Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks

Sensors

Osamah Ibrahim Khalaf¹  Sakthi Ulaganathan²  Prakash Mohan³  Neelakandan Subramani⁴  Youseef Alotaibi⁵  Saleh Alghamdi⁶ 

[1] Al-Nahrain Nano Renewable Energy Research Center, Al-Nahrain University, Baghdad 10071, Iraq;Department of Computer Science and Engineering Saveetha School of Engineering,SIMATS, Chennai 602105, India;Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore 641032, India;Department of Computer Science and Engineering, R.M.K Engineering College, Chennai 601206, India;Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah 21955, Saudi Arabia;Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia;

关键词: underwater sensor networks;    energy efficiency;    metaheuristics;    network lifetime;    communication;    routing;   

DOI  :  10.3390/s22041618

来源: DOAJ

【 摘 要 】

Underwater wireless sensor networks (UWSNs) comprise numerous underwater wireless sensor nodes dispersed in the marine environment, which find applicability in several areas like data collection, navigation, resource investigation, surveillance, and disaster prediction. Because of the usage of restricted battery capacity and the difficulty in replacing or charging the inbuilt batteries, energy efficiency becomes a challenging issue in the design of UWSN. Earlier studies reported that clustering and routing are considered effective ways of attaining energy efficacy in the UWSN. Clustering and routing processes can be treated as nondeterministic polynomial-time (NP) hard optimization problems, and they can be addressed by the use of metaheuristics. This study introduces an improved metaheuristics-based clustering with multihop routing protocol for underwater wireless sensor networks, named the IMCMR-UWSN technique. The major aim of the IMCMR-UWSN technique is to choose cluster heads (CHs) and optimal routes to a destination. The IMCMR-UWSN technique incorporates two major processes, namely the chaotic krill head algorithm (CKHA)-based clustering and self-adaptive glow worm swarm optimization algorithm (SA-GSO)-based multihop routing. The CKHA technique selects CHs and organizes clusters based on different parameters such as residual energy, intra-cluster distance, and inter-cluster distance. Similarly, the SA-GSO algorithm derives a fitness function involving four parameters, namely residual energy, delay, distance, and trust. Utilization of the IMCMR-UWSN technique helps to significantly boost the energy efficiency and lifetime of the UWSN. To ensure the improved performance of the IMCMR-UWSN technique, a series of simulations were carried out, and the comparative results reported the supremacy of the IMCMR-UWSN technique in terms of different measures.

【 授权许可】

Unknown