This book, at first, explores the evolution of the IoT to SIoT and offers a comprehensive understanding of SIoT and trust management vis-à-vis SIoT. It subsequently envisages trust quantification models by employing key SIoT-specific trust features, including SIoT relationships (e.g., friendships, working relationships, and community-of-interest), direct observations, and indirect observations, to augment the idea of trust quantification of a SIoT object. Furthermore, diverse trust aggregation techniques, i.e., conventional weighted sum, machine learning, and artificial neural networks, are proposed so as to address the challenges of the trust aggregation. Finally, the book outlines the future research directions for emphasizing the importance of trustworthiness management in the evolving notion of the SIoT.

This book, at first, explores the evolution of the IoT to SIoT and offers a comprehensive understanding of SIoT and trust management vis-à-vis SIoT. It subsequently envisages trust quantification models by employing key SIoT-specific trust features, including SIoT relationships (e.g., friendships, working relationships, and community-of-interest), direct observations, and indirect observations, to augment the idea of trust quantification of a SIoT object. Furthermore, diverse trust aggregation techniques, i.e., conventional weighted sum, machine learning, and artificial neural networks, are proposed so as to address the challenges of the trust aggregation. Finally, the book outlines the future research directions for emphasizing the importance of trustworthiness management in the evolving notion of the SIoT.