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基于有向传感器节点概率感知模型,提出了DSm T(Dezert-Smarandache theory)数据融合策略,针对有向传感器网络覆盖增强问题,提出一种基于粒子群算法的有向传感器节点部署算法,以重叠率作为适应度函数,通过迭代调整有向传感器节点的感知方向减少网络节点的重叠率,提高了网络数据可靠性。仿真结果表明,对于感知方向连续可调的有向传感器网络节点在随机部署情况下,和现有的部署方案相比,该部署算法收敛速度快,能够有效增强有向传感器网络节点利用率,提高网络覆盖率。
A DSm T (Dezert-Smarandache theory) data fusion strategy is proposed based on the probabilistic sensing model of a directed sensor node. In order to enhance the coverage of a directed sensor network, a directed sensor node deployment algorithm based on Particle Swarm Optimization (PSO) As a function of fitness, iteratively adjusting the perceived direction of a directed sensor node reduces the overlapping rate of network nodes and improves the reliability of network data. The simulation results show that the proposed algorithm has the advantages of fast convergence when compared with the existing deployment schemes in the case of randomly deployed sensor nodes with continuously adjustable perceived direction. It can effectively enhance the utilization rate of the sensor network nodes and improve Network coverage.