基于概率感知模型和量子粒子群算法的移动节点部署

Sensor Node Deployment Based on Probability Sensor Model and Quantum Particle Swarm Algorism

为了解决监测区域的传感器节点部署问题,设计了一种基于概率感知模型和量子粒子群算法的移动节点部署方法。首先,在传统概率感知模型中加入节点剩余能量因素进而得到改进的概率感知模型C(si,p)={0,if d(si,p)≥r-reEir/Ei0e-λασ,if d(si,p)≤r+re,1,if r-re≤d(si,p)≤r+re然后基于改进的概率感知模型设计了多目标优化的节点部署模型,在优化模型中考虑了网络覆盖率和能量因素。最后定义了基于量子粒子群算法来获得节点的最优位置对应的Pareto最优解的优化算法(即将粒子编码为节点部署方案,采用最小化网络能耗和最大化网络覆盖率为粒子的Pareto目标,引导粒子在可行解空间不断更新位置寻求最优解)。仿真实验结果表明:文中方法能正确地实现监测区域的传感器节点部署,能实现较为均匀的网络覆盖,与其他方法相比,具有较高的网络覆盖率和较长的网络生命周期,具有较大的优越性。

In order to solve the node deployment problem in monitoring area, a mobile node deployment method based on probability sensor model and quantum particle swarm algorism was proposed. Firstly, the probability sensor model was improved by adding the energy factor, and then the node deployment model based on the improved probability sensor model considering the network coverage rate and energy. Finally, the optimal algorism was designed based on quantum particle algorism to get the Pareto solution. The simulation experiment shows the method in this paper can realize node deployment in monitoring area, and the network coverage can be realized evenly, and compared with the other methods, it has higher coverage rate and longer network life cycle. It has more priority.