# Trust Evaluation Mechanism for User Recruitment in Mobile Crowd-Sensing in the Internet of Things

## TL;DR

This paper introduces a novel trust model called Experience-Reputation (E-R) for evaluating trust among mobile users in MCS, improving data quality and security in IoT services by effectively recruiting trustworthy contributors.

## Contribution

The paper proposes the E-R trust model and a trust-based recruitment scheme, enhancing trust evaluation and malicious user detection in MCS systems.

## Key findings

- Trust-based scheme improves data quality in MCS.
- E-R model effectively detects malicious users.
- Enhanced trust evaluation leads to better IoT service reliability.

## 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 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.