The Intelligent Optimization of Agricultural Wireless Sensor Networks via Salp Swarm and whale optimization strategies

Neel Raj E; Vasanth Arumugam; Dharmaraj N; Krishnamoorthy P

Authors

Neel Raj E Sri Eshwar College of Engineering Author
Vasanth Arumugam Author
Dharmaraj N Care College of Engineering Author
Krishnamoorthy P Author

Keywords:

Wireless sensor network, sensor nodes, base station, clustering, routing, salp swarm optimization

Abstract

Wireless sensor networks (WSNs) can effectively offer sensing and communication services for IoT-based applications, particularly those with limited energy resources. Clustering routing technology is useful for decreasing energy usage and increasing network lifespan. This work discusses the existing optimization techniques along with new strategies to optimize WSNs in agricultural conditions. Some of the existing methods that have efficiently exploited clustering and routing optimization techniques, such as Modified Particle Swarm Optimization (MPSO), Bee Colony Optimization (BCO), and Enhanced Moth Flame Optimization (EMFO), have shown their effectiveness in clustering and routing optimization. However, to improve cluster head (CH) selection and routing performance, this work suggests using the Salp Swarm Optimization (SSO) for CH selection and the Enhanced Whale Optimization Algorithm (EWOA) for routing. These methods are used to improve network measures such as energy consumption, packet delivery ratio (PDR), throughput, latency, and network longevity. By using salps' cooperative behavior for balanced CH selection and whales' strategic exploration-exploitation balance for routing, the suggested strategy intends to decrease energy depletion, increase data transmission reliability, and prolong the network's operational lifespan. The comparison research with current algorithms demonstrates considerable gains in energy economy, higher PDR, improved throughput, reduced latency, and extended network lifespan, proving these algorithms' potential for optimizing WSNs in agricultural applications.

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Author Biography

Vasanth Arumugam

Vasanth Arumugam holds a Bachelor of Technology (B.Tech.) degree in Information Technology and a Master of Engineering (M.E.) degree in Embedded and Real-Time Systems. He is currently pursuing his Ph.D. in Embedded Systems at Annamalai University, Chidambaram, Tamil Nadu, India. His research interests include embedded systems, real-time computing, the Internet of Things, artificial intelligence, cloud computing, and intelligent automation. He is particularly interested in developing practical and innovative technology solutions for academic and industrial applications.

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