MagiCrowd: A crowd based incentive for location-aware crowd sensing

Mobile devices have become more and more powerful with lots of embedded sensors providing sensibility of various data, which makes crowd sensing a compelling paradigm followed by emerging applications. In this paper, we design a crowd based incentive mechanism to stimulate participants joining sensing campaigns with consideration of location privacy. In our incentive, MagiCrowd, we creatively cluster the participants as several atomic crowds to bargain with the crowd sensing campaigner. We propose K-least to cluster the participants and achieve K-anonymity location privacy. Negotiated Bargain Theory is introduced to determine the incentive payoffs and Penalty Allocation is designed to allocate the gross crowd rewards. MagiCrowd can achieve high participation rate, rational reward and location privacy. We discuss the proposed incentive in depth and conduct thorough experiments to evaluate the desirable properties.