3D Deployment Optimization of Solar Insecticidal Lamps Using an Improved Jumping Particle Swarm Optimization Algorithm

As an innovative application of wireless sensor networks (WSNs), the Solar Insecticidal Lamp Internet of Things (SIL-IoTs) is widely deployed for green pest control. However, existing deployment strategies commonly assume homogeneous energy-harvesting capabilities among nodes and mandate full coverage, thereby overlooking the realities of uneven solar irradiation and budget constraints in complex 3D terrains. To address these issues, this paper investigates the SIL deployment problem using real DEM data. We first introduce a 3D coverage model alongside a terrain-based solar energy weight model, and formulate the task as a partial coverage optimization problem constrained by both energy harvesting and budget limitations. To solve this problem and avoid premature convergence, we propose an Improved Jumping Particle Swarm Optimization (IJPSO) algorithm. Experimental results on benchmark datasets and DEM-based 3D terrain simulations demonstrate that IJPSO outperforms six competing algorithms, achieving average relative percentage deviations (RPD) of 0.045% and 0.140% and average 3D deployment objective values of 62.27 and 84.37 under the 80% and 90% coverage constraints, respectively. These results demonstrate the effectiveness of IJPSO in reducing deployment costs while maintaining solution consistency.