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,...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.展开更多
To deploy sensor nodes over the area of interest,a scheme,named node scattering manipulation,was proposed.It adopted the following method:during node scattering,the initial states of every node,including the velocity ...To deploy sensor nodes over the area of interest,a scheme,named node scattering manipulation,was proposed.It adopted the following method:during node scattering,the initial states of every node,including the velocity and direction,were manipulated so that it would land in a region with a certain probability;every sensor was relocated in order to improve the coverage and connectivity.Simultaneously,to easily analyze the process of scattering sensors,a trajectory model was also proposed.Integrating node scattering manipulation with trajectory model,the node deployment in wireless sensor network was thoroughly renovated,that is,this scheme can scatter sensors.In practice,the scheme was operable compared with the previous achievements.The simulation results demonstrate the superiority and feasibility of the scheme,and also show that the energy consumption for sensors relocation is reduced.展开更多
文摘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.
基金Project(2007AA01Z224) supported by National High-Tech Research and Development Program of China
文摘To deploy sensor nodes over the area of interest,a scheme,named node scattering manipulation,was proposed.It adopted the following method:during node scattering,the initial states of every node,including the velocity and direction,were manipulated so that it would land in a region with a certain probability;every sensor was relocated in order to improve the coverage and connectivity.Simultaneously,to easily analyze the process of scattering sensors,a trajectory model was also proposed.Integrating node scattering manipulation with trajectory model,the node deployment in wireless sensor network was thoroughly renovated,that is,this scheme can scatter sensors.In practice,the scheme was operable compared with the previous achievements.The simulation results demonstrate the superiority and feasibility of the scheme,and also show that the energy consumption for sensors relocation is reduced.
