Multi-Use Trust in Crowdsourced IoT Services

TL;DR

This paper presents an adaptive, fine-grained trust management framework for crowdsourced IoT services that considers user-specific usage patterns to evaluate service trustworthiness dynamically.

Contribution

It introduces a novel adaptive trust framework that combines detection algorithms and usage patterns to assess IoT service trust at a granular level.

Findings

Framework effectively evaluates trust in real-world datasets.

Trust indicators are accurately identified and weighted.

Dynamic trust assessment improves service reliability.

Abstract

We introduce the concept of adaptive trust in crowdsourced IoT services. It is a customized fine-grained trust tailored for specific IoT consumers. Usage patterns of IoT consumers are exploited to provide an accurate trust value for service providers. A novel adaptive trust management framework is proposed to assess the dynamic trust of IoT services. The framework leverages a novel detection algorithm to obtain trust indicators that are likely to influence the trust level of a specific IoT service type. Detected trust indicators are then used to build service-to-indicator model to evaluate a service's trust at each indicator. Similarly, a usage-to-indicator model is built to obtain the importance of each trust indicator for a particular usage scenario. The per-indicator trust and the importance of each trust indicator are utilized to obtain an overall value of a given service for a specific consumer. We conduct a set of experiments on a real dataset to show the effectiveness of the proposed framework.