Privacy-preserving QoI-aware participant coordination for mobile crowdsourcing

Abstract Mobile crowdsourcing systems are important sources of information for the Internet of Things (IoT) such as gathering location related sensing data for various applications by employing ordinary citizens to participate in data collection. In order to improve the Quality of Information (QoI) of the collected data, the system server needs to coordinate participants with different data collection capabilities and various incentive requirements. However, existing participant coordination methods require the participants to reveal their trajectories to the system server which causes privacy leakage. But, with the improvement of ordinary citizens’ consciousness to protect their rights, the risk of privacy leakage may reduce their enthusiasm for data collection. In this paper, we propose a participant coordination framework, which allows the system server to provide optimal QoI for sensing tasks without knowing the trajectories of participants. The participants work cooperatively to coordinate their sensing tasks instead of relying on the traditional centralized server. A cooperative data aggregation, an incentive distribution method, and a punishment mechanism are further proposed to both protect participant privacy and ensure the QoI of the collected data. Simulation results show that our proposed method can efficiently select appropriate participants to achieve better QoI than other methods, and can protect each participant’s privacy effectively.