Incentive-Aware Recruitment of Intelligent Vehicles for Edge-Assisted Mobile Crowdsensing

Edge-assisted mobile crowdsensing is an emerging paradigm where mobile users collect, and share sensing data at the edge of networks. With the abundant on-board resources, and large movement patterns of intelligent vehicles, they have become candidates to sense up-to-date, and fine-grained informati...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2020-10, Vol.69 (10), p.12085-12097
Main Authors: Liu, Luning, Wen, Xiangming, Wang, Luhan, Lu, Zhaoming, Jing, Wenpeng, Chen, Yawen
Format: Article
Language:English
Subjects:
Algorithms
Cooperation
Decision theory
Edge computing
Edge-assisted vehicular crowdsensing
Heuristic methods
incentive mechanism
Intelligent vehicles
nash bargaining theory
Optimization
participant recruitment
Quality of service
Recruitment
Sensors
Servers
Spatiotemporal phenomena
Task analysis
Vehicles
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Summary:Edge-assisted mobile crowdsensing is an emerging paradigm where mobile users collect, and share sensing data at the edge of networks. With the abundant on-board resources, and large movement patterns of intelligent vehicles, they have become candidates to sense up-to-date, and fine-grained information for large areas. The design of vehicle recruitment in edge-assisted mobile crowdsensing is challenging due to the selfishness, and the uneven distribution of vehicles, as well as the spatiotemporal constraints of vehicular crowdsensing applications. To deal with these challenges, this paper proposes an incentive-aware vehicle recruitment scheme for edge-assisted mobile crowdsensing. In particular, we first design an incentive mechanism to motivate cooperation among the edge server, and the intelligent vehicles, and apply the Nash bargaining theory to obtain the optimal cooperation decision. Furthermore, a practical, and efficient scheme is proposed to weigh the contribution of vehicles. Then, we formulate the participant recruitment as an optimization problem, and prove that it is NP-hard. To address this problem, an effective heuristic algorithm with a guaranteed approximation ratio is proposed, by leveraging the property in submodular optimization. Finally, we conduct extensive simulations, based on a real dataset, to validate the superiority of the proposed schemes.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2020.3011693