TITLE:
AC-COA: A Coati-Inspired Optimization Algorithm for Area Coverage Enhancement in Wireless Sensor Networks
AUTHORS:
Pangop Doris-Khöler Nyabeye, Ngangmo Olga Kengni
KEYWORDS:
Area Coverage Problem, Coati Optimization Algorithm, Swarm Intelligence, Wireless Sensor Networks
JOURNAL NAME:
Journal of Computer and Communications,
Vol.14 No.2,
February
14,
2026
ABSTRACT: Optimizing area of interest (AoI) coverage in wireless sensor networks (WSNs) is essential for ensuring reliable monitoring and high quality of service. Although numerous approaches have been proposed to enhance sensor coverage, many of them struggle to simultaneously achieve high coverage rates, efficient sensor distribution, and low computational cost. As a result, maximizing coverage in WSNs remains a challenging optimization problem that continues to attract significant research interest. In this paper, a novel area coverage approach based on the Coati Optimization Algorithm (COA), a recently proposed parameter-free meta-heuristic, is introduced. The proposed method, called AC-COA, enhances the original COA by incorporating adaptive sensor placement and diversification mechanisms that help avoid local optima and rapidly guide sensors toward under-covered regions. During the exploration phase, a scaling coefficient is employed to intensify the search in low-coverage areas. In the exploitation phase, a dynamic attenuation coefficient gradually reduces randomness, while a gradient-based overlap minimization term improves sensor distribution by limiting redundant coverage. The performance of AC-COA is evaluated through extensive simulations and compared with Particle Swarm Optimization (PSO), Improved Grey Wolf Optimization (IGWO), Improved Honey Badger Algorithm (IHBA), and Genetic Algorithm (GA) in terms of coverage rate and execution time. Simulation results demonstrate that AC-COA achieves superior coverage while significantly reducing sensor redundancy and computational cost. For a network consisting of 30 sensors, AC-COA outperforms GA by 15%, PSO and IGWO by 25%, and IHBA by 36%. Overall, AC-COA provides an effective solution for WSN area coverage optimization, offering an excellent trade-off between coverage efficiency and execution speed. Its parameter-free design makes it a robust and practical approach for applications requiring dense and continuous monitoring.