Leveraging Social Networks to Enhance Effective Coverage for Mobile Crowdsensing

Abstract

With the development of the Internet of Things and smart city, the demand for mobile crowdsensing (MCS) is increasing. Most state-of-the-art studies in MCS assume that the participants are those who have registered with the MCS platform. In this paper, we propose to exploit social network for MCS worker recruitment instead of limiting participants to the platform. MCS platform can motivate more users to join in the task by leveraging the social influence of seed workers. Inspired by this, we first propose a social influence propagation model for MCS task. Considering the constraint of budget, our objective is to maximize the effective sensing coverage by selecting a limited number of seed workers, which is formulated as MESC problem. Based on the voting theory, a heuristic algorithm named as KT Voting is proposed to select seed workers. KT Voting algorithm allows users to vote for the most influential user to themselves and add a weight to their vote based on their sensing locations. After that, seed workers are selected based on the votes received. Extensive experiments based on two real-world data sets verify the effectiveness and efficiency of the proposed KT Voting algorithm.