Participant Service Quality Aware Data Collecting Mechanism with High Coverage for Mobile Crowdsensing

Abstract

A large number of participants are required to complete specific tasks to sense data and obtain the sensing information in Mobile Crowdsensing. In order to ensure the real-time effectiveness and comprehensiveness of the sensing information, this paper proposes a participant service quality aware data collecting mechanism with high coverage. Firstly, the service quality is measured by the willingness and regional preference of participants to analyze the real-time effectiveness of the sensing data. Then the sensing data coverage is evaluated according to the number of target points covered by the participants during the execution of sensing tasks. Furthermore, the efficiency of the participants is determined by service quality and data coverage under the condition of a limited platform budget. Finally, an iterative greedy algorithm is designed to select the sensing data from the participants set with the highest efficiency, so the coverage of sensing data can be efficiently optimized. The results demonstrate that the proposed data collecting mechanism can reduce the total reward for participants, make the tasks accomplished more efficiently, and effectively collect sensing data with high coverage.