Nondeterministic-Mobility-Based Incentive Mechanism for Efficient Data Collection in Crowdsensing

Abstract

Mobile crowdsensing (MCS) booms the implementation of the Internet of Things (IoT) in different areas due to flexibility and low deployment cost. However, collecting sufficient high quality sensing data is crucial for the success of various applications. Incentive mechanism design plays a critical role in the successful implementation of mobile MCS systems. Most of existing work consider that the platform exactly knows the trajectory of mobile users. However, in most cases, it is difficult to obtain the accurate information of the location of mobile users due to either privacy issue or the lack of information. In this article, we consider nondeterministic mobility of mobile users, where only the probability distribution of users’ mobility is available. We design an effective mechanism to achieve the quality data collection with the objective of maximizing the expected social welfare. Simulation results show that the proposed mechanism achieves her expected social welfare compared with four existing schemes, while satisfying truthfulness, individual rationality, and computational efficiency.