Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 358-362.DOI: 10.11772/j.issn.1001-9081.2019081402

• DPCS 2019 • Previous Articles     Next Articles

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

Junying HAN1,2(), Zhenyu ZHANG1,2, Deshi KONG3   

  1. 1.College of Information Science and Engineering,Xinjiang University,Urumqi Xinjiang 830046,China
    2.Xinjiang Multilingual Information Technology Key Laboratory,Xinjiang University,Urumqi Xinjiang 830046,China
    3.School of Computer Science,Sichuan University,Chengdu Sichuan 610065,China
  • Received:2019-07-31 Revised:2019-09-02 Accepted:2019-09-19 Online:2019-09-29 Published:2020-02-10
  • Contact: Junying HAN
  • About author:ZHANG Zhenyu, born in 1964, M. S., professor. His research interests include opportunity network, mobile crowd sensing.
    KONG Deshi, born in 1995, M. S. candidate. His research interests include mobile crowd sensing, multi-agent reinforcement learning.
  • Supported by:
    the National Natural Science Foundation of China(61262089)


韩俊樱1,2(), 张振宇1,2, 孔德仕3   

  1. 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
  • 通讯作者: 韩俊樱
  • 作者简介:张振宇(1964—),男,山西太原人,教授,硕士,主要研究方向:机会网络、移动群智感知
  • 基金资助:


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.

Key words: Mobile Crowd Sensing (MCS), multi-task allocation, task combination, distributed computing, dynamic pricing



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

CLC Number: