Trust management for the internet of things and its application to service composition

The Internet of Things (IoT) integrates a large amount of everyday life devices from heterogeneous network environments, bringing a great challenge into security and reliability management. Recognizing that the smart objects in IoT are most likely human-carried or human-operated devices, we propose a scalable trust management protocol for IoT, with the emphasis on social relationships. We consider multiple trust properties including honesty, cooperativeness, and community-interest to account for social interaction. Each node performs trust evaluation towards a limited set of devices of its interest only. The trust management protocol is event-driven upon the occurrence of a social encounter or interaction event, and trust is aggregated using both direct observations and indirect recommendations. We analyze the effect of trust parameters on trust assessment accuracy and trust convergence time. Our results show that there exists a trade-off between trust assessment accuracy vs. trust convergence time in the presence of false recommendations attacks performed by malicious nodes. We demonstrate the effectiveness of the proposed trust management protocol with a trust-based service composition application. Our results indicate that trust-based service composition significantly outperforms non-trust-based (random) service composition and its performance approaches the maximum achievable performance with global knowledge.