Research Article
An Efficient and Truthful Online Incentive Mechanism for a Social Crowdsensing Network
@INPROCEEDINGS{10.1007/978-3-030-67537-0_4, author={Lu Fang and Tong Liu and Honghao Gao and Chenhong Cao and Weimin Li and Weiqin Tong}, title={An Efficient and Truthful Online Incentive Mechanism for a Social Crowdsensing Network}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2021}, month={1}, keywords={Crowdsensing Incentive mechanism Truthfulness Social influence}, doi={10.1007/978-3-030-67537-0_4} }- Lu Fang
Tong Liu
Honghao Gao
Chenhong Cao
Weimin Li
Weiqin Tong
Year: 2021
An Efficient and Truthful Online Incentive Mechanism for a Social Crowdsensing Network
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-67537-0_4
Abstract
Crowdsening plays an important role in spatiotemporal data collection by leveraging ubiquitous smart devices equipped with sensors. Considering rational and strategic device users, designing a truthful incentive mechanism is a crucial issue. Moreover, another key challenge is that there may not exist adequate participating users in reality. To encourage more users to participate, the social relationship among them can be leveraged, as users may be significantly influenced by their social friends. In this paper, we assume recruited users to diffuse uncompleted sensing tasks to their friends, and propose an efficient and truthful online incentive mechanism for a such social crowdsensing network. Specially, we model the time-varying social influence of a user by extending two metrics of node centrality used in social networks. In order to maximize the accumulated social welfare achieved by the network, we design a user selection algorithm and a payment determination algorithm respectively, in which payments given to participants not only depend on data qualities but also related with social influences. We theoretically prove that our mechanism achieves properties of computational efficiency, individual rationality, and truthfulness. Extensive simulations are conducted, and the results show the superiority of our mechanism.
