Incentive Mechanism Design for Uncertain Tasks in Mobile Crowd Sensing Systems Utilizing Smart Contract in Blockchain

【Author】 Jiang, Xikun; Ying, Chenhao; Yu, Xinchun; Dudder, Boris; Luo, Yuan

【Source】COLLABORATIVE COMPUTING: NETWORKING, APPLICATIONS AND WORKSHARING, COLLABORATECOM 2022, PT I

【影响因子】

【Abstract】Mobile crowd sensing (MCS) systems recently have been regarded as a newly-emerged sensing paradigm, where the platform receives the requested tasks from requesters and outsources the collection of sensory data to participating workers. However, the centralized structure of the MCS system is vulnerable to a single point of failure, and there is a lack of trust between participants and the platform. Additionally, participating in MCS is often costly. So the paramount problem is how to solve these problems associated with centralized structures and incentivize more participation. Most existing works design the incentive mechanisms only considering static sensing tasks whose information is completely known a priori (e.g., when and which task arrives). Due to the dynamic environment and severe resource constraints, the tasks are usually uncertain, i.e., the information of tasks is incompletely known by the platform. Therefore, in this paper, we design an incentive mechanism, HERALD, for the uncertain tasks in MCS systems by using smart contracts. Specifically, the uncertain tasks are low sensitive to time (that is, tasks do not require real-time information) and arrive according to a probability distribution. HERALD utilizes the decentralized nature of the blockchain to eliminate the system's reliance on third parties and satisfies truthfulness, individual rationality, as well as low computational complexity and low social cost. The desirable properties of HERALD are validated through both theoretical analysis and extensive simulations.

【Keywords】Incentive mechanism; Uncertain sensing tasks; Mobile crowd sensing; Smart contract

【发表时间】2022

【收录时间】2023-05-24

【文献类型】理论模型

【主题类别】

区块链技术-核心技术-激励机制