Incentive Mechanisms for Time Window Dependent Tasks in Mobile Crowdsensing


Mobile crowdsensing can enable various enticing novel sensing applications thanks to the prominent advantages like wide spatiotemporal coverage, low value, smart scalability, pervasive application eventualities, etc. In mobile crowdsensing applications, incentive mechanisms are necessary to stimulate a lot of potential smartphone users and to attain smart service quality. During this paper, we have a tendency to specialize in exploring truthful incentive mechanisms for a completely unique and sensible scenario where the tasks are time window dependent, and therefore the platform has strong requirement of information integrity. We tend to present a universal system model for this state of affairs primarily based on reverse auction framework and formulate the matter as the Social Optimization User Choice (SOUS) problem. We tend to design 2 incentive mechanisms, MST and MMT. In single time window case, we have a tendency to style an optimal algorithm based on dynamic programming to pick users. Then we have a tendency to verify the payment for each user by VCG auction; while in multiple time window case, we have a tendency to show the overall SOUS problem is NP-hard, and we have a tendency to style MMT based mostly on greedy approach, that approximates the optimal solution inside a issue of , where is the length of sensing time window outlined by the platform. Through both rigorous theoretical analysis and intensive simulations, we have a tendency to demonstrate that the proposed mechanisms achieve high computation efficiency, individual rationality and truthfulness.

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