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Particle swarm optimization-based energy efficient clustering protocol in wireless sensor network

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Abstract

The nodes in the wireless sensor network are furnished with restricted and irreplaceable battery power. The continuous sensing, computation, and communication drain out the energy of sensors very quickly. The optimal utilization of the sensor energy has always been a key issue for all the applications in the wireless sensor network. To manage the energy issue of nodes, various approaches were proposed in the past which focused on designing the proper energy management methods. The clustering of sensors is one of the most popular techniques used to manage the energy-related concerns of networks. In this paper, a particle swarm optimization-based energy efficient clustering protocol (PSO-EEC) is proposed to enhance the network lifetime and performance. The proposed protocol uses the particle swarm optimization technique to select the cluster head and relay nodes for the network. The cluster head is selected by employing the particle swarm optimization based fitness function which considers the energy ratio (initial energy and residual energy) of nodes, distance between nodes and cluster head, and node degree to appoint the most optimal node for the cluster head job. For the data transfer to base station, the proposed scheme uses the fitness value based on residual energy of cluster head and distance to base station parameters to nominate the relay nodes for the multi-hop data transfer to the base station. The performance of the proposed protocol is compared with the various existing approaches in terms of different performance parameters such as energy expenditure, network lifetime, and throughput to evaluate its effectiveness. The proposed scheme has improved the lifetime of the network by 238%, 136%, 106%, and 71% as compared to the existing MDCH-PSO, MCHEOR, MOPSO, and HSA-PSO techniques used in the simulation results for the comparison purpose. The stability period of the network in proposed scheme is approximately 396%, 321%, 246%, and 126% more than the existing MDCH-PSO, MCHEOR, MOPSO, and HSA-PSO protocol .

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References

  1. Dietrich I, Dressler F, Dietrich I, Dressler F (2009) On the lifetime of wireless sensor networks. ACM Trans Sen Netw ACM Trans Sens Netw 5:39–41. https://doi.org/10.1145/1464420.1464425

    Article  Google Scholar 

  2. Zheng J, Jamalipour A (2009) Wireless sensor networks: a networking perspective. IEEE. https://doi.org/10.1002/9780470443521

  3. García-hernández CF, Ibargüengoytia-gonzález PH, García-hernández J, Pérez-díaz JA (2007) Wireless sensor networks and applications: a survey. J Comput Sci 7:264–273. https://doi.org/10.1109/MC.2002.1039518

    Article  Google Scholar 

  4. Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52:2292–2330. https://doi.org/10.1016/j.comnet.2008.04.002

    Article  Google Scholar 

  5. Al-Karaki JN, Kamal AE (2004) Routing techniques in wireless sensor networks: a survey. IEEE Wirel Commun 11:6–28. https://doi.org/10.1109/MWC.2004.1368893

    Article  Google Scholar 

  6. Tripathi A, Gupta HP, Dutta T et al (2018) Coverage and connectivity in WSNs: a survey, research issues and challenges. IEEE Access 6:26971–26992. https://doi.org/10.1109/ACCESS.2018.2833632

    Article  Google Scholar 

  7. Farsi M, Elhosseini MA, Badawy M et al (2019) Deployment techniques in wireless sensor networks, coverage and connectivity: a survey. IEEE Access 7:28940–28954. https://doi.org/10.1109/ACCESS.2019.2902072

    Article  Google Scholar 

  8. Toor AS, Jain AK (2019) International journal of electronics and communications (AEÜ) energy aware cluster based multi-hop energy efficient routing protocol using multiple mobile nodes (MEACBM ) in wireless sensor networks. AEUE Int J Electron Commun 102:41–53. https://doi.org/10.1016/j.aeue.2019.02.006

    Article  Google Scholar 

  9. Rajendra Prasad D, Kiran Kumar B, Indraneel S (2020) Mobility in wireless sensor networks. In: Lecture notes on data engineering and communications technologies. Springer, pp 165–171

  10. Kaswan A, Singh V, Jana PK (2018) A multi-objective and PSO based energy efficient path design for mobile sink in wireless sensor networks. Pervasive Mob Comput 46:122–136. https://doi.org/10.1016/j.pmcj.2018.02.003

    Article  Google Scholar 

  11. Rawat P, Chauhan S, Priyadarshi R (2020) A novel heterogeneous clustering protocol for lifetime maximization of wireless sensor network. Wirel Pers Commun. https://doi.org/10.1007/s11277-020-07898-8

    Article  Google Scholar 

  12. Priyadarshi R, Rawat P, Nath V (2019) Energy dependent cluster formation in heterogeneous wireless sensor network. Microsyst Technol 25:2313–2321. https://doi.org/10.1007/s00542-018-4116-7

    Article  Google Scholar 

  13. Sharma S, Bansal RK, Bansal S (2014) Issues and challenges in wireless sensor networks. In: Proceedings—2013 International Conference on Machine Intelligence Research and Advancement, ICMIRA 2013. Institute of Electrical and Electronics Engineers Inc., pp 58–62

  14. Akkaya K, Younis M (2005) A survey on routing protocols for wireless sensor networks. Ad Hoc Netw 3:325–349. https://doi.org/10.1016/j.adhoc.2003.09.010

    Article  Google Scholar 

  15. Gherbi C, Aliouat Z, Benmohammed M (2017) A survey on clustering routing protocols in wireless sensor networks. Sens Rev 37:12–25

    Article  Google Scholar 

  16. Rawat P, Chauhan S (2021) Performance analysis of RNC clustering protocol in wireless sensor network. Int J Sens Wirel Commun Control 10:957–966. https://doi.org/10.2174/2210327910666191218143503

    Article  Google Scholar 

  17. Rawat P, Chauhan S, Priyadarshi R (2020) Energy efficient clusterhead selection scheme in heterogeneous wireless sensor network. J Circuits Syst Comput. https://doi.org/10.1142/S0218126620502047

    Article  Google Scholar 

  18. Fanian F, Kuchaki Rafsanjani M (2019) Cluster-based routing protocols in wireless sensor networks: a survey based on methodology. J Netw Comput Appl 142:111–142. https://doi.org/10.1016/j.jnca.2019.04.021

    Article  Google Scholar 

  19. Arjunan S, Pothula S (2019) A survey on unequal clustering protocols in wireless sensor networks. J King Saud Univ Comput Inf Sci 31:304–317. https://doi.org/10.1016/j.jksuci.2017.03.006

    Article  Google Scholar 

  20. Priyadarshi R, Rawat P, Nath V et al (2020) Three level heterogeneous clustering protocol for wireless sensor network. Microsyst Technol 26:3855–3864. https://doi.org/10.1007/s00542-020-04874-x

    Article  Google Scholar 

  21. Rawat P, Chauhan S (2020) Probability based cluster routing protocol for wireless sensor network. J Ambient Intell Humaniz Comput 1:3. https://doi.org/10.1007/s12652-020-02307-1

    Article  Google Scholar 

  22. Rostami AS, Badkoobe M, Mohanna F et al (2018) Survey on clustering in heterogeneous and homogeneous wireless sensor networks. Springer, US

    Book  Google Scholar 

  23. Singh SK, Kumar P, Singh JP (2017) A survey on successors of LEACH Protocol. IEEE Access 5:4298–4328. https://doi.org/10.1109/ACCESS.2017.2666082

    Article  Google Scholar 

  24. Rawat P, Chauhan S (2018) Energy efficient clustering in heterogeneous environment. In: 2018 second international conference on inventive communication and computational technologies (ICICCT). IEEE, pp 388–392. https://doi.org/10.1109/ICICCT.2018.8473296

  25. Rawat P, Chauhan S (2018) Performance analysis of RN-LEACH protocol over LEACH protocol. Int J Futur Gener Commun Netw 11:1–10. https://doi.org/10.14257/ijfgcn.2018.11.5.01

    Article  Google Scholar 

  26. Attea BA, Khalil EA (2012) A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Appl Soft Comput J 12:1950–1957. https://doi.org/10.1016/j.asoc.2011.04.007

    Article  Google Scholar 

  27. Edla DR, Kongara MC, Cheruku R (2019) SCE-PSO based clustering approach for load balancing of gateways in wireless sensor networks. Wirel Netw 25:1067–1081. https://doi.org/10.1007/s11276-018-1679-2

    Article  Google Scholar 

  28. Wang J, Cao Y, Li B et al (2017) Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Futur Gener Comput Syst 76:452–457. https://doi.org/10.1016/j.future.2016.08.004

    Article  Google Scholar 

  29. Yadav A, Kumar S, Vijendra S (2018) Network life time analysis of WSNs using particle swarm optimization. In: Procedia Computer Science. Elsevier BV, pp 805–815. https://doi.org/10.1016/j.procs.2018.05.092

  30. Anand V, Pandey S (2020) New approach of GA–PSO-based clustering and routing in wireless sensor networks. Int J Commun Syst 33:e4571. https://doi.org/10.1002/dac.4571

    Article  Google Scholar 

  31. Datta A, Nandakumar S (2017) A survey on bio inspired meta heuristic based clustering protocols for wireless sensor networks. IOP Conf Ser Mater Sci Eng. https://doi.org/10.1088/1757-899X/263/5/052026

    Article  Google Scholar 

  32. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks. pp 1942–1948 vol. 4

  33. Rao PCS, Jana PK, Banka H (2017) A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wirel Netw 23:2005–2020. https://doi.org/10.1007/s11276-016-1270-7

    Article  Google Scholar 

  34. Mehta D, Saxena S (2020) MCH-EOR: multi-objective cluster head based energy-aware optimized routing algorithm in wireless sensor networks. Sustain Comput Informatics Syst 28:100406. https://doi.org/10.1016/j.suscom.2020.100406

    Article  Google Scholar 

  35. Azharuddin M, Jana PK (2017) PSO-based approach for energy-efficient and energy-balanced routing and clustering in wireless sensor networks. Soft Comput 21:6825–6839. https://doi.org/10.1007/s00500-016-2234-7

    Article  Google Scholar 

  36. Preethiya T, Muthukumar A, Durairaj S (2020) Double cluster head heterogeneous clustering for optimization in hybrid wireless sensor network. Wirel Pers Commun 110:1751–1768. https://doi.org/10.1007/s11277-019-06810-3

    Article  Google Scholar 

  37. Arikumar KS, Natarajan V, Satapathy SC (2020) EELTM: an energy efficient LifeTime maximization approach for WSN by PSO and fuzzy-based unequal clustering. Arab J Sci Eng. https://doi.org/10.1007/s13369-020-04616-1

    Article  Google Scholar 

  38. Edla DR, Kongara MC, Cheruku R (2019) A PSO based routing with novel fitness function for improving lifetime of WSNs. Wirel Pers Commun 104:73–89. https://doi.org/10.1007/s11277-018-6009-6

    Article  Google Scholar 

  39. Zahedi ZM, Akbari R, Shokouhifar M et al (2016) Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks. Expert Syst Appl 55:313–328. https://doi.org/10.1016/j.eswa.2016.02.016

    Article  Google Scholar 

  40. Sahoo BM, Amgoth T, Pandey HM (2020) Particle swarm optimization based energy efficient clustering and sink mobility in heterogeneous wireless sensor network. Ad Hoc Netw 106:102237. https://doi.org/10.1016/j.adhoc.2020.102237

    Article  Google Scholar 

  41. Shankar T, Shanmugavel S, Rajesh A (2016) Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm Evol Comput 30:1–10. https://doi.org/10.1016/j.swevo.2016.03.003

    Article  Google Scholar 

  42. Elhabyan RSY, Yagoub MCE (2015) Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network. J Netw Comput Appl 52:116–128. https://doi.org/10.1016/j.jnca.2015.02.004

    Article  Google Scholar 

  43. Darabkh KA, Al-Maaitah NJ, Jafar IF, Khalifeh AF (2018) EA-CRP: a novel energy-aware clustering and routing protocol in wireless sensor networks. Comput Electr Eng 72:702–718. https://doi.org/10.1016/j.compeleceng.2017.11.017

    Article  Google Scholar 

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  1. Department of Computer Science and Engineering, National Institute of Technology, Hamirpur, HP, India

    Piyush Rawat & Siddhartha Chauhan

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  1. Piyush Rawat
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Rawat, P., Chauhan, S. Particle swarm optimization-based energy efficient clustering protocol in wireless sensor network. Neural Comput & Applic 33, 14147–14165 (2021). https://doi.org/10.1007/s00521-021-06059-7

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