Multi-Perspective Trust Management Framework for Crowdsourced IoT Services
Mohammed Bahutair, Athman Bouguettaya, and Azadeh Ghari Neiat

TL;DR
This paper introduces a multi-perspective trust management framework for crowdsourced IoT services, utilizing machine learning to evaluate trust based on various attributes, validated through experiments on real datasets.
Contribution
It presents a novel, generic trust management framework that captures multiple perspectives and employs machine learning for trust evaluation in crowdsourced IoT services.
Findings
Effective trust assessment demonstrated on real-world datasets
Multi-perspective model improves trust accuracy
Framework adaptable to various IoT environments
Abstract
We propose a novel generic trust management framework for crowdsourced IoT services. The framework exploits a multi-perspective trust model that captures the inherent characteristics of crowdsourced IoT services. Each perspective is defined by a set of attributes that contribute to the perspective's influence on trust. The attributes are fed into a machine-learning-based algorithm to generate a trust model for crowdsourced services in IoT environments. We demonstrate the effectiveness of our approach by conducting experiments on real-world datasets.
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