A Stable and Fair Coalition Formation Scheme in Mobile Crowd Sensing
Yingying Pei, Fen Hou, Lin Cai · 2019
In most of the existing works about mobile crowd sensing, the service provider collects data from each mobile user separately. However, comparing with the collection of data from individual users, batch trading is more attractive for both service provider and mobile users. On one hand, the service provider prefers to buy a batch of data each time even if it may offer a higher unit price since batch trading can save time and efforts in data collection. On the other hand, batch trading is profitable for mobile users since they can take advantage of volume premium. In this paper, we study how mobile users form a coalition to sell their sensing data together. Based on the concept of majorization, we propose a novel scheme to form a fair and stable coalition. Simulation results show the super performance of the proposed method compared with alternative solutions. In specific, the proposed scheme can improve the achieved utility and fairness by 623.68% and 5.51%, respectively, compared to the scheme with independent sell when the number of users is 90.