基于感知因子的水下传感器节点覆盖模型研究

The research of underwater sensor node coverage model based on perception factor

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.

It was found in our study that the sensor node model used in the previous underwater sensor network simulation has the defects that are difficult to overcome.Boolean model has sacrificed more coverage performance to enhance the efficiency of simulation calculation,while continuous probability model （CPM）has given up the computational efficiency to approach the actual coverage performance simulation.In order to find a sensor node model which can not only meet the need of the covering performance but improve the calculation efficiency,we first proposed the concept of perception factor（PF）from the point of view of the effective detection of targets;and then puted forward a sensor node discrete model based on PF.After that,it was hypothesized that the nodes could be divided without limit;discrete perceptual factor model（DPFM）was extended to continuous perceptual factor model （CPFM）,and the validity of CPFM was proved.Finally,CPFM was further optimized for multi ring perceptualnbsp;factor model（MRPFM）to avoid the calculation of the logarithm.In this paper,the coverage performance and compu-tational efficiency of MRPFM were simulated and compared with other models.The same model parameters and en-vironmental parameters were set up by simulation.First,the coverage performance of CPFM in the condition of positive triangle,regular and regular hexagon deployment was simulated.It was found that the best way was not a regular hexagon deployment,but a positive triangle deployment.Then,we simulated the positive triangle optimal de-ployment of MRPFM and continuous probability model,and compared the coverage performance of the three models.It was obvious that the performance of MRPFM was a bit lower than that of continuous exponential probability model（CEPM）,but significantly higher than Boolean model;Finally,this research simulated the time of the deployment of Boolean model,CEPM,multi ring probability model （MRPM）and MRPFM,based on different node numbers.The simulation map clearly shows that,compared with using CEPM,the time to complete the deployment calculation of using the MRPFM is obviously reduced,and the more the number of nodes,the more obvious the effect of time reduction;compared with using Boolean model and MRPM,the time is also reduced to some extent.Therefore,the MRPFM has practical value in underwater sensor network simulation.