移动群智网中基于粒子群寻优和距离协作判别的数据收集研究

Data collection mechanism based on particle swarm optimization and distance cooperative discrimination for mobile crowd sensing networks

针对移动群智网应用对数据收集的实时性和可靠性要求,提出了一种基于粒子群寻优和距离协作判别的数据收集机制。该机制基于时空二维感知区域定义了具有高精度和适应度的粒子群寻优模型,结合二维正态分布基于时间序列给出了用于判断感知数据源的距离协作判别机制,激励用户移动节点积极加入到协作通信,从而提出了适用于移动群智网应用的数据收集机制。仿真实验结果表明,所提出的数据收集机制在网络生存能力、传输延迟、移动节点存活能力和能耗等方面表现优越。

This paper proposed a data collection mechanism based on the particle swarm optimization and distance cooperation for the real-time performance and reliability of data collection . Based on spatio-tem poral sensing region , the mechanism defined with high accuracy and adaptation of partic le swarm optimization model, combined with two-dim ensional normal distribution based on the time series method was used to determ ine the perception of data source distance collab ora tive decision mechanis m , incentive user mobile nodes to actively participate in the cooperation com m unica tion, and put forward the applicable tom obile swarm in telligentnet work application data collection m echanism . The results o f sim ulation experim ents show that theproposed data collection system has advantages in the aspects of network survivability , transmission delay , and the surviva l ability o f m obile nodes and energy consum ption.