考虑障碍物的RSSI室内定位传感器多目标优化

Multi-objective Optimization of RSSI Sensor Deployment Considering Obstacles for Indoor Positioning

针对目前无线传感器网络(wireless sensor network, WSN)部署时,障碍物影响WSN优化部署的问题,以接收信号强度指示传感器室内定位应用为例,提出了一种考虑障碍物的无线传感器多目标优化部署方法。首先,基于室内定位算法原理和传感器覆盖模型,给出了在室内定位场景下WSN有效覆盖率的概念和信标节点部署模型。然后,在分析障碍物感知模型和信标节点部署策略的基础上,提出了考虑障碍物的传感器部署多目标优化模型。最后,以第三代非支配排序遗传算法为基础设计优化模型求解算法,数值仿真结果与正三角形、正方形、正六边形均匀部署,以及没有考虑障碍物的优化部署(进化1 000代,传感器个数为36)结果进行对比,结果表明所提方法的WSN有效覆盖率分别提高了52.7%、112.1%、16.6%和9.62%。

Objectives:At present,in the research on the optimization of wireless sensor network(WSN)deployment under the application of indoor positioning,most of studies are generally carried out under the ideal conditions without obstacles,and the impact of obstacles on WSN is rarely considered.To consider the influence of obstacles for WSN deployment,we propose a multi-objective optimization method of WSN topology considering obstacles under the application of indoor positioning using received signal strength indication sensors.Methods:Firstly,a concept of effective coverage and beacon node deployment model is proposed based on the indoor positioning algorithm and sensor coverage model.Secondly,after the analysis of obstacle perception model and beacon node deployment strategy,a multi-objective optimization model of sensor deployment considering obstacles is proposed.Finally,the non-dominated sorting genetic algorithm III is introduced to solve this optimization model.Three optimization objectives of all individuals,effective coverage,number of sensors,and convex hull area,as well as constraints considering obstacles and WSN topology rationality are calculated.Results:The optimization method without considering obsta-cles is used as the comparison method,and three uniform sensor deployments are compared with the numericalsimulation results of the proposed method.Regular triangle,square and hexagon are the mosaic shapes forthe uniform sensor deployment.In the 1 000 generation, the effective coverage rate of the proposed methodincreased by 9.62% compared with the comparison method and increased by 52.7%, 112.1% and 16.6%respectively compared with the uniform deployment of regular triangle, square and regular hexagon.Conclusions:The method proposed in this paper not only has advantages in improving the effective coverage rateand accelerating convergence of the optimization algorithm, but also increases the effective coverage ratecompared with the traditional uniform deployment of sensors. Therefore, the proposed method will be helpfulto improve the accuracy of indoor positioning.