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

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 highquality 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. We believe that the trust-based user recruitment offers an effective capability for selecting trustworthy users for various MCS systems and, importantly, the proposed mechanism is practical to deploy in the real world.

GDPR Compliant Blockchains–A Systematic Literature Review

“…Since information inside the blockchain cannot be removed, it directly contradicts with Article 17 [28][29][30][31][32][33]. Technically, the same goes for Article 16 (right to rectification) also since data cannot be edited either [29], [34].…”

A Trust Based Scheme to Protect 5G UAV Communication Networks

“…Li et al [20] proposed a attack-resistant trust management mechanism to evaluate the trustworthiness of mobile nodes and detect the malicious attacks. Truong et al [21] introduced a novel experiencereputation scheme to evaluate the trust relationships between two mobile device users in mobile crowd-sensing. Departing from the above studies, our work focuses on designing an effective trust evaluation scheme for UAVs to improve the security.…”

Simulation of Trust-Based Mechanism for Enhancing User Confidence in Mobile Crowdsensing Systems

“…Another important issue in MCS campaigns is the trustworthiness or reliability of the data collected. Trust is a great issue as tasks are assigned anonymously and data is collected from multiple locations or participants which can be in one or another compromised leading to unreliability of the collected data [33]. It is crucial to maintain a high level of data trustworthiness which shows how much the data used are trusted, authentic and protected from abuse so that decision making should be based on precise, and certain data [34].…”

“…The indicators used to evaluate the direct trust of an MCS includes interactions, past related experiences, and relationships. The experience is constructed from the interactions between two entities, while the reputation is constructed from all the experiences towards an entity [33]. The comprehensive trust is the one that incorporates the features of all the former two types of trust.…”