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Adaptive SSO based node selection for partial charging in wireless sensor network

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Abstract

Wireless chargers provide dynamic range of power to wireless sensor network (WSN). These chargers may come under the Wireless Rechargeable Sensor Networks (WRSNs) category. The WRSNs are flexible in nature and this is further improved by introducing the mobile chargers (MCs) in WRSNs. MCs perform wireless energy transfer in WSN to replenish the energy minimized nodes. WRSNs offer controllable and predictable energy replacement to maximize the network lifetime. The availability of a single MC fails to maximize the lifetime of whole network. Therefore, multiple MCs are used in this partial charging approach to maximize the lifetime of whole network. To accomplish this partial charging process, initially the whole network is clustered by k-means clustering algorithm. Then, a deer hunting optimization (DHO) algorithm is introduced to select the Cluster Head (CH) for the selected clusters. Through the selected CH, the data transmission is performed between the source node and sink node. Next, the nodes that lost their energy during data transmission is identified using a metaheuristic algorithm namely adaptive social ski-driver optimization (ASSO). Based on the optimal result, the path is planned by multiple MCs to perform partial charging. While compared with single MC, the multiple MCs may face some coordination problems. To avoid this, the multiple MCs are co-ordinate on a distance basis, so that the complex partial charging can be achieved effectively. Partial charging improves the robustness and scalability of the entire network. Experimental results shown by the proposed approach has significantly outperforms the existing methods in terms of efficiency, network lifetime, delay, and complexity. The overall survival rate achieved by the proposed algorithm is 98% for 400 nodes, which is better than other algorithms. This is due to the introduction of an optimization for optimal node selection, whose performance has directly induced an influence in overall survival rate.

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References

  1. Fulara YK (2015) Some aspects of wireless sensor networks. Int J AdHoc Network Syst 5(1):15–24

    Article  Google Scholar 

  2. Iqbal Z, Kim K, Lee HN (2016) A cooperative wireless sensor network for indoor industrial monitoring. IEEE Trans Industr Inf 13(2):482–491

    Article  Google Scholar 

  3. Anisi MH, Abdul-Salaam G, Idris MY, Wahab AW, Ahmedy I (2017) Energy harvesting and battery power based routing in wireless sensor networks. Wireless Netw 23(1):249–266

    Article  Google Scholar 

  4. Kaswan A, Nitesh K, Jana PK (2017) Energy efficient path selection for mobile sink and data gathering in wireless sensor networks. AEU-Int J Electron Commun 73:110–118

    Article  Google Scholar 

  5. Ha I, Djuraev M, Ahn B (2017) An optimal data gathering method for mobile sinks in wsns. Wirel Pers Commun 97(1):1401–1417

    Article  Google Scholar 

  6. Garg V, Jhamb M (2013) Tracking the location of mobile node in wireless sensor network. Int J Adv Res Comput Commun Eng 2(6):2358–2362

    Google Scholar 

  7. Abdul-Salaam G et al (2016) A comparative analysis of energy conservation approaches in hybrid wireless sensor networks data collection protocols. Telecommun Syst 61(1):159–179

    Article  Google Scholar 

  8. Peixoto JPJ, Costa DG (2017) Wireless visual sensor networks for smart city applications: A relevance-based approach for multiple sinks mobility. Futur Gener Comput Syst 76:51–62

    Article  Google Scholar 

  9. Madhja A, Nikoletseas S, Raptis TP (2016) Hierarchical, collaborative wireless energy transfer in sensor networks with multiple mobile chargers. Comput Netw 97:98–112

    Article  Google Scholar 

  10. Xu W, Liang W, Lin X, Mao G (2015) Efficient scheduling of multiple mobile chargers for wireless sensor networks. IEEE Trans Veh Technol 65(9):7670–7683

    Article  Google Scholar 

  11. Sangare F, Xiao Y, Niyato D, Han Z (2017) Mobile charging in wireless-powered sensor networks: Optimal scheduling and experimental implementation. IEEE Trans Veh Technol 66(8):7400–7410

    Article  Google Scholar 

  12. Jiang L et al (2014) Effective on-demand mobile charger scheduling for maximizing coverage in wireless rechargeable sensor networks. Mob Netw Appl 19(4):543–551

    Article  Google Scholar 

  13. Gui T et al (2016) A novel cluster-based routing protocol wireless sensor networks using spider monkey optimization. IECON 2016–42nd Annual Conference of the IEEE Industrial Electronics Society. IEEE

  14. Gherbi C, Aliouat Z, Benmohammed M (2016) An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks. Energy 114:647–662

    Article  Google Scholar 

  15. Kushal BY, Chitra M (2016) Cluster based routing protocol to prolong network lifetime through mobile sink in WSN. 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). IEEE

  16. Jan B, Farman H, Javed H, Montrucchio B, Khan M, Ali S (2017) Energy efficient hierarchical clustering approaches in wireless sensor networks: A survey. Wirel Commun Mob Comput, 2017

  17. Salem AOA, Shudifat N (2019) Enhanced leach protocol for increasing a lifetime of wsns. Pers Ubiquit Comput, 1–7

  18. Abuarqoub A, Hammoudeh M, Adebisi B, Jabbar S, Bounceur A, Al-Bashar H (2017) Dynamic clustering and management of mobile wireless sensor networks. Comput Netw 117:62–75

    Article  Google Scholar 

  19. Huang H, Savkin AV, Ding M, Huang C (2019) Mobile robots in wireless sensor networks: A survey on tasks. Comput Netw 148:1–19

    Article  Google Scholar 

  20. Zorbas D, Raveneau P, Ghamri-Doudane Y, Douligeris C (2017) On the optimal number of chargers in battery-less wirelessly powered sensor networks. In 2017 IEEE Symposium on Computers and Communications (ISCC), IEEE 1312–1317

  21. Shokouhifar M, Jalali A (2015) A new evolutionary based application specific routing protocol for clustered wireless sensor networks. AEU-Int J Electron Commun 69(1):432–441

    Article  Google Scholar 

  22. Fu L, He L, Cheng P, Gu Y, Pan J, Chen J (2015) ESync: Energy synchronized mobile charging in rechargeable wireless sensor networks. IEEE Trans Veh Technol 65(9):7415–7431

    Article  Google Scholar 

  23. Mostafaei H (2018) Energy-efficient algorithm for reliable routing of wireless sensor networks. IEEE Trans Industr Electron 66(7):5567–5575

    Article  Google Scholar 

  24. Tomar MS, Shukla PK (2019) Energy efficient gravitational search algorithm and fuzzy based clustering with hop count based routing for wireless sensor network. Multimed Tools Appl 1–22

  25. Rahmati V (2020) Near optimum random routing of uniformly load balanced nodes in wireless sensor networks using connectivity matrix. Wirel Pers Commun 1–17

  26. Sarkar A, Murugan TS (2019) Cluster head selection for energy efficient and delay-less routing in wireless sensor network. Wireless Netw 25(1):303–320

    Article  Google Scholar 

  27. Mo L, Kritikakou A, He S (2019) Energy-aware multiple mobile chargers coordination for wireless rechargeable sensor networks. IEEE Internet Things J

  28. Zhu Y et al (2018) Velocity Control of Multiple Mobile Chargers over Moving Trajectories in RF Energy Harvesting Wireless Sensor Networks. IEEE Trans Veh Technol 67(11):11314–11318

    Article  Google Scholar 

  29. Rault T (2019) Avoiding radiation of on-demand multi-node energy charging with multiple mobile chargers. Comput Commun 134:42–51

    Article  Google Scholar 

  30. Soni S, Shrivastava M (2019) Novel wireless charging algorithms to charge mobile wireless sensor network by using reinforcement learning. SN Appl Sci 1(9):1052

    Article  Google Scholar 

  31. Liu F, Lu H, Wang T, Liu Y (2019) An Energy-Balanced Joint Routing and Charging Framework in Wireless Rechargeable Sensor Networks for Mobile Multimedia. IEEE Access 7:177637–177650

    Article  Google Scholar 

  32. Wu R, Guo H, Tang L, Fan B (2019) Autonomous Load Regulation Based Energy Balanced Routing in Rechargeable Wireless Sensor Networks. Appl Sci 9(16):3251

    Article  Google Scholar 

  33. Yang X, Han G, Liu L, Qian A, Zhang W (2019) IGRC: An improved grid-based joint routing and charging algorithm for wireless rechargeable sensor networks. Futur Gener Comput Syst 92:837–845

    Article  Google Scholar 

  34. Aslam N, Xia K, Hadi MU (2019) Optimal wireless charging inclusive of intellectual routing based on SARSA learning in renewable wireless sensor networks. IEEE Sens J 19(18):8340–8351

    Article  Google Scholar 

  35. Xie L, Shi Y, Hou YT, Sherali HD (2012) Making sensor networks immortal: An energy-renewal approach with wireless power transfer. IEEE/ACM Trans Networking 20(6):1748–1761

    Article  Google Scholar 

  36. Lehsaini M, Benmahdi MB (2018) An improved k-means cluster-based routing scheme for wireless sensor networks. In 2018 International Symposium on Programming and Systems (ISPS), IEEE 1–6

  37. Rajasekaran A, Nagarajan V (2018) Cluster-based wireless sensor networks using ant colony optimization. In International Conference on Intelligent Data Communication Technologies and Internet of Things, Springer, Cham 42–55

  38. Singh B, Lobiyal DK (2012) A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. HCIS 2(1):13

    Google Scholar 

  39. Brammya G, Praveena S, Ninu Preetha NS, Ramya R, Rajakumar BR, Binu D (2019) Deer hunting optimization algorithm: a new nature-inspired meta-heuristic paradigm. Comput J

  40. Tharwat A, Gabel T (2019) Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Comput and Applic 1–14

  41. Rajagopal A, Somasundaram S, Sowmya B (2018) Performance Analysis for Efficient Cluster Head Selection in Wireless Sensor Network Using RBFO and Hybrid BFO-BSO. Int J Wirel Commun Mob Comput 6(1):1

    Google Scholar 

  42. Azharuddin M, Jana PK (2017) PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks. Soft Comput 21(22):6825–6839

    Article  Google Scholar 

  43. Wang T, Zhang G, Yang X, Vajdi A (2018) Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks. J Syst Softw 146:196–214

    Article  Google Scholar 

  44. He L, Gu Y, Pan J, Zhu T (2013) On-demand charging in wireless sensor networks: Theories and applications. In 2013 IEEE 10th international conference on mobile ad-hoc and sensor systems, 28–36

  45. Boukerche A, Wu Q, Sun P (2020) A novel two-mode QoS-aware mobile charger scheduling method for achieving sustainable wireless sensor networks. IEEE Trans Sustain Comput

  46. Chawra VK, Gupta GP (2021) Hybrid meta-heuristic techniques based efficient charging scheduling scheme for multiple Mobile wireless chargers based wireless rechargeable sensor networks. Peer-to-Peer Netw Appl 14(3):1303–1315

    Article  Google Scholar 

  47. Huong TT, Binh HTT, Le Nguyen P, Long DCT, An VD (2020) Optimizing charging locations and charging time for energy depletion avoidance in wireless rechargeable sensor networks. In 2020 IEEE Congress on Evolutionary Computation (CEC) 1–8

  48. Liu T, Wu B, Zhang S, Peng J, Xu W (2020) An effective multi-node charging scheme for wireless rechargeable sensor networks. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications 2026–2035 

  49. Priyadarshani S, Tomar A, Jana PK (2021) An efficient partial charging scheme using multiple mobile chargers in wireless rechargeable sensor networks. Ad Hoc Netw 113:102407

    Article  Google Scholar 

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  1. Department of Computer Science & Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India

    Devarapalli Prasannababu & Tarachand Amgoth

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  1. Devarapalli Prasannababu
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  2. Tarachand Amgoth
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Correspondence to Devarapalli Prasannababu.

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Prasannababu, D., Amgoth, T. Adaptive SSO based node selection for partial charging in wireless sensor network. Peer-to-Peer Netw. Appl. 15, 1057–1075 (2022). https://doi.org/10.1007/s12083-021-01282-4

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  • DOI: https://doi.org/10.1007/s12083-021-01282-4

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