HH-NMSFRA: A Heterogeneity-Aware Hybrid Protocol for Energy-Efficient Routing in Wireless Sensor Networks

Abstract

When designing and implementing Wireless Sensor Networks (WSNs), where sensor nodes are restricted by battery power, energy efficiency is a fundamental challenge. In order to optimize energy consumption and enhance data delivery performance, this study suggests a new Heterogeneity-aware Hybrid NMSFRA (HH-NMSFRA) protocol that combines energy-aware node selection, multi-hop routing, and hybrid clustering approaches. By dynamically modifying the cluster head (CH) selection procedure in response to residual energy and node capabilities, the protocol takes node heterogeneity into consideration. Additionally, swarm intelligence methods like Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are integrated for effective multi-hop routing towards the base station (BS), and reinforcement learning (RL) is used to improve the adaptive behavior of the protocol. According to simulation studies, HH-NMSFRA performs better than conventional protocols likeM-LEACH, EDEEC, and NMSFRA in important performance parameters like control overhead, energy consumption, data delivery ratio, and network lifetime. In particular, HH-NMSFRA improves the data transmission ratio by 25% and extends network lifetime by up to 30% when compared to DEEC, making it a viable option for HWSNs with limited energy.

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