TRUST MANAGEMENT OF CROWDSOURCED IOT SERVICES

We propose a novel trust management framework for crowdsourced IoT services. The framework targets three main aspects: trust assessment, trust information credibility and accuracy, and trust information storage. First, trust assessment is achieved by leveraging machine-learning-based multi-perspective trust model that captures the inherent characteristics of IoT services. Additionally, we harness the usage patterns of IoT consumers to offer a trust assessment that adapts to IoT consumers' uses. For this, we propose a technique that detects the set of indicators that may influence trust for a given IoT service type. The indicators' significance is computed based on a given IoT consumer's usage pattern. The framework leverages the computed significance to provide a trust assessment tailored to IoT consumers. We propose memoryless just-in-time trust assessment; an approach for assessing trust without relying on historical records (memoryless) that exploits the service-session-related data (just-in-time). Second, our framework ascertains the credibility and accuracy of trust-related information before trust assessment. This is achieved by validating the data collected by IoT consumers and providers. In addition, our framework ensures the contextual fairness between IoT services and trust information. Third, we propose a blockchain-based trust information storage approach. Our proposed storage solution preserves the integrity and availability of trust information