A Weighted Voronoi Diagram Based Self-deployment Algorithm for Heterogeneous Mobile Sensor Network in Three-Dimensional Space
For the node deployment problem in three-dimensional heterogeneous sensor networks, the traditional virtual force method is prone to local optimization and the parameters required for calculation are uncertain. A spatial deployment algorithm for 3D mobile wireless sensor networks based on weighted Voronoi diagram is proposed (TDWVADA) to solve the problem. Based on the positions and weights of all nodes in the monitoring area, a three-dimensional weighted Voronoi diagram is constructed. Next, the central position of each node’s the Voronoi region is calculated and the position is regarded as target position of the node movement. Each node moves from the original position to the target position to complete one iteration. After multiple iterations, each node is moved to the optimal deployment location and network coverage is improved. In view of the initial centralized placement of sensor nodes, the addition of virtual force factors is added to the TWDVDA algorithm. An improved algorithm TDWVADA-I was proposed. The algorithm enables nodes that are centrally placed to spread quickly and speeds up deployment. The simulation results show that TDWVADA and TDWVADA-I effectively improve the network coverage of the monitored area compared to the virtual force algorithm and the unweighted Voronoi method. Compared with the virtual force method, the coverage of TDWVADA has increased from 90.53% to 96.70%, and the coverage of TDWVADA-I has increased from 81.12% to 96.56%. Compared with the Voronoi diagram method, the coverage of TDWVADA has increased from 85.01% to 96.70%, and the coverage of TDWVADA-I has increased from 80.82% to 96.56%. TDWVADA and TDWVADA-I also greatly reduce the energy consumption of the network. Experimental results demonstrate the effectiveness of the algorithms.
KeywordsHeterogeneous Wireless Sensor Networks (HWSNs) 3D coverage Area coverage Voronoi diagram Energy consumption
This research was funded by the National Natural Science Foundation of China grant number (61702020), Beijing Natural Science Foundation grant number (4172013) and Beijing Natural Science Foundation-Haidian Primitive Innovation Joint Fund grant number (L182007).
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