3D Coverage Optimization for WSNs Based on Improved Flow Direction Algorithm

Traditional algorithms often struggle to address the issue of three-dimensional non-uniform coverage in Wireless Sensor Networks (WSNs). This paper presents a three-dimensional coverage optimization algorithm for WSNs, based on an improved flow direction algorithm. Firstly, Gauss mapping is introduced to process the initialized distribution of nodes, allowing for a more uniform distribution and enhancing the coverage of events within the sensor network. Secondly, T-distribution perturbation is integrated into the flow direction algorithm, further improving its global search capability. Finally, a random-number-based processing method is incorporated to optimize the relocation of out-of-bounds nodes. The proposed optimization algorithm was compared experimentally with Virtual Force Algorithm (VFA), Exact Coverage Algorithm for Unknown Targets (ECA), and Artificial Potential Field Algorithm (APFA) under two scenarios: T-type non-uniform distribution and linear non-uniform distribution of events. The results showed that, under the former scenario, the coverage efficiency of the Improved Flow Direction Algorithm (IFDA) was improved by 3.0%, 4.2%, and 6.3% compared to VFA, ECA, and APFA, respectively. Under the latter scenario, the coverage efficiency of IFDA was improved by 5.1%, 6.2%, and 7.1% compared to the other three algorithms, respectively. These findings demonstrate the better performance of the proposed algorithm in addressing the node distribution issue in WSNs in the case of three-dimensional non-uniform coverage.