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水下传感器网络节点造价昂贵,所处的服役环境恶劣,水下传感器网络仿真是目前常用的重要研究方法.目前,在传感器网络仿真中最常用的布尔模型和概率模型均具有其难以克服的缺点.为了设计一种能同时满足覆盖性能和计算效率要求的节点模型,提出了感知因子(perception factor,PF)的概念;基于PF提出了离散感知因子(discrete perceptual factor model,DPFM)模型、连续感知因子模型(continuous perceptual factor model,CPFM)和多环感知因子模型(multi ring perceptual factor model,MRPFM).对MRPFM进行了仿真,与同参数条件布尔模型、连续概率模型(continuous probability model,CPM)进行了覆盖性能对比分析,与布尔模型、CPM、多环概率模型(multi ring probability model,MRPM)进行了算法时间需求对比分析.仿真表明:CPFM最佳几何部署方式为正三角部署;MRPFM覆盖性能比CPM下降不明显,比布尔模型提升显著;MRPFM计算时间需求比CPM明显减少,比布尔模型和MRPM也有较大幅度的减少.MRPFM有效发挥低概率感应带感知能力,提升了覆盖性能,又减少了计算量.
Underwater sensor network node is expensive and its service environment is harsh, and underwater sensor network simulation is an important research method nowadays.At present, the most commonly used Boolean and probabilistic models in sensor network simulation all have their insurmountable shortcomings In order to design a node model which can meet both coverage and computational efficiency requirements, a concept of perception factor (PF) is proposed. Based on PF, a discrete perceptual factor model (DPFM) model is proposed, MRPFM was simulated with continuous perceptual factor model (CPFM) and multi-ring perceptual factor model (MRPFM), and compared with the same parameter conditional Boolean model and continuous probability model (CPM) And compared with the Boolean model, CPM and multi-ring probability model (MRPM), the simulation results show that the best geometric deployment method of CPFM is equilateral triangulation, MRPFM coverage performance ratio CPM decline is not obvious, Boolean model significantly improved; MRPFM calculation time required CPM ratio was significantly reduced, and MRPM also beable to model a more substantial reduction .MRPFM effectively play with a low probability sense perception, improved coverage performance, but also reduces the amount of calculation.