A Novel Fuzzy Enhanced Black Widow Spider Optimization for Energy Efficient Cluster Communication by Optimal Cluster Head Selection in WSN

International Journal of Electrical and Electronics Engineering

© 2022 by SSRG - IJEEE Journal

Volume 9 Issue 12

Year of Publication : 2022

Authors : P. Vijitha Devi, K. Kavitha

10.14445/23488379/IJEEE-V9I12P105

How to Cite?

P. Vijitha Devi, K. Kavitha, "A Novel Fuzzy Enhanced Black Widow Spider Optimization for Energy Efficient Cluster Communication by Optimal Cluster Head Selection in WSN," SSRG International Journal of Electrical and Electronics Engineering, vol. 9,  no. 12, pp. 49-58, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I12P105

Abstract:

Wireless sensor networks' substantial growth and momentous potential have expanded their application in realworld scenarios. However, the energy-constrained characteristics of sensor nodes create various network functioning issues. To deal with such shortcomings, the energy efficient clustering approach is required. The clustering method is the most significant and effective technique for optimising sensor nodes. Although numerous clustering approaches for determining ideal CH in the network area exist, it requires effective solutions to enhance wireless network performance. Therefore, this paper develops a novel Fuzzy Enhanced Black Widow Spider (FEBWS) method that promotes effective Communication between inter and intra clusters by optimal selection of cluster heads. The most-optimal cluster head is selected from the cluster groups by the proposed FEBWS algorithm uses the fuzzy logic system with an improved black widow spider optimisation algorithm considering energy, delay, and distance parameters. The efficiency of the proposed FEBWS algorithm is investigated by relating its performance with the existing techniques. The proposed FEBWS algorithm achieves an enhanced performance rate, especially less energy consumption and high network lifetime, than other state-of-the-art techniques.

Keywords:

Cluster head selection, Fuzzy logic system, Improved black widow spider optimisation algorithm, Energy consumption.

References:

[1] Zhenhua Zhou, and Yugang Niu, “An Energy Efficient Clustering Algorithm Based on Annulus Division Applied in Wireless Sensor Networks,” Wireless Personal Communications, vol. 115, no. 3, pp. 2229-2241, 2020. Crossref, https://doi.org/10.1007/s11277-020- 07679-3
[2] Sepehr Ebrahimi Mood, and Mohammad Masoud Javidi, “Energy-Efficient Clustering Method for Wireless Sensor Networks Using Modified Gravitational Search Algorithm,” Evolving Systems, vol. 11, no. 4, pp. 575-587, 2020. Crossref, https://doi.org/10.1007/s12530-019-09264-x
[3] MohammedAl-Shalab et al., “Energy Efficient Multi-Hop Path in Wireless Sensor Networks Using an Enhanced Genetic Algorithm,” Information Sciences, vol. 500, pp. 259-273, 2019. Crossref, https://doi.org/10.1016/j.ins.2019.05.094
[4] M.Preethi, and S.Vibu, “Energy Efficient Cluster Based Optimizing Broadcast Mechanism for Adhoc Networks,” International Journal of P2P Network Trends and Technology, vol. 6, no. 5, pp. 11-15, 2016.
[5] Pandiyaraju V et al., “An Energy Efficient Routing Algorithm for WSNs Using Intelligent Fuzzy Rules in Precision Agriculture,” Wireless Personal Communications, vol. 112, no. 1, pp. 243-259, 2020.
[6] Dr. E. Gajendran, Dr. J. Vignesh, and Dr. S.R. Boselin Prabhu, “Prolonging Network Lifetime in Wireless Sensor Networks Using Enhanced Integrated Clustering,” International Journal of P2P Network Trends and Technology, vol. 7, no. 4, pp. 6-11, 2017. Crossref, https://doi.org/10.14445/22492615/IJPTT-V33P402
[7] YuxinLiu et al., “DDC: Dynamic Duty Cycle for Improving Delay and Energy Efficiency in Wireless Sensor Networks,” Journal of Network and Computer Applications, vol. 131, pp. 16-27, 2019. Crossref, https://doi.org/10.1016/j.jnca.2019.01.022
[8] Poonguzhali P.K, and Ananthamoorthy N.P, “Improved Energy Efficient WSN Using ACO Based HSA for Optimal Cluster Head Selection,” Peer-to-Peer Networking and Applications, vol. 13, no. 4, pp. 1102-1108, 2020. Crossref, https://doi.org/10.1007/s12083- 019-00814-3
[9] Kamana Singh, and Ankur Goyal, "Implementation of Modified CRT Algorithm for Packet Routing Evaluation to Improved Energy Saving and Reliability in Wireless Sensor Networks,” International Journal of Computer & Organization Trends, vol. 4, no. 5, pp. 25- 30, 2014. Crossref, http://doi.org/10.14445/22492593/IJCOT-V12P307 
[10] Nader Ajmi et al., “MWCSGA-Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network,” Sensors, vol. 21, no. 3, p. 791, 2021. Crossref, https://doi.org/10.3390/s21030791
[11] Sachi Nandan Mohanty et al., “Deep Learning with LSTM Based Distributed Data Mining Model for Energy Efficient Wireless Sensor Networks,” Physical Communication, vol. 40, p. 101097, 2020. Crossref, https://doi.org/10.1016/j.phycom.2020.101097
[12] Astanginiselvaraj, and Dr. C. Senthilkumar, "A Lion Optimization Based Energy Efficient Clustering in WSN," SSRG International Journal of Electronics and Communication Engineering, vol. 8, no. 4, pp. 18-21, 2021. Crossref, https://doi.org/10.14445/23488549/IJECE-V8I4P104
[13] Shamineh Tabibi, and Ali Ghaffari, “Energy-Efficient Routing Mechanism for Mobile Sink in Wireless Sensor Networks Using Particle Swarm Optimization Algorithm,” Wireless Personal Communications, vol. 104, no. 1, pp. 199-216, 2019. Crossref, https://doi.org/10.1007/s11277-018-6015-8
[14] Qi Song et al., “Dynamic Path Planning for Unmanned Vehicles Based on Fuzzy Logic and Improved Ant Colony Optimization,” IEEE Access, vol. 8, pp. 62107-62115, 2020. Crossref, https://doi.org/10.1109/ACCESS.2020.2984695
[15] Ms.Madhu Patil, and Dr.Chirag, "A Cross-Layer Based Energy Efficient Cluster Head Selection Model for Wireless Sensornetwork," SSRG International Journal of Mobile Computing and Application, vol. 3, no. 3, pp. 10-17, 2016. Crossref, https://doi.org/10.14445/23939141/IJMCA-V3I5P103
[16] Manisha Rathee et al., “Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks,” IEEE Transactions on Engineering Management, vol. 68, no. 1, pp. 170-182, 2019. Crossref, https://doi.org/10.1109/TEM.2019.2953889
[17] D. Laxma Reddy, Puttamadappa C, and H.N. Suresh, “Merged Glowworm Swarm with Ant Colony Optimization for Energy Efficient Clustering and Routing in Wireless Sensor Network,” Pervasive and Mobile Computing, vol. 71, p. 101338, 2021. Crossref, https://doi.org/10.1016/j.pmcj.2021.101338
[18] S.Shanmadhi, K.Sekar, and T.Dheepa, "Enhancing Energy Efficient in Fault Node Recovery for a Wireless Sensor Network," SSRG International Journal of Computer Science and Engineering, vol. 2, no. 4, pp. 13-16, 2015. Crossref, https://doi.org/10.14445/23488387/IJCSE-V2I4P107
[19] Chenxin Wan et al., “Improved Black Widow Spider Optimization Algorithm Integrating Multiple Strategies,” Entropy, vol. 24, no. 11, p. 1640, 2022. Crossref, https://doi.org/10.3390/e24111640
[20] Jin Wang, “Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks,” Sensors, vol. 19, no. 7, p. 1494, 2019. Crossref, https://doi.org/10.3390/s19071494
[21] Dr.J.Amitava, and T. Anil, "Wireless Sensor Network Based on Reduced Packet Loss with Maximum Clusters," SSRG International Journal of Mobile Computing and Application, vol. 2, no. 2, pp. 1-5, 2015. Crossref, https://doi.org/10.14445/23939141/IJMCA-V2I3P102
[22] Kale Navnath Dattatraya K, and Raghava Rao, “Hybrid Based Cluster Head Selection for Maximizing Network Lifetime and Energy Efficiency in WSN,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 3, pp. 716-726, 2022. Crossref, https://doi.org/10.1016/j.jksuci.2019.04.003
[23] Amir Guidara et al., “Energy-Efficient on-Demand Indoor Localization Platform Based on Wireless Sensor Networks Using Low Power Wake up Receiver,” Ad Hoc Networks, vol. 93, p. 101902, 2019. Crossref, https://doi.org/10.1016/j.adhoc.2019.101902
[24] R. Surendiran, “Similarity Matrix Approach in Web Clustering,” Journal of Applied Science and Computations, vol. 5, no. 1, pp. 267- 272, 2018. Crossref, https://doi.org/16.10089.Jasc.2018.V5i1.140146.22858
[25] Jin Wang, “An Enhanced PEGASIS Algorithm with Mobile Sink Support for Wireless Sensor Networks,” Wireless Communications and Mobile Computing, vol. 2018, 2018. Crossref, https://doi.org/10.1155/2018/9472075
[26] Qi Song et al., “Dynamic Path Planning for Unmanned Vehicles Based on Fuzzy Logic and Improved Ant Colony Optimization,” IEEE Access, vol. 8, pp. 62107-62115, 2020. Crossref, https://doi.org/10.1109/ACCESS.2020.2984695
[27] AmrutaLipare et al., “Energy Efficient Load Balancing Approach for Avoiding Energy Hole Problem in WSN Using Grey Wolf Optimizer with Novel Fitness Function,” Applied Soft Computing, vol. 84, p. 105706, 2019. Crossref, https://doi.org/10.1016/j.asoc.2019.105706
[28] Xinlu Li et al., “Energy-Efficient Load Balancing Ant Based Routing Algorithm for Wireless Sensor Networks,” IEEE Access, vol. 7, pp. 113182-113196, 2019. Crossref, https://doi.org/10.1109/ACCESS.2019.2934889