Socially Aware Task Selection Game for Users in Mobile Crowdsensing

Mobile Crowdsensing (MCS) has become an emerging paradigm to solve complex urban sensing problems by utilizing the ubiquitous sensing capacities of the crowd. One critical issue in MCS is to efficiently allocate tasks to users. We focus on addressing the task allocation problem in a distributed manner, where each user individually and freely makes his decision to undertake tasks. Existing distributed schemes simply consider users behave completely selfishly, which leads to inefficient solutions and damages the overall benefit of all users. Different from existing schemes, in this paper we integrate the social relationship into users' decision making and build a socially aware utility model for each user, which consists of both user's own utility and the weighted sum of his social neighbors' utilities. Based on this, we formulate a novel Socially Aware Task Selection (SATS) game for users in MCS. We theoretically prove the existence of pure Nash equilibrium in the SATS game with the help of a potential game framework. We further propose a distributed user selection algorithm to actually achieve the pure Nash equilibrium. Extensive simulations based on both real and synthetic social relationship graph datasets demonstrate that our approach can achieve more efficient solutions which improve users' overall benefit compared with existing schemes.