Abstract
Energy Efficiency now a day’s becomes a main issues in Wireless sensor Network. Hierarchical Clustering with multipath routing protocol technique is the important to improve packet over head, network lifetime, QOS, and power consumption. There are many such methodssuggested to Improve the Energy efficiency of whole WSN region. Out of these protocols the ad-hoc On demand Distance Vector (AODV) routing protocol is very suitable, as it has more scalable and less overhead. This AODV protocol has two operations to find and maintain routes i.e. Path discovery and path maintenance. By doing the Clustering approach the data packet is shared among members of different clusters by the help of Cluster Head, which ultimately saves energy. Hence Hierarchical clustering algorithm is used in this approach along with Hybrid Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) algorithm.The GA and PSO algorithm creates a hierarchy of cluster heads. The energy saving scheme increases with number of level increase in the Cluster presents in WSN. Therefore Hierarchical Clustering with Hybrid GA and PSO (HC-HGAPSO) methodology performed better Throughput, Network lifetime, and Residual Energy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ramluckun N, Basoo V (2018) Energy-efficient chain-cluster based intelligent routing technique for wireless sensor networks. Appl Comput Inf
Wang J, Cao Y, Li B, Kim HJ, Lee S. (2017) Particle swarm optimization based clustering algorithm with mobile sink for WSNs. Future Gener Comput Syst, 452–457
Barekatain B, Dehghani S, Pourezaferani M (2015) An energy-aware routing protocol for wireless sensor networks based on new combination of genetic algorithm & k-mean .Procedia Comput Sci 72:552–560
Gupta SK, Kulia P, Jana PK (2016) Energy efficient multipath routing for wireless sensor networks: a genetic algorithm approach. In: 2016 International conference on advance in computing, communications and informatics (ICACCI). IEEE, pp 1735–1740
Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy efficient communication protocol for wireless microsensor networks. In: Proceedings of 33rd annual Hawaii international conference on system sciences, pp 10–19
Heinzelman WR, Chandrakasan A, Balakrishnan H (2002) An application-specific protocol architecture for wireless micro sensor networks. Wireless Commun 660–670
Dogar AB, Saha GA, Farooq MO (2010) MR-LEACH: multi-hop routing with low energy adaptive clustering hierarchy. In: Fourth international conference on sensor technologies and applications (SENSORCOMM), pp 262–268
Younis O, Fahmy S (2004) A hybrid energy-efficient, distributed clustering approach for ad-hoc sensor networks. EEE Trans Mobile Comput 366–379
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
Mohamed E, Elsherif Samiha M, Elsayed WM (2017) An enhancement approach for reducing the energy consumption in wireless sensor networks. J King Saud Univ Comput Inform Sci. ISSN 1319-1578
Elhabyan RS, Yagoub MC (2015) Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network. J Netw Comput Appl 52:116–128
Kuila P, Jana PK (2014) Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Eng Appl Artif Intell 33:127–140
Gao F, Luo W, Ma X (2019) Energy constrained clustering routing method based on particle swarm optimization. Cluster Comput 22(3):7629–7635
Aziz L, Raghay S, Aznaoui H, Jamali A (2016) A new approach based on a genetic algorithm and an agent cluster head to optimize energy in wireless sensor networks. In: 2016 International conference on information technology for organizations development (IT4OD), pp 1–5
Yao G-S, Dong Z-X, Wen W-M, Ren Q (2016) A routing optimization strategy for wireless sensor networks based on improved genetic algorithm. J Appl Sci Eng Technol 19:221–228
Wang J, Gao Y, Liu W, Sangaiah AK, Kim HJ (2019) An improved routing schema with special clustering using PSO algorithm for heterogeneous wireless sensor network. Sensors 19(3):671
Sambo DW, Yenke BO, Förster A, Dayang P (2019) Optimized clustering algorithms for large wireless sensor networks: a review. Sensors 19(2):322
Roy C, Das DK, Srivastava A (2019) Particle swarm optimization based cost optimization for demand side management in smart grid. In: Proceedings of the 2019 international conference on electrical, electronics and computer engineering (UPCON). IEEE, pp 1–6
Kennedy J, Eberhart R (1995) Particle swarm optimization (PSO). In: Proceedings of the IEEE international conference on neural networks, Perth, Australia, pp 1942–1948
Park JB, Jeong YW, Shin JR, Lee KY (2009) An improved particle swarm optimization for nonconvex economic dispatch problems. IEEE Trans Power Syst 25(1):156–166
Logenthiran T, Srinivasan D, Phyu E (2015) Particle swarm optimization for demand side management in smart grid. In: Proceedings of the 2015 IEEE innovative smart grid technologies-Asia (ISGT ASIA). IEEE, pp 1–6
Goldberg D (2014) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA
Konak A, Coit DW, Smith AE (2006) Multi-objective optimization using genetic algorithms: a tutorial. Reliab Eng Syst Saf 91(9):992–1007
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Patra, B.K., Mishra, S., Patra, S.K. (2022). Energy Efficient Clustering and Optimal Multipath Routing Using Hybrid Metaheuristic Protocol in Wireless Sensor Network. In: Kaiser, M.S., Bandyopadhyay, A., Ray, K., Singh, R., Nagar, V. (eds) Proceedings of Trends in Electronics and Health Informatics. Lecture Notes in Networks and Systems, vol 376. Springer, Singapore. https://doi.org/10.1007/978-981-16-8826-3_47
Download citation
DOI: https://doi.org/10.1007/978-981-16-8826-3_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8825-6
Online ISBN: 978-981-16-8826-3
eBook Packages: EngineeringEngineering (R0)