群智感知网络任务渐进式分配仿真研究  

Simulation Research on Progressive Allocation of Group Intelligence Network Tasks

作　　者：薄文彦[1] BO Wen-yan(School of Computer and Network Engineering,Shanxi Datong University,Shanxi Datong 037009,China)

机构地区：[1]山西大同大学计算机与网络工程学院,山西大同037009

出　　处：《计算机仿真》2020年第5期255-258,316,共5页Computer Simulation

基　　金：大同市科技计划基金项目(2018187)。

摘　　要：当前方法分配群智感知网络中存在的任务时,分配网络任务所用的时间较长,且大多数用户没有完成接受的感知任务,存在任务分配效率低和任务完成率低的问题。为高效的完成群智感知网络任务的分配,需要研究群智感知网络任务分配方法,提出群智感知网络任务渐进式分配方法,采用经验模态分解方法分解群智感知网络中的原始信号,根据分解得到的IMF分量重构信号,去除群智感知网络中存在的噪声信号。分析感知数据和移动节点在群智感知网络中的位置和移动规则,构建感知数据模型和移动模型,在感知数据模型和移动模型的基础上构建信誉模型,利用信誉模型完成群智感知网络任务的渐进式分配。仿真结果表明,所提方法的任务分配效率高、任务完成率高。The current method needs long time to allocate network tasks and most users can not complete the accepted perception tasks. Therefore, task allocation efficiency and task completion rate are low. In order to efficiently allocate the task in crowd-sensing network, it is necessary to research the task allocation method. In this article, a progressive allocation method for task in crowd-sensing network was proposed. Firstly, the empirical mode decomposition method was used to decompose the original signal in crowd-sensing network. According to the decomposed IMF component, the signal was reconstructed to remove the noise signal in the crowd-sensing network. Then, location and movement rules of perception data and mobile nodes in crowd-sensing networks were analyzed. Moreover, the perception data model and mobile model were built. On this basic, the reputation model was built. Finally, the reputation model was used to complete progressive assignment of tasks in crowd-sensing network. Simulation results show that the proposed method has high task allocation efficiency and task completion rate.

关 键 词：群智感知网络 渐进式 任务分配 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]