A Novel Task Allocation Algorithm in Mobile Crowdsensing with Spatial Privacy Preservation

Abstract

The Internet of Things (IoT) has attracted the interests of both academia and industry and enables various real-world applications. The acquirement of large amounts of sensing data is a fundamental issue in IoT. An efficient way is obtaining sufficient data by the mobile crowdsensing. It is a promising paradigm which leverages the sensing capacity of portable mobile devices. The crowdsensing platform is the key entity who allocates tasks to participants in a mobile crowdsensing system. The strategy of task allocating is crucial for the crowdsensing platform, since it affects the data requester’s confidence, the participant’s confidence, and its own benefit. Traditional allocating algorithms regard the privacy preservation, which may lose the confidence of participants. In this paper, we propose a novel three-step algorithm which allocates tasks to participants with privacy consideration. It maximizes the benefit of the crowdsensing platform and meanwhile preserves the privacy of participants. Evaluation results on both benefit and privacy aspects show the effectiveness of our proposed algorithm.

Truthful Profit Maximization Mechanisms for Mobile Crowdsourcing

“…Algorithmic game theory has become a useful method to build incentive auctions in the crowdsourcing system due to the strategic behavior of workers. Yaron [5] is the first person who studied the budget-constraint mechanism design which then became the major category of auctions for crowdsourcing auction design, i.e., [6][7][8][9][10][11][12][13][14][15][16][17]. In particular, Yaron proposed a method for provable guarantee auctions in crowdsourcing markets with fixed budget [7].…”