基于数据驱动的群智感知任务分配算法

摘要

The crowd sensing realizes the maximization perception for crowd, which has been considered the key techno-logy for society network.However, to perceive the position of the participants and trajectory uncertainty problem caused by poor group of mental perception data real-time,this paper proposed a crowd sensing group of intellectual task allocation algorithm based on space task allocation.The algorithm adopted the dynamic and adaptive data-driven scheme to obtain the optimal model to solve the problem of dynamic perception.Track algorithm was based on public mobile model (based on Markov model), Bayesian inference, according to the initial task to estimate the next position, data acquisition strategy based on this algorithm could achieve a local service to guide the future data collection, thus it completed the entire loop feedback of perception.Task assignment is proved based on the uncertain path of mobile group of mental perception task allocation is effective.%群智感知技术的应用实现了人群感测作用的最大化,作为社会网络研究的核心技术之一,对于感知参与者的位置和轨迹不确定性的问题造成群智感知数据实时性较差,为此,提出了一种基于空间任务分配的移动群智任务分配算法.该算法采用动态和自适应的数据驱动方案获取最优的模式来解决感知动态化问题;算法基于公开历史轨迹的移动模型(基于马尔可夫模型),根据初始任务按照贝叶斯推理来估算下一位置,基于该算法的数据采集策略可以实现有本地服务引导未来数据的收集,从而完成整个感知的回路反馈.所提出的任务分配被证明基于不确定轨迹的移动群智感知任务分配是有效的.