Static Node Deployment Optimization in Wireless Sensor Networks Based on Fractional-order Chameleon Swarm Algorithm

Objectives To address issues in wireless sensor networks for monitoring target sea areas, such as arbitrary deployment methods, low effective coverage, and high coverage redundancy, Fractional-order Chameleon Swarm Algorithm (FCSA) is proposed. Methods First, an improved Circle chaotic map is employed to initialize the population, enhancing both diversity and global distribution. Second, a fractional-order velocity update strategy integrates historical velocity information in the update process, enabling adaptability and agility across different iteration phases and improving global search efficiency. Additionally, a Levy flight mechanism is used to guide individual position updates, thereby further enhancing global search capability and diversity. Results Experimental results demonstrate that, compared with nine algorithms, including CSA, CSA-Circle, CSA-Levy, and RSO, FCSA achieves higher coverage while significantly reducing coverage redundancy. It exhibits superior optimization performance, higher convergence accuracy, and more uniform individual distribution. Conclusions In addressing the static node deployment optimization problem in wireless sensor networks, FCSA effectively enables efficient and rational node distribution, significantly improving coverage and deployment quality, and providing theoretical support for engineering applications such as sea area monitoring.