Citation :

M Dharma Teja, R. Srinivasan, "Tent Mapping Reverse Learning-Elk Herd Optimization Algorithm for Trust-aware and Energy Efficient Clustering and Routing in Wireless Sensor Networks," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 4, pp. 277-289, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I4P121

Abstract

Wireless Sensor Networks (WSNs) emerge as an important development for tracking and monitoring applications in a wide range. WSN captures environmental information, acquires data, and communicates to a sink node for processing and analysis. The important task in deploying WSNs is to enhance the network lifetime, particularly when the energy is constrained. Moreover, maintaining trust in WSN becomes complex, which establishes consistent trust relationships among nodes that are important for achieving reliable communication. Hence, this research proposes a new approach of the Tent Mapping Reverse Learning-Elk Herd Optimization Algorithm (TMRL-EHO) for energy-effective Cluster Head (CH) selection and routing in WSN. In TMRL-EHO, an objective value for CH selection considers multiple factors, including distance among nodes, trust, traffic rate, density of the cluster, and remaining energy. The simulation findings indicate that an introduced approach enhances a better throughput of 97% with 500 nodes, as compared to the existing Quantum Behavior and Gaussian Mutation-assisted Archimedes Optimization Algorithm (QGAOA).

Keywords

Cluster Head, Elk Herd Optimization Algorithm, Energy efficiency, Tent Mapping Reverse Learning, Wireless Sensor Network.

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