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Competitive swarm optimization based unequal clustering and routing algorithms (CSO-UCRA) for wireless sensor networks

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Abstract

Cluster based routing approaches have been researched extensively for saving energy of wireless sensor networks (WSNs). In a cluster based routing mechanism, cluster heads (CHs) cooperate mutually to forward their data to the base station (BS) through multi-hop fashion. Due to this process, CHs near to the BS loaded with huge relay traffic and tend to die quickly, which causes partition of the network is popularly known as a hot-spot problem. To tackle the hot-spot problem, in this paper, competitive swarm optimization (CSO) based algorithms have been proposed, jointly call these algorithms as CSO-UCRA (CSO based Unequal Clustering and Routing Algorithms). First, the CH selection algorithm has been presented which is based on CSO based technique, next assign the non-CH sensors to CHs based on the derived CHproficiency function. Finally, a CSO based routing algorithm has been presented. Efficient particle encoding schemes and novel fitness functions have been developed for these algorithms. The CSO-UCRA is simulated extensively with varying number of sensor nodes and CHs for various WSN scenarios, and the obtained results are compared with some recent devised algorithms and standard meta-heuristic based algorithm called PSO-UCRA to show the efficiancy in terms of various performance metrics. CSO-UCRA shows decreased energy consumption of 28.48%, 22.55%, 12.92%, and 3.81%, increased network lifetime of 56.92%, 46.02%, 26.2%, and 8.04% and increased data packets received 73%, 52.5%, 20.8%, and 6.18% over EBUC, EAUCF, EPUC and PSO-UCRA respectively.

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References

  1. Abbasi AA, Younis M (2007) A survey on clustering algorithms for wireless sensor networks. Comput Commun 30(14-15):2826–2841

    Article  Google Scholar 

  2. Afsar MM, Tayarani-N MH (2014) Clustering in sensor networks: A literature survey. J Netw Comput App 46:198–226

    Article  Google Scholar 

  3. Afsar MM, Younis M (2014) An energy-and proximity-based unequal clustering algorithm for wireless sensor networks. In: 39th Annual IEEE conference on local computer networks, IEEE, pp 262–269

  4. Akkaya K, Younis M (2005) A survey on routing protocols for wireless sensor networks. Ad hoc Netw 3(3):325–349

    Article  Google Scholar 

  5. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422

    Article  Google Scholar 

  6. Bagci H, Yazici A (2013) An energy aware fuzzy approach to unequal clustering in wireless sensor networks. Appl Soft Comput 13(4):1741–1749

    Article  Google Scholar 

  7. Banka H, Jana PK, et al. (2016) Pso-based multiple-sink placement algorithm for protracting the lifetime of wireless sensor networks. In: Proceedings of the second international conference on computer and communication technologies, Springer, pp 605–616

  8. Bara’a AA, Khalil EA (2012) A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Appl Soft Comput 12 (7):1950–1957

    Article  Google Scholar 

  9. Carrabs F, Cerulli R, D’Ambrosio C, Raiconi A (2016) Extending lifetime through partial coverage and roles allocation in connectivity-constrained sensor networks. IFAC-PapersOnLine 49(12):973–978

    Article  Google Scholar 

  10. Carrabs F, Cerulli R, D’Ambrosio C, Raiconi A (2017) Prolonging lifetime in wireless sensor networks with interference constraints. In: International conference on green, pervasive, and cloud computing, Springer, pp 285–297

  11. Carrabs F, Cerulli R, Gentili M, Raiconi A, et al. (2015) Maximizing lifetime in wireless sensor networks with multiple sensor families. Comput Oper Res 60:121–137

    Article  MathSciNet  Google Scholar 

  12. Carrabs F, Cerulli R, Raiconi A, et al. (2015) A hybrid exact approach for maximizing lifetime in sensor networks with complete and partial coverage constraints. J Netw Comput Appl 58:12–22

    Article  Google Scholar 

  13. Cheng R, Jin Y (2014) A competitive swarm optimizer for large scale optimization. IEEE Trans Cybern 45(2):191–204

    Article  Google Scholar 

  14. Dietrich I, Dressler F (2009) On the lifetime of wireless sensor networks. ACM Trans Sens Netw (TOSN) 5(1):1–39

    Article  Google Scholar 

  15. Guru S, Halgamuge S, Fernando S (2005) Particle swarm optimisers for cluster formation in wireless sensor networks. In: 2005 International conference on intelligent sensors, sensor networks and information processing, IEEE, pp 319–324

  16. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd annual Hawaii international conference on system sciences, IEEE, pp 10–pp

  17. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wireless Commun 1(4):660–670

    Article  Google Scholar 

  18. Jiang CJ, Shi WR, Tang XL, et al. (2010) Energy-balanced unequal clustering protocol for wireless sensor networks. J China Univ Posts Telecommun 17 (4):94–99

    Article  Google Scholar 

  19. Khalil EA, Bara’a AA (2011) Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm Evol Comput 1 (4):195–203

    Article  Google Scholar 

  20. Lee S, Choe H, Park B, Song Y, Kim CK (2011) Luca: An energy-efficient unequal clustering algorithm using location information for wireless sensor networks. Wirel Pers Commun 56(4):715–731

    Article  Google Scholar 

  21. Lindsey S, Raghavendra CS (2002) Pegasis: Power-efficient gathering in sensor information systems. In: Proceedings, IEEE aerospace conference, vol 3. IEEE, pp 3–3

  22. Liu T, Li Q, Liang P (2012) An energy-balancing clustering approach for gradient-based routing in wireless sensor networks. Comput Commun 35 (17):2150–2161

    Article  Google Scholar 

  23. Malathi L, Gnanamurthy R, Chandrasekaran K (2015) Energy efficient data collection through hybrid unequal clustering for wireless sensor networks. Comput Electr Eng 48:358–370

    Article  Google Scholar 

  24. Nayyar A., Le D-N, Nguyen NG (2018) Advances in swarm intelligence for optimizing problems in computer science. CRC Press

  25. Nayyar A, Nguyen NG (2018) Introduction to swarm intelligence. Advances in Swarm Intelligence for Optimizing Problems in Computer Science 53–78

  26. Nayyar A, Singh R (2015) A comprehensive review of simulation tools for wireless sensor networks (WSNs). J Wirel Netw Commun 5(1):19–47

    Google Scholar 

  27. Rao PS, Banka H (2017) Energy efficient clustering algorithms for wireless sensor networks: novel chemical reaction optimization approach. Wirel Netw 23 (2):433–452

    Article  Google Scholar 

  28. Rao PS, Banka H (2017) Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks. Wirel Netw 23(3):759–778

    Article  Google Scholar 

  29. Rao PS, Banka H, Jana PK (2015) Energy efficient clustering for wireless sensor networks: A gravitational search algorithm. In: International conference on swarm, evolutionary, and memetic computing, Springer, pp 247–259

  30. Rao PS, Banka H, Jana PK (2015) A gravitational search algorithm for energy efficient multi-sink placement in wireless sensor networks. In: International conference on swarm, evolutionary, and memetic computing, Springer, pp 222–234

  31. Rao PS, Jana PK, Banka H (2017) A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Netw 23(7):2005–2020

    Article  Google Scholar 

  32. Sabor N, Abo-Zahhad M, Sasaki S, Ahmed SM (2016) An unequal multi-hop balanced immune clustering protocol for wireless sensor networks. Appl Soft Comput 43:372–389

    Article  Google Scholar 

  33. Sharma S, Gupta M, Nayyar A (2014) Combating congestion problem in wireless sensor network using combined dominating set technique

  34. Sharma S, Gupta M, Nayyar A (2014) Review of routing techniques driving wireless sensor networks. Int J Comput Sci Mobile Comput 3(5):112–122

    Google Scholar 

  35. Sharma N, Nayyar A (2014) A comprehensive review of cluster based energy efficient routing protocols for wireless sensor networks. Int J Appl Innov Eng Manag (IJAIEM) 3(1):441–453

    Google Scholar 

  36. Singh S (2019) A sustainable data gathering technique based on nature inspired optimization in wsns. Sustain Comput Inform Syst 24:100354

    Google Scholar 

  37. Singh S (2020) An energy aware clustering and data gathering technique based on nature inspired optimization in WSNs. Peer-to-Peer Networking and Applications 13(5):1–18

    Article  Google Scholar 

  38. Song M, Zhao CL (2011) Unequal clustering algorithm for wsn based on fuzzy logic and improved aco. J China Univ Posts Telecommun 18(6):89–97

    Article  Google Scholar 

  39. Soro S, Heinzelman WB (2005) Prolonging the lifetime of wireless sensor networks via unequal clustering. In: 19th IEEE international parallel and distributed processing symposium, IEEE, pp 8–pp

  40. Verma S, Sood N, Sharma AK (2019) Genetic algorithm-based optimized cluster head selection for single and multiple data sinks in heterogeneous wireless sensor network. Appl Soft Comput 85:105788

    Article  Google Scholar 

  41. Xu J, Liu W, Lang F, Zhang Y, Wang C (2010) Distance measurement model based on rssi in wsn. Wirel Sens Netw 2(8):606

    Article  Google Scholar 

  42. Younis O, Fahmy S (2004) Heed: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans Mobile Comput 3 (4):366–379

    Article  Google Scholar 

  43. Yu J, Qi Y, Wang G, Guo Q, Gu X (2011) An energy-aware distributed unequal clustering protocol for wireless sensor networks. Int J Distrib Sens Netw 7(1):202145

    Article  Google Scholar 

  44. Zeng B, Dong Y (2016) An improved harmony search based energy-efficient routing algorithm for wireless sensor networks. Appl Soft Comput 41:135–147

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

    P. C. Srinivasa Rao & G. Siva Nageswara Rao

  2. School of Computing Science and Engineering, VIT Bhopal University, Bhopal, India

    Praveen Lalwani

  3. Computer Science and Engineering, IIT(ISM) Dhanbad, Dhanbad, India

    Haider Banka

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  1. P. C. Srinivasa Rao
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  2. Praveen Lalwani
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  3. Haider Banka
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  4. G. Siva Nageswara Rao
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Correspondence to Praveen Lalwani.

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Rao, P.C.S., Lalwani, P., Banka, H. et al. Competitive swarm optimization based unequal clustering and routing algorithms (CSO-UCRA) for wireless sensor networks. Multimed Tools Appl 80, 26093–26119 (2021). https://doi.org/10.1007/s11042-021-10901-4

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  • DOI: https://doi.org/10.1007/s11042-021-10901-4

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