  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 or
intentionally 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 a MCS
platform. To enable the E-R model, 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 a MCS testbed 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.
