PROJECT TITLE :
Budget-Feasible Online Incentive Mechanisms for Crowdsourcing Tasks Truthfully
Mobile crowd sensing (MCS) may be a new paradigm that takes advantage of pervasive mobile devices to efficiently collect knowledge, enabling varied novel applications. To achieve sensible service quality for an MCS application, incentive mechanisms are necessary to attract additional user participation. Most existing mechanisms apply solely for the offline scenario where all users report their strategic sorts beforehand. On the contrary, we tend to concentrate on a a lot of realistic situation where users arrive piecemeal on-line in an exceedingly random order. Primarily based on the web auction model, we tend to investigate the problem that users submit their non-public sorts to the crowdsourcer when arriving, and the crowdsourcer aims at choosing a subset of users before a specified deadline for maximizing the price of services (assumed to be a nonnegative monotone submodular operate) provided by selected users underneath a budget constraint. We tend to style 2 on-line mechanisms, OMZ and OMG, satisfying the computational potency, individual rationality, budget feasibility, truthfulness, client sovereignty, and constant competitiveness below the zero arrival-departure interval case and a more general case, respectively. Through in depth simulations, we evaluate the performance and validate the theoretical properties of our on-line mechanisms.
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