Multi-Perspective Trust Management Framework for Crowdsourced IoT Services

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.