Abstract
In a cluster-based Wireless Sensor Networks (WSN) , dividing the network into clusters and choosing an efficient Cluster Head (CH) is a big issue. The selection of CH is a very challenging task, and it affects the energy consumption of the network and also the lifetime of sensors and ultimately network lifetime. This chapter presents a new approach for CH selection based on Artificial Bee Colony (ABC) optimization. This ABC optimization is based upon the remaining energy, intra-cluster distance , and distance from the sink station. The fitness function for ABC is calculated based on three parameters, i.e., residual energy ; distance from the sink station; and intra-cluster distance. We optimized the fitness function using ABC optimization. The objective of optimizing the fitness function is to select an optimal CH for each cluster which reduces the energy consumption of the WSN. The proposed model is analyzed through extensive experiments and the outcomes are compared with some famous existing approaches.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu X (2012) A survey on clustering routing protocols in wireless sensor networks. Sensors 12:11113–11153
Haque M, Ahmad T, Imran M (2017) Hierarchical routing protocols for wireless sensor networks: a comparative survey. In: Proceedings of the IEEE international conference on wireless communications, signal processing and networking (WiSPNET), pp 2115–2119
Abbasi AA, Younis M (2007) A survey on clustering algorithms for wireless sensor networks. Comput Commun 30:2826–2841
Mishra H, Kumar V, Shibu S (2015) Cluster based energy efficient routing protocol for wireless sensor network. Eng Univ Sci Res Manag 7(1):1–5
Jiang C, Yuan D, Zhao Y (2009) Towards clustering algorithms in wireless sensor networks: a survey. In: IEEE wireless communications and networking conference, Budapest, Hungary pp 1–6
Boyinbode O, Le H, Mbogho A, Takizawa M, Poliah R (2010) A survey on clustering algorithms for wireless sensor networks. In: 13th IEEE international conference on network-based information system, pp 358–364
Wei C, Yang J, Gao J, Zhang Z (2011) Cluster-based routing protocols in wireless sensor networks: a survey. In: 2011 international conference on computer science and network technology, Harbin, China, pp 1659–1663
Marhoon HA, Mahmuddin M, Nor SA (2015) Chain-based routing protocols in wireless sensor networks: a survey. ARPN J Eng Appl Sci 10(3):1389–1398
Naeimi S, Ghafghazi H, Chow CO, Ishi H (2012) A survey on the taxonomy of cluster-based routing protocols for homogeneous wireless sensor networks. Sensors 12(6):7350–7409
Xu X, Ansari R, Khokhar A, Vasilakos AV (2015) Hierarchical data aggregation using compressive sensing (HDACS) in WSNs. ACM Trans Sensor Netw (TOSN) 11(3)
Haque M, Ahmad T, Imran M (2017) Review of hierarchical routing protocols for wireless sensor networks. In: Hu Y-C et al (eds) Intelligent communication and computational technologies, Lecture notes in networks and systems, vol 19. Springer, Berlin, pp 237–246
Ran G, Zhang H, Gong S (2010) Improving on LEACH protocol of wireless sensor networks using fuzzy logic. J Inf Comput Sci 7(3):767–775
Hani RMB, Ijjeh A (2013) A survey on LEACH-based energy aware protocols for wireless sensor networks. J Commun 8(3):192–205
Pal V, Yogita, Singh G, Yadav RP (2015) Cluster head selection optimization based on genetic algorithm to prolong lifetime of wireless sensor networks. Procedia Comput Sci 57:1417–1423
Gupta V, Sharma SK (2015) Cluster head selection using modified ACO. In: Proceedings of fourth international conference on soft computing for problem solving, advances in intelligent systems and computing, pp 11–20
Das S, Wagh S (2015) Prolonging the lifetime of the wireless sensor network based on blending of genetic algorithm and ant colony optimization. J Green Eng 4:245–260
Singh B, Lobiyal DK (2012) A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human Centric Comput Inf Sci 2(1):2–13
Rao PCS, Banka H, Jana PK (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, pp 605–616
Guru SM, Halgamuge SK, Fernando S (2005) Particle swarm optimizers for cluster formation in wireless sensor networks. In: Proceedings of the IEEE international conference on intelligent sensors, sensor networks and information processing (ISSNIP’05), pp 319–324. Melbourne, Australia, Dec (2005)
Lati NMA, Tsimenidis CC, Sharif BS (2007) Energy-aware clustering for wireless sensor networks using particle swarm optimization. In: Proceedings of 18th IEEE international symposium on personal, indoor and mobile radio communications, pp 1–5
Davidovic T, Teodorovic D, Selmic M (2015) Bee colony optimization part I: the algorithm overview. Yugoslav J Oper Res 25(1):33–36
Gao W, Liu S, Huang L (2012) A global best artificial bee colony algorithm for global optimization. J Comput Appl Math 236(11):2741–2753
Bhardwaj M, Chandrakasan AP (2001) Upper bounds on the lifetime of wireless sensor networks. In: Proceedings of IEEE international conference on communications (ICC), vol. 3, pp 785–790
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Ahmad, T., Haque, M., Khan, A.M. (2019). An Energy-Efficient Cluster Head Selection Using Artificial Bees Colony Optimization for Wireless Sensor Networks. In: Shandilya, S., Shandilya, S., Nagar, A. (eds) Advances in Nature-Inspired Computing and Applications. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-96451-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-96451-5_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96450-8
Online ISBN: 978-3-319-96451-5
eBook Packages: EngineeringEngineering (R0)