Multi-Objective Energy Efficient Adaptive Whale Optimization Based Routing for Wireless Sensor Network

Himani Bali, Amandeep Gill, Abhilasha Choudhary, Divya Anand, Fahd S. Alharithi, Sultan M. Aldossary and Juan Luis Vidal Mazón
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Himani Bali: Department of Electrical & Electronics Engineering, JECRC University, Jaipur 303905, India
Amandeep Gill: Department of Electrical Engineering, Chandigarh University, SAS Mohali 140413, India
Abhilasha Choudhary: Department of Electrical & Electronics Engineering, JECRC University, Jaipur 303905, India
Divya Anand: School of Computer Science & Engineering, Lovely Professional University, Phagwara 144411, India
Fahd S. Alharithi: Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Sultan M. Aldossary: Department of Computer Sciences, College of Arts and Sciences, Prince Sattam Bin Abdulaziz University, Wadi Aldawaser 11990, Saudi Arabia
Juan Luis Vidal Mazón: Higher Polytechnic School, Universidad Europea del Atlantico, C/Isabel Torres 21, 39011 Santander, Spain

Energies, 2022, vol. 15, issue 14, 1-19

Abstract: In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm.

Keywords: clustering; whale optimization; supercluster head (sch); hop count; routing; Wireless Sensor Networks (WSNs); fuzzy inference system (FIS) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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