移动群智感知中面向用户区域的分布式多任务分配方法  

Distributed multi-task allocation method for user area in mobile crowd sensing

作　　者：韩俊樱 张振宇[1,2] 孔德仕 HAN Junying;ZHANG Zhenyu;KONG Deshi(College of Information Science and Engineering,Xinjiang University,Urumqi Xinjiang 830046,China;Xinjiang Multilingual Information Technology Key Laboratory,Xinjiang University,Urumqi Xinjiang 830046,China;School of Computer Science,Sichuan University,Chengdu Sichuan 610065,China)

机构地区：[1]新疆大学信息科学与工程学院,乌鲁木齐830046 [2]新疆大学新疆多语种信息技术实验室,乌鲁木齐830046 [3]四川大学计算机学院,成都610065

出　　处：《计算机应用》2020年第2期358-362,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61262089)~~

摘　　要：多数群智感知(MCS)任务分配方法针对单个任务,难以适用于多任务实时并发的现实场景,而且往往需要实时获取用户位置,不利于保护参与者隐私。针对上述问题,提出了一种面向用户区域的分布式多任务分配方法Crowd-Cluster。该方法首先通过贪心启发算法将全局感知任务及用户区域进行分簇;其次,基于空间关联性采用Q-learning算法将并发任务组合构成任务路径;接着,构建符合玻尔兹曼分布的用户意愿模型对任务路径进行动态定价;最后,基于历史信誉记录贪心优选参与者实现任务分配。基于真实数据集mobility的实验结果表明,Crowd-Cluster能有效减少参与者总人数及用户总移动距离,并且在低人群密度场景下,还能降低感知资源不足对任务完成度的影响。Most Mobile Crowd Sensing(MCS)task allocation methods are specific to a single task and are difficult to apply to real-world scenarios of real-time concurrent multi-task.And it is often necessary for these methods to obtain user location in real time,which is not conducive to the protection of participant privacy.Concerning the above problems,a distributed multi-task allocation method for user area was proposed,named Crowd-Cluster.Firstly,the global perception task and the user area were clustered by using the greedy heuristic algorithm.Secondly,based on the spatial correlation,the Q-learning algorithm was used to combine the concurrent tasks into the task path.Then,the task path was dynamically priced by constructing user intention model that satisfying the Boltzmann distribution.Finally,based on the historical reputation records,the participants were greedily selected to implement task allocation.Experimental results on the real dataset mobility show that Crowd-Cluster can effectively reduce the total number of participants and the total movement distance of users,and can also reduce the impact of insufficient perception resources on task completion in the low population density scenarios.

关 键 词：移动群智感知 多任务分配 任务组合 分布式计算 动态定价 

分 类 号：TP393.01[自动化与计算机技术—计算机应用技术]