Multi-Mobile Sink-Based Clustering and Routing in Heterogeneous Wireless Sensor Networks Using the Crow Search Algorithm

Abstract

Heterogeneous Wireless Sensor Networks (HWSNs) face conspicuous challenges in maximizing network lifetime due to uncurbed power utilization depletion at sensor nodes (SNs) and cluster heads (CHs). This study presents an optimal Multi-Mobile Sink-based Clustering and Routing (MSCR) control strategy using the multi-objective Crow Search Algorithm (CSA) to address resource-constrained node energy-efficiency challenges. The proposed CSA-based MSCR model enhances deployed node’s performance by intelligently coordinating SNs, CHs, and mobile sink (MS) to optimize the data acquisition process while reducing energy consumption. The CSA strategy is deployed for two crucial optimization operations: constructing optimal mobile sink trajectories and selecting energy-efficient cluster heads under limited resources and harsh environmental circumstances. Performance analysis compares the CSA-based model against established traditional metaheuristic methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO). Large-scale simulations conducted under various network and harsh environmental conditions demonstrate significant improvements in critical performance metrics. The CSA-based MSCR approach achieves a 36% reduction in power consumption, 49% accelerated cluster formation and cluster head (CH) selection, and 42% improvements in data delivery efficiency compared to conventional approaches. Furthermore, the proposed model extends average network lifetime by 45% while maintaining data accuracy above 97%. The results endorse the usefulness of the CSA optimization strategy in solving composite multi-objective optimization problems in wireless sensor networks. This work contributes a strong and scalable solution for next-generation IoT applications requiring energy-efficient data collection in challenging deployment environments. The outcome highlights the strengths of metaheuristic algorithms like CSA in advancing MSCR control for WSNs, offering a promising alternative to traditional approaches for improving the data delivery and lifetime of the sensor networks.