The Truthful Evolution and Incentive for Large-Scale Mobile Crowd Sensing Networks

Abstract

With the rapid development in mobile devices, mobile crowdsourcing has become an important research focus. In large-scale mobile crowdsourcing, the effective evolution prediction and incentive mechanism are the key focuses to improve the efficiency of systems. The evolution model based on evolutionary game theory is researched to predict the evolution trends of mobile crowdsourcing systems effectively. Based on the evolution trends, the reputation updating mechanism (RUMG) is proposed to address free-riding and false-reporting problems. According to spatio-temporal privacy preserving, the incentive mechanism with spatio-temporal privacy preserving for mobile crowdsourcing is researched. In order to protect worker’s spatio-temporal privacy information effectively, a spatio-temporal privacy preserving based on $k$ -anonymity (LKAC) is proposed. In addition, the effectivenesses of the proposed RUMG and LKAC are verified through comparison experiments. This proposed mechanism also improves the security of system and resolves the free-riding and false-reporting problems of mobile crowdsourcing.