{k}\\\\\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:0\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:otherwise\\:\\end{array}\\right.\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFor best results, the ant pheromone trace values are updated with each iterations. This makes it possible to show the performance of the ants and to evaluate the worth of the solution. The process of updating is based on unsupervised learning approach of ACO which helps to get better subsequent decisions. The pheromone updating execution includes localized and globalized updates. The amount of pheromone evaporate with respect to time and after some time traces are updated as\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:{\\tau\\:}_{ij}\\left(t+m\\right)=\\:\\left(1-\\:{\\rho\\:}\\right){\\tau\\:}_{ij}\\left(t\\right)+\\:\\varDelta\\:{\\tau\\:}_{ij}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhere ρ is a evaporation coefficient between time t and t\u0026thinsp;+\u0026thinsp;m, 0\u0026thinsp;\u0026le;\u0026thinsp;ρ\u0026thinsp;\u0026le;\u0026thinsp;1. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{\\tau\\:}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is current iteration value of pheromone deposited in time between t and t\u0026thinsp;+\u0026thinsp;m, on the edges made by (i, j) by ant k. The \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:{\\tau\\:}_{ij}\\)\u003c/span\u003e\u003c/span\u003e can be obtained by\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:{\\tau\\:}_{ij}=\\:\\sum\\:_{K=1}^{N}\\varDelta\\:{\\tau\\:}_{ij}^{k}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere total quantity of ants represented by n. The quantity of pheromone left by each ant, when it travels\u003c/p\u003e\u003cp\u003efrom node i to node j is define as\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:{\\tau\\:}_{ij\\:}^{k}=\\:\\left\\{\\:\\:\\:\\begin{array}{c}\\frac{Q}{{L}_{ij}}\\:\\:\\:\\:\\:edge\\:\\left(i,j\\right)\\:Є\\:best\\:route\\:\\\\\\:0\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:otherwise\\end{array}\\right.$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhere Q represents a constant parameter and L\u003csub\u003eij\u003c/sub\u003e is a distance parameter from BSNC\u003csub\u003ei\u003c/sub\u003e to BSNC\u003csub\u003ej\u003c/sub\u003e. The updated approach will work in number of iteration until unless it gets the optimal path for travelling of mobile sinks among BSNC. Algorithm1 represents an algorithm to find optimal and best route using IACO-RD from mobile sink to BSNC.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAlgorithm1\u003c/strong\u003e\u003cp\u003e\u003cem\u003eIntelligent ACO-Route Discovery\u003c/em\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eCalculate Average for BSNC assignment to Ms\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAR0AAAAiCAYAAABiMQ23AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAAAzQSURBVHhe7ZxPaCPl/8ff/aKwh5SCs4dmi2BnaXuThtrioeDSadx2uwdZaVkPOwra7tGatOx6chcRBSdVDy7URLDr2nTpRUzTCtJQu4LgkozUgzSRrOB2JgdzKDOHFRae38E8w8yTmWSm2ybz/X3nBTl0nj/zzOf9mc/zPJ8naQchhCAgICCgRfyHvRAQEBBwkgRBJyAgoKUEQScgIKClBEEnICCgpQRBJyAgoKUEQScgIKClBEEnICCgpQRBJyAgoKUEQScgIKClBEEnICCgpfy/DDq6rmNxcRGyLLNFLUWWZSwuLkLXdbbovwq/2LOdPImWqqoiGo1CVVW26Fjwiz5ubXTsQefmzZsnZlw3yLKM8+fPY2hoCJFIhC2GLMs4ffo0Ojo60NHRgfHx8aZGgk27s2fPNn3OSCSCgYEBnD9/vu0OcVQa2VPXdYyPjxs2YT8DAwP45JNPHO1bKpUwNzdnsevc3BxKpRJ0Xcf7778PXdfx+uuvW/pNJBJsV1BVFWfPnrXUc9J2c3MTk5OTtveVZRmpVIpt8kRa5vN5nDlzBuFwmC16YhrpQ8t95+/kGJEkifA8TxRFYYtaQqFQIP39/aRQKLBFdUiSRAC4Gq+maUQQBKN+sVhkqzQkk8m4HpefcGvPQqFAOI4jgiAQTdMIqdnsxo0bBIDlOtsmFotZyrLZLOE4jgAgoiha2iSTyaaaSZJEOI6zHXM2myXDw8OE4ziSTqct902n08Z9M5mMpZ2Zo2gpimLDPo+KW32Iz/z92IKOneO1EmooSZLYIltEUSQAHB3UDBUMgOv+WdodkL3ixZ5UezZIEJOdzS8d7dvJV2h/7ItK+2L7MyOKou2YqYZO9yQeNHJbjxBCFEUh4+Pjrup6wYs+xGf+fizbK13X8dFHH4HjOPT09CAUCrFVTpzl5WUAwNWrV9miOlRVhaIoGB8fR7VaxcHBAVvFYGNjA/fv3wfP8+A4DmNjY2wVV1y9ehW9vb1YXV1li3yJF3vmcjlUq1VMT0+zRXj++ecBAPv7+8Y1TdPw4MEDUy0rfX19EAQBPT09xjVd1/Ho0SOkUilwHIf19XVLG9R0/f333+s0SiQSWFhYgCAI+Pbbbx39c2xsDC+88AI6OzvZIgtetDyprZUXffzm78cSdJaXlxGNRvH48WO2qCWoqopbt25hcnLS0aHMVCoVPPPMMxgdHQWYF8KMqqq4c+cOLl26hMPDQwwODqKvr4+t5opQKITJyUncunWr6d643Xixp67r2NraAs/zGBoaYouxt7cHABgYGDCudXZ2ore3F9vb29jZ2THV/pdQKISXX37ZYutSqQQAmJqawuDgILLZbF3eoFKpoLe319JOlmV8+OGH4DgOH3/8cdPniUajTet40XJ9fd02GD8JXvSBD/39iYPOxsYGFEXB0NAQDg8PjZmNsrm5ibm5OYyMjFhunkgkbJOCuq4jHo/XJfkaJb/y+TzK5bLFsRuRy+UwMjJi+5KYkSQJ165dw19//YVqtWoRmY6zo6MDp0+fxtraGmRZxu7uLtuNwcDAAMrlsm30PynMSVhzMlCWZYyMjNgmF73Yk65aent761YIsiwjm81CEAScO3fOuE4dEgDeeOMNrK2tmVr9y5tvvml5oQ4ODnDq1Cl0d3djfn4e1WoVuVzO0iaXy+HUqVMWjRYXF1GtVjE1NWWbaDUTiUTw1ltvsZdtcaMlXWE08zOvGnnRBz709ycKOqqq4rvvvsPNmzfZIqBmzKmpKSSTSdy/fx/5fJ6tgq2tLcOYsizjueeew8rKCm7fvg1CCIrFIsrlMjo7Ow1h2EC1vr5ubO2aoes6fvnlF4yNjaGnpwccxxmzsZlEIoEzZ84gEonYztbLy8tYWVlBoVDA33//DdRmSfbFM9PofifFysoKFEUBz/MYHR1FOBxGKpXCzs4OcrkcRFHEgwcPoGma0caLPekLYHbQSqWCeDyOaDSKwcFB2y1NPB6HJEmoVqt47bXXEI/HG04s5hXD0NAQeJ63zKJ0xWVeVZRKJfz666/gOA7z8/PG9ePAjZZut1ZeNfKijy/9nU3yeCEWixlJqUwmY5vgM2fC2aSUJElG8pHWs0t00b7Z9sTUzilpxWJO7CmKQnier0suFgoFMjMzQzRNM+qw/YuiaLmmaRqZnZ1tOAanvljMiTw3Hzu7mKH9ZTIZkkwmDY3sErpe7WlO7po/w8PDdTraYT41YnWg2CVj2QS1XR363G6fxQtutPRyauVWI6/6mO1Cx8zaudX+fuSVjjkyorZPtIu+oVDImGXYiPfss88aZTs7O9je3rbdRzrtQdFkeW+HefahuQXzLEKT4tevX0coFDJmcjoDUaanp1Eul3H9+nWg9pxffPFFw1mN3q8Z8XgctZNFV594PM52YWFvbw88zxt5kYsXLwKmlYA5+e/Fnrqu4+DgAIIgQNM0EEKgaRrS6TTK5TKi0Sg2NjbYZhYuX76MH374ARzHYXt7G6+88krdiofmJMzjmZ+ftySU7VYV1N9Y7Y6DZlrqtcQ3+z444VYjL/rAp/5+pKBDk3MLCwvGlmdhYYGtZkCXWQcHB4ZD0WUxu89mT79o0gzMcu+o7O/v1+WdDg8PUalUAADvvfcerly5YgmmMJ3CUC5evAhRFHH79u267Z6fUFUVP/30E5566ik8fPjQkrNodOrkBvaFQM0ZL1++jK+++grVahVvv/22ZQv05ZdfMr386wPmwENPZig0J2H2i76+PktCeX19vU6jdkKDBzuB2nGSGvnR3z0HHV3Xsbq6it9++80y24qiiK6uLnR3d7NN0N3dja6uLsu1zc1NXLhwwfib7tOz2Sw2NzeBmnBXrlxBuVyGJElG9LeDzUvYYd7fovaC9PT0GMeIdFam96F5Aqejw88//xyCIGBhYaHpjN4u6Mz1+PFjy8RAn83p1MmNPRu9EHSiKZfLRi5P0zQ8fPiQrQrUAs+7774L2KyIFUWpsz9dQVerVXzzzTd49OhRXR364pgnu1ZhFyidOIpGbvTxq797DjrLy8t46aWXLMsqusx2gl3WybKMCxcuWAQJh8P4/vvvIQgCpqam0NHRgf7+fjz99NMoFAqOWwinJZwdmqahq6vLMvtQxyyVSrhz545FdLqUNQfTVCplHNWGQiF8/fXX4Hken376aVPHpv01g57suf00mnnozPXZZ59ZNKNjYZfRbu1pdtBGWwhzeT6fx7179xztZLeSVVUVe3t7tpMZnagSiQSq1WrdqoL25+YF3d3drTuCb0QjLdmXvRleNHKrD3zs756CztraGrq6uupWHLRzN/vMP/74A6FQyHYvGA6H8eKLL2JpaQlaLUewtbVVtwUzQ6O3ecnoRD6fxz///GMJdtQxP/jgA1y7ds0yrtXV1br97b179ywBNhwOY3R01JVjU9gXneW4cjqNZko6u05PT6NSqRgO5NaedGtll4PTHY6q9/f3Hb+bg9qpDGr5A4pdroZCbQ/A9jsr586dgyAIllyEHbIso1gsNvQzJ+y09LK18qqRW33gZ3+3pJsd0Gq/o7E7WSK1Ewh6SuD0Ow1RFG2/2m6GZsid+nDC6eTMTLFYJDzP15300K/cs9dpffPpkPnkgI6xUCgQnudtfwLA4nSvk6LZzxN4nie7u7vk7t27lrJm9tQ0zTg9isViluv09030BIs9AUHtq/jm3z5pmkZisZjF1qSmwfDwsO34KU4/maAoimKMRxAEi2+pqkpisRhJJpOWNm5opKUkSbbX7TiKRs30IT7396ZBhzZE7SjUHHjokRgtox+7AYmiSNLpNHvZAnVK+pmYmCBLS0tEVVW2qgU6DvbhzGXmftlnmJ2dNV4A9nnNn2QySe7evUuKxSKZmZkx+rpx44btUS+L1OD3KCeBk3NSZ+I4zvaFa2RPerTr9OE4jkxMTJBsNmtppyiKYadisUhmZ2ctdja3oeMz9+tkNzdHt5qmkXQ6Tfr7+y19TkxMeJ7gKE5aappGZmZmbCdnO46iUSN9/hv8vWnQaQWappGlpSWytLRESqUSSaVSZGJiwmIA84xqh9MD+gXqRHaO4kf8bs924gct/a5PIxu1PegoteWv3eBIbQl86dIlS7S2o9FD+oFMJuNrJ2Hxuz3biR+09Ls+jWzU9qAjNfj/J5RGD2Cm2f6+XRQ8/N8TP+FXe7YTP2npV32a2ajtQYcabnh4uC4PoKqqsRd3a1jan19mADp+JwH8jt/s2U78qKXf9HFjI09H5idBJBLBzz//jMHBQbzzzjuW75+8+uqrUFUVP/74Y90xvRORSAR//vkndF339L2Lk0CWZeTzeWxsbBzpONYP+Mme7cSvWvpJH7c26iCEEPZiQEBAwEnR9pVOQEDA/xb/B4NceJeV+enfAAAAAElFTkSuQmCC\" width=\"285\" height=\"34\"\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eConstruct a distance matrix as per Eq.\u0026nbsp;4.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eApply ACO for route formation between Ms and BSNC\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eAssign BSNC to nearest Ms\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eFor i\u0026thinsp;=\u0026thinsp;1 to Avg(Ms)\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eFor j\u0026thinsp;=\u0026thinsp;1 to Ms\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eAssign Ms[j] := BSNC[i]\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eEnd for loop\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eIf (Avg(Ms) * Ms\u0026thinsp;\u0026le;\u0026thinsp;n(BSNC))\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eRandomly assign remaining BSNC[i] to Ms[j]\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eEnd if\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eStart data collection at Ms\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eRepeat process till last BSNC.\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. RESULT AND DISCUSSION","content":"\u003cp\u003eThe proposed IACO algorithm is used to increase the lifetime of the network and collects the health data from BSNC through mobile sinks in WBSN environment to increase the efficiency of routing. Mobile sinks are used to collect data from sensor nodes by establishing the shortest path between BSNC and itself using proposed IACO algorithm. The performance of proposed algorithm is evaluated through simulations. The proposed algorithm is compared with ACO-M [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], PSO [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and IACO-MS [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Matlab R2018b is carried out the simulation iterations. Simulations of proposed algorithms are performed with parameter listed in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The sensor nodes and mobile sinks are deployed with an area 300 x 300 m\u003csup\u003e2\u003c/sup\u003e and network size (100\u0026ndash;500 nodes).\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\u003eSimulation parameters\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\u003eParameters\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\u003eTarget Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Sensor nodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[10\u0026ndash;50]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensor type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStatic\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSensor nodes initial energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5 J\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommunication radius\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSimulation rounds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000\u0026ndash;5000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePacket length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e500 bytes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE\u003csub\u003eTx\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.405 \u0026micro;J/bit\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE\u003csub\u003eRx\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.64 \u0026micro;J/bi\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE\u003csub\u003eel\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\u003eЄ\u003csub\u003efs\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 pJ/bit/m2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eЄ\u003csub\u003empf\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0013 pJ/bit/m4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Ms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\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 performance of network is evaluated with a metric called lifetime and Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the measured network lifetime comparison at first and last node death. Energy consumption can be reduced in the data forwarding from BSNC to mobile sink using mobile sink nodes. The proposed work is compared with aforementioned work and it can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the network life times of presented work are higher than the existing work.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn proposed work BSNC is selected among BSNs having higher residual energy. The rotation of BSNC is performed when the existing BSNC have less residual energy as compared to threshold energy. The average energy consumption is shown by Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. It has been clearly observed that the proposed algorithm has lower consumption of energy as compared to existing works and thus proposed algorithm performance in terms of consumption of energy is good.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that average loss of packets in different network sizes. It is clearly observed that in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e that the proposed algorithm has the minimum packet loss as compared to existing work. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presenting the average loss of packets with different numbers if mobile sinks. It can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e when number of mobile sink increases the packet losses are decreases. The rate of decrease in packet loss is lower in proposed IACO-RD algorithm as compared to existing work.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presenting the data collected by mobile sink with different number of rounds. It is clearly seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e that the presented algorithm collecting more data in comparison to other existing works.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the comparative view of all existing work and the proposed algorithm with respect to alive sensor node after number of rounds. It is clearly notice that in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e number of alive nodes decrease as the number of rounds increases and the proposed algorithm results has better than as compared to the other existing work.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eWe proposed a novel IACO-RD algorithm to increases the efficiency of data collection, lifetime of network, lifespan of BSN and reduction in data loss of WBSN in healthcare. Highly energetic BSN is elected as a BSNC of the BSNs. Mobile sinks are used to collect data through BSNC which covers minimum distance to get data. The minimum distance coverage increases the lifespan of BSN and improves the lifetime of network with the use of ACO based routing scheme. The simulation results verified the proposed route detection scheme that reduces the travelled distances as compared to the prior existing works and collects the data from BSNC with minimum losses of data. In future, bio-inspired algorithm can be implemented through the mobile sinks to increases the effectiveness and to reduce the delay in healthcare.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests \u0026ndash;\u003c/strong\u003e\u003cp\u003eThere are no conflicts of interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003edetails: There no funding details.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAmit Kishor wrote the main manuscript text and Ashima prepared all figures used in the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e\u003cp\u003eThe authors would like to thanks to Department of Computer Science \u0026amp; Engineering, Subharti Institute of Engineering and Technology, Swami Vivekanand Subharti University, Meerut, India to give this platform to work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJ. 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Shirazi, A. Gouglidis, A. Farshad, and D. Hutchison, \u0026quot;The extended cloud: Review and analysis of mobile edge computing and fog from a security and resilience perspective,\u0026quot; IEEE Journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2586-2595, 2017.\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":"Body Sensor Node, Intelligent System, Mobile Sink, Healthcare, Data Transmission, Wireless Body Sensor Networks","lastPublishedDoi":"10.21203/rs.3.rs-7532533/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7532533/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDelay in the transmission of patient data is a major issue in healthcare for both patients and end-users with the use of static sink nodes in Wireless Body Sensor Networks (WBSNs). Reduction in the network lifetime, early death of sensor nodes, and hot spot problems are the main issues in healthcare data transmission. A body sensor node (BSN) collects the real time data therefore agile approaches must be adopted. Data collecting efficiency can be increased with mobile sinks. In WBSN, an artificial intelligence-based approach for route selection in healthcare plays a significant role in delay sensitive scenarios. An Intelligent Ant Colony Optimization based route detection (IACO-RD) is proposed to overcome the aforementioned issues. The performance is verified through the simulation and the result is compared with previous works. The simulation result shows the proposed algorithm performance that considerably enhances the lifetime of network, lifespan of sensor nodes, and reduces the data loss.\u003c/p\u003e","manuscriptTitle":"Energy-Efficient Intelligent Ant Colony Optimization for Route Detection in Healthcare Using Wireless Body Sensor Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 09:02:41","doi":"10.21203/rs.3.rs-7532533/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":"1ade31f8-6222-4c31-aa5b-fefa6aba00ed","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-19T13:54:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 09:02:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7532533","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7532533","identity":"rs-7532533","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}