Enhanced Crow Search Algorithm with Cooperative Island Strategy for Energy-Aware Routing in Wireless Sensor Networks

Energy efficiency is a fundamental problem experienced by Wireless Sensor Networks (WSNs), as limited battery power affects network lifespan and reliability. This paper develops a novel energy-efficient routing protocol based on an Enhanced Crow Search Algorithm (ECSA) optimization approach to optimize cluster head selection. The proposed ECSA combines a cooperative island model and an adaptive tournament selection procedure to overcome traditional Crow Search Algorithm (CSA) disadvantages caused by low population diversity, a slow convergence rate, and undesirable exploration-exploitation tradeoffs. A multi-objective fitness function is constructed by analyzing residual energy and remaining battery life, distance to the base station, packet delivery rate, throughput, and path loss to achieve overall network design optimality. Sensor nodes are organized optimally to reduce power consumption and prolong the system's lifespan. The experimental results demonstrate that, for a network of 100 nodes, the proposed ECSA-based routing protocol significantly outperforms recent metaheuristic approaches. Specifically, ECSA achieved 22% lower optimization cost than CSA, 28.2% than Black Widow Optimization (BWO), 26.3% than Grey Wolf Optimizer (GWO), and 30% than Whale Optimization Algorithm (WOA). It further attained 4.8–10.8% higher throughput, 24.4–40.3% lower path loss, 4.5–13.7% higher packet delivery ratio, and 40.1–109.1% more alive nodes compared to these benchmarks. These results confirm that ECSA provides superior energy efficiency, reliability, and robustness for large-scale WSN deployments.

@article{LI2025,
  title     = {Enhanced Crow Search Algorithm with Cooperative Island Strategy for Energy-Aware Routing in Wireless Sensor Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {9},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Xiangqian LI and Xuemei ZHOU},
  doi       = {10.14569/IJACSA.2025.0160966},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160966}
}