Enhanced Sand Cat Swarm Optimization to Achieving Maximum Coverage for Wireless Sensor Networks

When it comes to ensuring the effective operation of Wireless Sensor Networks (WSNs), it is essential to achieve total coverage of the region that is being targeted. However, the random initial deployment of sensor nodes often leads to inefficient positioning, resulting in coverage gaps. To mitigate this issue, an improved algorithm based on the Optimization of the Virtual Force-Directed Enhanced Sand Cat Swarm (SCSO) is introduced to optimize sensor node placement, thereby reducing coverage voids and enhancing overall network coverage. This approach enhances the natural hunting behavior of sand cats and integrates a nonlinear convergence mechanism to improve their sensitivity. Moreover, the algorithm incorporates a virtual resultant force as a perturbation factor during node position updates, accelerating convergence and enhancing optimization efficiency. Simulation results, based on the deployment of 30 sensor nodes within a 60 m × 80 m area, demonstrate that the SCSO algorithm that was proposed is capable of achieving a coverage rate (CR) of approximately 99.61%, reflecting a 29.3% increase over the baseline. Additionally, the algorithm maintains superior coverage performance across different sensor node densities and monitoring region sizes.