Distributed Optimization Strategy of Tangent Search Quadratic Interpolation Optimization Algorithm for Real Terrain

In order to effectively tackle the problems of low network coverage and high complexity in the area coverage optimization algorithms of wireless sensor networks within three-dimensional real terrains, this paper proposes a Distributed Optimization Strategy based on the Tangent Search Quadratic Interpolation Optimization Algorithm (TS-QIODOS). The strategy gradually implements optimization in three stages. Firstly, the quadratic interpolation optimization algorithm is improved by introducing the tangent search quadratic interpolation optimization algorithm (TS-QIO), which significantly enhances the search speed and convergence by incorporating adaptive switching probability and tangent search techniques to optimize the node distribution in the monitoring area. Secondly, a method is proposed to calculate the node’s horizontal and vertical deflection angles for node angle optimization. Finally, the TS-QIO algorithm is used to adjust the node heights again to achieve optimal coverage. Simulation results show that the proposed algorithm has a fast convergence speed, significantly improves coverage, and reduces runtime. Under different terrain conditions, the algorithm demonstrates good adaptability and stability.