Abstract

Wireless Sensor Networks (WSNs) are gaining importance due to the applicability in various domains of modern research. Nevertheless, energy-constrained nodes pose the biggest challenge in WSNs. The optimization of energy usage and stability within WSNs has consistently been a focal point in research endeavors. This paper presents a hybrid mechanism, Hybrid Leader Artificial Ecosystem based Optimization (HLAEO) algorithm as an effective strategy to address the dual challenges of cluster head selection and routing in WSNs. It combines two powerful mechanisms namely Hybrid Leader-Based Optimization (HLBO) and Artificial Eco-based Optimisation (AEO). HLAEO uses leader-based optimization for cluster selection. Here channel probabilities are adjusted based on network performance metrics and thus making nodes to efficiently navigate available channels. This would certainly enhance the spectral efficiency and reducing interference. For routing, the algorithm uses an efficient optimization approach to discover and maintain optimal paths between the sensor nodes. The proposed HLAEO algorithm continuously refines routing paths, adapting to the changes in the network conditions and topology dynamics. Performance of the proposed HLAEO is validated through simulations. Results show that the proposed HLAEO perform well with respect to energy utilization. HLAEO provides several benefits including real-time adaptation to environmental changes and enhanced energy efficiency by minimizing redundant transmissions and extending the lifespan of a network.