Autonomous trust mechanisms enable Internet of Things (IoT) devices to function cooperatively in a wide range of ecosystems, from vehicle-to-vehicle communications to mesh sensor networks. A common property desired in such networks is a mechanism to construct a secure, authenticated channel between any two participating nodes to share sensitive information, nominally a challenging proposition for a large, heterogeneous network where node participation is constantly in flux. This work explores a contract-theoretic framework that exploits the principles of network economics to crowd-source trust between two arbitrary nodes based on the efforts of their neighbors. Each node in the network possesses a trust score, which is updated based on useful effort contributed to the authentication step. The scheme functions autonomously on locally adjacent nodes and is proven to converge onto an optimal solution based on the available nodes and their trust scores. Core building blocks include the use of Stochastic Learning Automata to select the participating nodes based on network and social metrics, and the formulation of a Bayesian trust belief distribution from the past behavior of the selected nodes. An effort-reward model incentivizes selected nodes to accurately report their trust scores and contribute their effort to the authentication process. Detailed numerical results obtained via simulation highlight the proposed framework's efficacy and performance. The performance achieved near-optimal results despite incomplete information regarding the IoT nodes' trust scores and the presence of malicious or misbehaving nodes. Comparison metrics demonstrate that the proposed approach maximized the overall social welfare and achieved better performance compared to the state of the art in the domain.
Keywords: Bayesian model; Internet of Things; PeerTrust; contract theory; crowdsourcing; reinforcement learning.