Computer Science ›› 2022, Vol. 49 ›› Issue (2): 231-240.doi: 10.11896/jsjkx.210400249

• Artificial Intelligence • Previous Articles     Next Articles

Dynamic Task Scheduling Method for Space Crowdsourcing

SHEN Biao, SHEN Li-wei, LI Yi   

  1. School of Computer Science,Fudan University,Shanghai 201203,ChinaShanghai Key Laboratory of Data Science (Fudan University),Shanghai 201203,China
  • Received:2021-04-23 Revised:2021-06-08 Online:2022-02-15 Published:2022-02-23
  • About author:SHEN Biao,born in 1996,postgraduate.His main research interests include the fusion of the ternary human-machine-thing and mobile cloud computing software system.
    SHEN Li-wei,born in 1982,Ph.D,associate professor.His main researchin-terests include the fusion of the ternary human-machine-thing,mobile application development and analysis,etc.
  • Supported by:
    National High Technology Research and Development Program of China(2018YB1004800).

Abstract: Space crowdsourcing is used to solve offline crowdsourcing tasks with time and space constraints,and it has developed rapidly in recent years.Task scheduling is an important research direction of space crowdsourcing.The difficulty lies in the dynamic uncertainty of tasks and workers in the scheduling process.In order to efficiently perform task scheduling,a dynamic task scheduling method for space crowdsourcing that considers the uncertainty of tasks and workers at the same time is proposed.The method has been improved in three aspects.First,the factors that need to be considered for scheduling are expanded.In addition to considering the uncertainty of the temporal and spatial attributes of the newly added tasks,it also considers the uncertainty of the transportation mode and temporal and spatial attributes of the newly added workers.Then,the scheduling strategy is improved.By using the aggregate scheduling strategy,the dynamically added tasks are aggregated first,and then the task allocation and path optimization are performed.Compared with the traditional non-aggregated scheduling,the calculation time is significantly reduced.The last aspect is to improve the scheduling algorithm.Based on the traditional genetic algorithm,the task allocation and path optimization operations are performed iteratively.Compared with the scheduling algorithm that first allocates tasks and then optimizes the path,it improves the accuracy of the optimal results.In addition,a simulation platform for dynamic scheduling of space crowdsourcing task paths based on real map navigation is designed and implemented,and the method is verified by this platform.

Key words: Genetic algorithm, Route planning, Space crowdsourcing, Task allocation, Task scheduling

CLC Number: 

  • TP311
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