Abstract
The clustering technique is one of the best techniques to consume less energy of sensor nodes and enhance the life of nodes in wireless sensor networks (WSNs). In cluster-based WSNs, cluster heads (CHs) deplete maximum power than its member nodes. Hence, the selection of CHs should be optimized. In this paper, the global optimization technique bacteria forging optimization (BFO) is used for the selection of CHs and proposed an energy-efficient clustering protocol bacteria foraging optimization-based clustering protocol (BFOCP). This protocol is tested extensively on various scenarios of WSNs. The simulated results are compared with existing protocols to prove efficiency of this protocol. Finally, the proposed protocol is proved to be suitable for the improvement of lifetime of WSN nodes.
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References
Mishra TK, Dass AK, Panda SK (2018) Enhanced path planning model for anchor-free distributed localization in wireless sensor networks. In: 5th IEEE international conference on parallel, distributed and grid computing (PDGC-2018), pp. 430–435
Singh M, Bhoi SK, Panda SK (2020) Geometric least square curve fitting method for localization of wireless sensor network. Ad Hoc Networks 116:102456
Memon I, Jamro DA, Mangi FA, Basti MA, Memon MH (2013) Source localization wireless sensor network using time difference of arrivals (TDOA). Int J Sci Eng Res 4(7):1406
Pantazis NA, Nikolidakis SA, Vergados DD (2013) Energy efficient routing protocols in wireless sensor networks: a survey. IEEE Commun Surv Tutorials 15(2):551–591
Afsar MM, Tayarani NM (2014) Clustering in sensor networks: a literature survey. J Network Comput Appl 46:198–226
Memon I, Hussian I, Akhtar R, Chen G (2015) Enhanced privacy and authentication: an efficient and secure anonymous communication for location based service using asymmetric cryptography scheme. Wireless Pers Commun 84:1487–1508
Bhoi SK, Panda SK Khilar PM (2012) A density-based clustering paradigm to detect faults in wireless sensor network. In: Proceedings of ICAdC—2012, AISC 174, pp 865–871
Abbasi AH, Mohamad YA (2007) A survey on clustering algorithms for wireless sensor networks. Comput Commun 30(14):2826–2841
Afsar MM, Tayarani N, Mohammad H (2014) Clustering in sensor networks: a literature survey. J Network Comput Appl 46:198–226
Liu XA (2012) Survey on clustering routing protocols in wireless sensor networks. Sensors 12(8):11113–11153. https://doi.org/10.3390/s120811113
Hu S, Han J, Wei X, Che Z (2015) A multi-hop heterogeneous cluster-based optimization algorithm for wireless sensor networks. Wireless Netw 21(1):57–65
Liu Y, PassinoKM (2002) Biomimicry of Social foraging Bacteria for Distributed optimization Models principles and Emergent Behaviors. Springer
Heinzelman WR, Chandrasekasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Hawaii international conference on system science, vol I, no c.p. 10
Lindsey S, Raghavendra CS (2002) PEGASIS: power efficient gathering in sensor information systems. In: Proceedings of IEEE aerospace conference, vol 3, pp 1125–1130
Younis O, Fahmy S (2004) HEED: Hybrid energy efficient distributed clustering approach for Ad Hoc sensor networks. IEEE Trans Mob Comput 3(4):366–379
Bandyopadhyay S, Coyle EJ (2003) An energy efficient hierarchical clustering algorithm for wireless sensor networks. In: IEEE INFOCOMM, vol 3, pp 1713–1723
Yao Y, Cao Q, Vasilakos AV (2013) EDAL: an energy-efficient, delay-aware, and lifetime-balancing data collection protocol for wireless sensor networks. In: Mobile ad-hoc and sensor systems (MASS), 2013 IEEE 10th international conference on, pp 182–190
Bari A, Jaekel A, Bandyopadhyay S (2008) Clustering strategies for improving the lifetime of two-tiered sensor net- works. Comput Commun 31(14):3451–3459
Low CP, Fang C, Ng JM, Ang YH (2008) Efficient load-balanced clustering algorithms for wireless sensor networks. Comput Commun 31(4):750–759
Kuila P, Gupta SK, Jana PK (2013) A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm Evol Comput 12:48–56
Rao PCS, Banka H, Jana PK (2015) 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, India, pp 605–616
Li M, Li Z, Vasilakos AV (2013) A survey on topology control in wireless sensor networks: taxonomy, comparative study and open issues. Proc IEEE 101(12):2538–2557
Dvir A, Vasilakos AV (2011) Backpressure-based routing protocol for DTNs. ACM SIGCOMM Comput Commun Rev 41(4):405–406
Jing Q, Vasilakos AV, Wan J, Lu J, Qiu D (2014) Security of the internet of things: perspectives and challenges. Wireless Netw 20(8):2481–2501
Yan Z, Zhang P, Vasilakos AV (2014) A survey on trust management for internet of things. J Network Comput Appl 42:120–134
Smaragdakis G, Matta I, Bestavros A (2004) SEP: a stable election protocol for clustered heterogeneous wireless sensor networks. In: second international workshop on sensor and actor network protocols and applications (SANPA 2004), pp 1–11
Naik LJ, Sudershan C (2015) Extended stable election protocol for increasing lifetime of the WSN Mannepalli Sreehari, no. January, pp 39–42
Sharma R, Vashisht V, Singh U (2019) Nature inspired algorithms for energy efficient lustering in wireless sensor networks. In: 9th international conference on cloud computing, data science & engineering (Confluence). IEEE Conference. pp 365–370
Bhavan A, Adiga HP, Chandana N, Keerthan AB, Sandeep E (2015) B-LEACH: a clustering protocol for wireless sensor networks based on bacterial forging algorithm. J Wireless Sens Netw 2:1–0008
Khan S, Lloret J, Macias-L´opez, E (2015) Bio-inspired mechanisms in wireless sensor networks. Int J Distrib Sens Networks 11:2
Tillet J, Rao R, Sachin F (2002) Cluster head identification in adhoc sensor networks using particle swarm optimization. In: IEEE international conference on personal wireless communications, pp 201–205
Abbas K, Abedini SM, Faraneh Z, Al-Haddad SAR (2013) Cluster head selection using fuzzy logic and chaotic based genetic algorithm in wireless sensor network. J Basic Appl Sci Res 3(3):694–703
Enan A, Bara A, Attea A (2011) Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm Evol Comput 1(4):195–203
Latiff NMA, Tsemenidis CC, Sheriff BS (2007) Energy-aware clustering for wireless sensor networks using particle swarm optimization. In: Proceedings of 18th annual IEEE international symposium on personal, indoor and mobile radio communications, pp 1–5
Buddha S, Lobiyal DK (2012) A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human-Centric Comput Inform Sci 2(1):2–13
Kuila P, Jana PK (2014) A novel differential evolution based clustering algorithm for wireless sensor networks. Appl Soft Comput 25:414–425
Dhiman V (2013) BIO inspired hybrid routing protocol for wireless sensor networks, www.Ijaret.Org, 1(Iv):33–36
Xu J, Liu W, Lang F, Zhan Y, Wang C (2010) Distance measurement model based on RSSI in WSN. Wireless Sens Networks 2(8):606–611
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Dora, S.S., Swain, P.K. (2022). Bacterial Forging Optimization-Based Clustering Protocol for Wireless Sensor Networks. In: Rout, R.R., Ghosh, S.K., Jana, P.K., Tripathy, A.K., Sahoo, J.P., Li, KC. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 427. Springer, Singapore. https://doi.org/10.1007/978-981-19-1018-0_12
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