IEEE Project Abstract

Mobile Crowd-Sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However,the human involvement in MCS results in a high possibility for unintentionally contributing corrupted and falsified data orintentionally spreading disinformation for malevolent purposes,consequently undermining IoT services. Therefore, recruiting trustworthy contributors plays a crucial role in collecting high quality data and providing better quality of services while minimizing the vulnerabilities and risks to MCS systems. In this article, a novel trust model called Experience-Reputation (E-R)is proposed for evaluating trust relationships between any two mobile device users in an MCS platform. To enable the E-Rmodel, virtual interactions among the users are manipulated by considering an assessment of the quality of contributed data from such users. Based on these interactions, two indicators of trust called Experience and Reputation are calculated accordingly. By incorporating the Experience and Reputation trust indicators(TIs), trust relationships between the users are established,evaluated and maintained. Based on these trust relationships, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy MCS users to contribute to data sensing tasks. In order to evaluate the performance and effectiveness of the proposed trust-based mechanism as well as the E-R trust model, we deploy several recruitment schemes in an MCS test bed which consists of both normal and malicious users. The results highlight the strength of the trust-based scheme as it delivers better quality for MCS services while being able to detect malicious users. We believe that the trust-based user recruitment offers an effective capability for selecting trustworthy users forvarious MCS systems and, importantly, the proposed mechanism is practical to deploy in the real world.