Social Welfare Maximization in Participatory Smartphone Sensing
Participatory smartphone sensing has lately become more and more popular as a new paradigm for performing large-scale sensing, in which each smartphone contributes its sensed data for a collaborative sensing application. Most existing studies assume that smartphone users are strictly strategic and completely rational, which can achieve only sub-optimal system performance. Few existing studies can maximize a system-wide objective which takes both the platform and smartphone users into account. This paper focuses on the crucial problem of maximizing the system-wide performance or social welfare for a participatory smartphone sensing system. There are two great challenges. First, the social welfare maximization can not be realized on the platform side because the cost of each user is private and unknown to the platform in reality. Second, the participatory sensing system is a large-scale real-time system due to the huge number of smartphone users who are geo-distributed in the whole world. We propose a novel price-based decomposition framework, in which the platform provides a unit price for the sensing time spent by each user and the users return the sensing time via maximizing the monetary reward. This pricing framework is an effective incentive mechanism as users are motivated to participate for monetary rewards from the platform. The original problem is equivalently converted into an optimal pricing problem, and a distributed solution via a step-size-free price-updating algorithm is proposed. More importantly, the distributed algorithm ensures that the cost privacy of each user is not compromised. Experimental results show that our novel distributed algorithm can achieve the maximum social welfare of the participatory smartphone system.
KeywordsParticipatory smartphone sensing pricing distributed optimizations
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