Oasis: Online all-phase quality-aware incentive mechanism for MCS

• Main Authors: ZHANG, Man, LI, Xinghua, MIAO, Yinbin, LUO, Bin, MA, Siqi, CHOO, Kim-Kwang Raymond, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Task Analysis; Sensors; Data Integrity; Recruitment; Costs; Resumes; Reliability; Knapsack Secretary; Mobile Crowdsensing; Online Recruit; Quality Aware Incentive Mechanisms; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8661
• Institution: Singapore Management University

To motivate users to submit high quality data for mobile crowdsensing (MCS), some quality-aware incentive mechanisms have been proposed, which recruit and pay users strategically. However, in the existing mechanisms, the recruitment based only on tasks matching degree leads to the ineffective insistent data quality incentive. Meanwhile, the absence of the reasonable payment strategy cannot motivate users to submit high quality data in the current task. To address the above problems, we propose an Online all-phase quality-aware incentive mechanism (Oasis) to realize the quality incentive in both recruitment and payment phases. With the knapsack secretary, Oasis first devises a quality-aware pre-budgeting recruitment strategy, which decides whether the arriving user's long-term data quality and bid satisfy the recruited criterion. Then, in the payment phase, Oasis evaluates and updates the current and long-term data qualities of users. Based on the evaluation results, a two-level payment strategy is devised employing the Myerson theorem, where users submitting higher quality data can obtain more utilities under the budget constraint. Theoretical analysis proves that Oasis satisfies economic feasibility and constant competitiveness while achieving quality incentive in recruitment and payment phases. Extensive experiments using the real-world dataset demonstrate that the sensing result accuracy of Oasis increases 67% compared with the existing works.