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Particle swarm optimization based sleep scheduling and clustering protocol in wireless sensor network

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Abstract

Energy management is considered as a challenging task for applications related to the wireless sensor network. The cluster-based networks are among the most effective solutions for the energy-related issue of the wireless sensor network. In this paper, a clustering protocol based on the sleep scheduling approach named Cluster based Sleep Scheduling Protocol (CSSP) is proposed for the lifetime enhancement of the network. The proposed scheme employs a particle swarm optimization based sleep scheduling technique that uses the remaining energy of nodes, distance to neighbors, and coverage neighbor parameter to choose the active and sleep nodes in the network to minimize the energy expenditure. The proposed scheme uses a probability based cluster head selection process which considers the initial energy and remaining energy of sensor nodes to choose the most energy efficient node for the cluster head job and form clusters with the selected cluster heads. The performance of the proposed scheme is compared with the various existing protocol for the different values of heterogeneity to show the effectiveness of the proposed scheme. The proposed protocol has improved the lifetime of the network by 257%, 172%, 119%, 128%, and 64% as compared to the existing CACP, EDHRP, ECDC, E2DR-MCS, and EBCS protocols. The stability period in the proposed scheme has enhanced by 413%, 240%, 145%, 125%, and 95% as compared to the existing CACP, EDHRP, ECDC, E2DR-MCS, and EBCS protocols.

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References

  1. Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330

    Article  Google Scholar 

  2. Goyal D, Tripathy MR (2012) Routing protocols in wireless sensor networks: a survey. In: 2012 Second International Conference on Advanced Computing & Communication Technologies, pp 474–480

  3. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40(8):102–105

    Article  Google Scholar 

  4. Hussain MA, Khan P, Kyung Sup K (2009) WSN research activities for military application. In: 2009 11th International Conference on Advanced Communication Technology 1:271–274

  5. Mainwaring A, Culler D, Polastre J, Szewczyk R, Anderson J (2002) Wireless sensor networks for habitat monitoring. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications - WSNA’02, p. 88

  6. Ali SM, Sattar SA, Rao DS (2019) Wireless sensor networks routing design issues: a survey. Int J Comput Appl 178(26):975–8887

  7. Farsi M, Elhosseini MA, Badawy M, Ali HA, Eldin HZ (2019) Deployment techniques in wireless sensor networks, coverage and connectivity: a survey. IEEE Access 7:28940–28954

  8. Priyadarshi R, Rawat P, Nath V, Acharya B, Shylashree N (2020) Three level heterogeneous clustering protocol for wireless sensor network. Microsyst Technol 26(12):3855–3864

    Article  Google Scholar 

  9. Priyadarshi R, Rawat P, Nath V (2019) Energy dependent cluster formation in heterogeneous wireless sensor network. Microsyst Technol 25(6):2313–2321

    Article  Google Scholar 

  10. 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, pp 58–62

  11. Rawat P, Chauhan S, Priyadarshi R (2020) A novel heterogeneous clustering protocol for lifetime maximization of wireless sensor network. Wirel Pers Commun 2020 1172 117(2):825–841

  12. Rawat P, Chauhan S (2021) A novel cluster head selection and data aggregation protocol for heterogeneous wireless sensor network. Arab J Sci Eng 2021:1–16

    Google Scholar 

  13. Kim B-S, Park H, Kim KH, Godfrey D, Kim K-I (2017) A survey on real-time communications in wireless sensor networks. Wirel Commun Mob Comput 2017:1–14

    Google Scholar 

  14. Rawat P, Chauhan S (2021) Clustering protocols in wireless sensor network: a survey, classification, issues, and future directions. Comput Sci Rev 40:100396

  15. Rawat P, Chauhan S (2021) A survey on clustering protocols in wireless sensor network: Taxonomy, comparison, and future scope. J Ambient Intell Humaniz Comput 1:1–47

  16. Rawat P, Chauhan S (2020) Probability based cluster routing protocol for wireless sensor network. J Ambient Intell Humaniz Comput 1:3

    Google Scholar 

  17. Elhabyan R, Shi W, St-Hilaire M (2019) Coverage protocols for wireless sensor networks: Review and future directions. J Commun Netw 21(1):45–60

    Article  Google Scholar 

  18. More A, Raisinghani V (2017) A survey on energy efficient coverage protocols in wireless sensor networks. J King Saud Univ - Comput Inf Sci 29(4):428–448

  19. Tripathi A, Gupta HP, Dutta T, Mishra R, Shukla KK, Jit S (2018) Coverage and connectivity in WSNs: a survey, research issues and challenges. IEEE Access 6:26971–26992

    Article  Google Scholar 

  20. Priyanka BN, Jayaparvathy R, Divyabharathi D, Divyabharathi D (2022) Efficient and dynamic cluster head selection for improving network lifetime in WSN using whale optimization algorithm. Wirel Pers Commun 1–15

  21. Yadav RK, Mahapatra RP (2022) Hybrid metaheuristic algorithm for optimal cluster head selection in wireless sensor network. Pervasive Mob Comput 79:101504

  22. Rawat P, Chauhan S (2021) Performance analysis of RNC clustering protocol in wireless sensor network. Int J Sens Wirel Commun Control 10(6):957–966

  23. Singh SK, Kumar P, Singh JP (2017) A survey on successors of LEACH protocol. IEEE Access 5:4298–4328

    Article  Google Scholar 

  24. Arjunan S, Pothula S (2019) A survey on unequal clustering protocols in wireless sensor networks. J King Saud Univ - Comput Inf Sci 31(3):304–317

  25. Rawat P, Chauhan S (2018) Performance analysis of RN-LEACH protocol over LEACH protocol. Int J Futur Gener Commun Netw 11(5):1–10

    Google Scholar 

  26. Gherbi C, Aliouat Z, Benmohammed M (2017) A survey on clustering routing protocols in wireless sensor networks. Sens Rev 37(1):12–25. Emerald Group Publishing Ltd

  27. Yu C, Guo W, Chen G (2012) Energy-balanced sleep scheduling based on particle swarm optimization in wireless sensor network. Proc 2012 IEEE 26th Int Parallel Distrib Process Symp Work IPDPSW 2012 1249–1255

  28. Wang B, Lim HB, Ma D (2012) A coverage-aware clustering protocol for wireless sensor networks. Comput Netw 56(5):1599–1611

    Article  Google Scholar 

  29. Faheem M, Abbas MZ, Tuna G, Gungor VC (2015) EDHRP: Energy efficient event driven hybrid routing protocol for densely deployed wireless sensor networks. J Netw Comput Appl 58:309–326

    Article  Google Scholar 

  30. Wan R, Xiong N, Loc NT (2018) An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks. Human-Centric Comput Inf Sci 8(1):1–22

  31. Danratchadakorn C, Pornavalai C (2015) Coverage maximization with sleep scheduling for wireless sensor network. ECTI-CON 2015 - 2015 12th Int Conf Electr Eng Comput Telecommun Inf Technol

  32. Guruprakash B, Balasubramanian C, Sukumar R (2019) An approach by adopting multi-objective clustering and data collection along with node sleep scheduling for energy efficient and delay aware WSN. Peer-to-Peer Netw Appl 13(1):304–319

  33. Radhika S, Rangarajan P (2021) Fuzzy based sleep scheduling algorithm with machine learning techniques to enhance energy efficiency in wireless sensor networks. Wirel Pers Commun 118(4):3025–3044

  34. Tripathi Y, Prakash A, Tripathi R (2021) A sleep scheduling based cooperative data transmission for wireless sensor network. https://doi.org/10.1080/00207217.2021.1914193

  35. Tanwar A, Sharma AK, Pandey RVS (2020) Fractional-grasshopper optimization algorithm for the sensor activation control in wireless sensor networks. Wirel Pers Commun 113(1):399–422

  36. Dong L, Tao H, Doherty W, Young M (2015) A sleep scheduling mechanism with PSO collaborative evolution for wireless sensor networks. Int J Distrib Sens Netw 2015

  37. Osamy W, Salim A, Khedr AM (2020) An information entropy based-clustering algorithm for heterogeneous wireless sensor networks. Wirel Netw 26(3):1869–1886

    Article  Google Scholar 

  38. Chaturvedi P, Daniel AK (2017) A novel sleep/wake protocol for target coverage based on trust evaluation for a clustered wireless sensor network. Int J Mob Netw Des Innov 7(3–4):199–209

    Google Scholar 

  39. Brindha G, Ezhilarasi P (2020) Energy efficient momento based dynamic scheduling for lifetime maximization in WSN. J Ambient Intell Humaniz Comput 12(6):5865–5875

  40. Naranjo PGV, Shojafar M, Mostafaei H, Pooranian Z, Baccarelli E (2017) P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks. J Supercomput 73(2):733–755

    Article  Google Scholar 

  41. Pooranian Z, Barati A, Movaghar A (2011) Queen-bee algorithm for energy efficient clusters in wireless sensor networks. World Acad Sci Eng Technol 73(1):1080–1083

    Google Scholar 

  42. Zhang J, Chen J (2019) An adaptive clustering algorithm for dynamic heterogeneous wireless sensor networks. Wirel Netw 25(1):455–470

    Article  Google Scholar 

  43. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc ICNN’95 – Int Conf Neural Netw 4:1942–1948

  44. Wang J, Cao Y, Li B, Kim H, Lee S (2017) Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Futur Gener Comput Syst 76:452–457

    Article  Google Scholar 

  45. 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 

  46. Edla DR, Kongara MC, Cheruku R (2019) A PSO based routing with novel fitness function for improving lifetime of WSNs. Wirel Pers Commun 104(1):73–89

    Article  Google Scholar 

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Authors and Affiliations

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

    Piyush Rawat & Siddhartha Chauhan

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  1. Piyush Rawat
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  2. Siddhartha Chauhan
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Correspondence to Piyush Rawat.

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Rawat, P., Chauhan, S. Particle swarm optimization based sleep scheduling and clustering protocol in wireless sensor network. Peer-to-Peer Netw. Appl. 15, 1417–1436 (2022). https://doi.org/10.1007/s12083-022-01307-6

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