Incentive Mechanisms for Spatio-Temporal Tasks in Mobile Crowdsensing

Abstract

Mobile crowdsensing emerges as a new paradigm that takes advantage of pervasive sensor-embedded smartphones to collect sensory data. Many incentive mechanisms for mobile crowdsensing have been proposed. However, none of them takes into consideration the spatio-temporal tasks in mobile crowdsensing systems, where the sensing areas of tasks can have overlaps, and the collective sensing time for each task needs to meet the specified time duration. In this paper, we present two system models for location sensitive users and location insensitive users, respectively, and formulate the social optimization problem for each model. We design two reverse auction based truthful incentive mechanisms to minimize the social cost subject to the constraint that each of the spatio-temporal tasks can be completed with its collective sensing time no less than a minimum sensing time required by the platform. Through both theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, and guaranteed approximation.