[1]XU Jingru,DONG Hongbin,ZHAO Bingxu,et al.Community service-oriented spatiotemporal crowdsourcing task allocation with role awareness[J].CAAI Transactions on Intelligent Systems,2023,18(2):293-304.[doi:10.11992/tis.202205017]
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Community service-oriented spatiotemporal crowdsourcing task allocation with role awareness

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 2 Page number: 293-304 Column: 学术论文—智能系统 Public date: 2023-05-05
Title:
Community service-oriented spatiotemporal crowdsourcing task allocation with role awareness
Author(s):
XU Jingru;  DONG Hongbin;  ZHAO Bingxu;  JI Ruohan
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
Keywords:
community service-oriented spatiotemporal crowdsourcing; task allocation; E-CARGO; role-based collaboration; kernel density clustering; role perception; learning and forgetting curve; multistage quantification of site qualification value
CLC:
TP311
DOI:
10.11992/tis.202205017
Abstract:
This paper proposes the community service-oriented spatiotemporal crowdsourcing task allocation problem to adapt to the limited amount of delivery boxes or collection points in community crowdsourcing. The articles will be relatively delivered to the ground in real time according to the user-defined time. This paper proposes a PQGR algorithm based on greedy allocation, a PQGM algorithm based on the GMRACRA method, and a PQGMA algorithm that further shortens the running time to address the aforementioned problem. This algorithm aims to realize the objective of delivering high-value order sets by workers with high qualification value through the formalization problem of E-CARGO based on the role cooperation model. Considering data processing and quantification, a new role perception method based on kernel density clustering is proposed to realize effective task division. The places-qualification-based multistage quantitative model of an agent is proposed on the basis of learning and forgetting curves to realize online learning and adaptive updating of agent location qualification value. Finally, experiments on gMission and synthetic datasets are conducted to verify the effectiveness and efficiency of the algorithm.
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