Coverage optimization strategy for 3D wireless sensor network based on adaptive inertia weight Arctic Puffin Optimization Algorithm

Abstract

A multi-strategy improved Arctic Puffin Optimization Algorithm (CAAPO) based node coverage optimization approach is provided to tackle the problems of unequal node deployment and low coverage in wireless sensor networks (WSNs). Drawing inspiration from the species’ survival strategies in polar environments, the methodology incorporates three key enhancements: Tent chaotic mapping for initial solution refinement, lens imaging inversion mechanisms to prevent premature convergence through population diversification, and adaptive inertia weighting for balanced exploration-exploitation dynamics across optimization phases.

Empirical validation through CEC2017 benchmarks and engineering simulations demonstrates CAAPO’s efficacy in nodal deployment optimization. Implementation within sensor coverage models achieves maximal coverage through strategic node positioning, exhibiting superior spatial distribution uniformity compared to conventional methods. The algorithm demonstrates operational efficiency through minimized computational overhead and energy expenditure, thereby enhancing network longevity in resource-constrained environments.