Adaptive Crow Search Algorithm for Hierarchical Clustering in Internet of Things-Enabled Wireless Sensor Networks
Lingwei Wang, Hua Wang · International Journal of Advanced Computer Science and Applications · 2025
The Internet of Things (IoT) relies on efficient Wireless Sensor Networks (WSNs) for data collection and transmission in various applications, including smart cities, industrial automation, and environmental monitoring. Clustering is a fundamental technique for structuring WSNs hierarchically, enabling load balancing, reducing energy consumption, and extending network lifespan. However, clustering optimization in WSNs is an NP-hard problem, necessitating heuristic and metaheuristic approaches. This study introduces an Adaptive Crow Search Algorithm (A-CSA) for clustering in IoT-enabled WSNs, addressing the inherent limitations of the standard CSA, such as premature convergence and local optima entrapment. The proposed A-CSA incorporates three key enhancements: (1) a dynamic awareness probability to improve global search efficiency during initial population selection, (2) a systematic leader selection mechanism to enhance exploitation and avoid random selection bias, and (3) an adaptive local search strategy to refine cluster formation. Performance evaluations conducted under varying network configurations, including node density, network size, and base station positioning, demonstrate that A-CSA outperforms existing clustering approaches in terms of energy efficiency, network longevity, and data transmission reliability. The results highlight the potential of A-CSA as a robust optimization technique for clustering in IoT-driven WSN environments.