LAUSR

Mobile crowdsourcing systems (MCSs) are important sources of information for the positioning services in Internet-of-Things such as gathering location information through employing citizens to participate in data collection. Although MCSs have attracted significant research and development efforts, there are salient open issues and challenges in security and privacy for MCS, which is an essential factor for its success. This paper proposes an integrated strategy named data trustworthiness enhanced crowdsourcing strategy (DTCS) to enhance data trustworthiness and defend against the internal threats for mobile crowdsourcing. The DTCS integrates effective methods including an evaluation scheme for the attribute relevancy and familiarity of participants, a trust relationship establishment method, a group division strategy based on attributes and metagraph, and a core-selecting-based incentive mechanism. The simulation results show that the DTCS improves the performance of the crowdsourcing strategy compared to the state-of-the-art including the TSCM and PPPCM. The DTCS can effectively defend against internal conflicting behavior attacks and collusion attacks to enhance data trustworthiness for mobile crowdsourcing.