Location, Context and Social Objectives Using Knowledge-based Rules and Conflict Resolution for Security in Internet of Things

Security and knowledge systems effectively identify the node behavior based on device identity, location, social attributes, and networking parameters. In this article, we propose a novel approach of location, context, and social objectives using knowledge-based rules and conflict resolution for security (LOCSKS) in the Internet of Things. The proposed system applies the Bayesian decision theory and analyzes the node behavior based on prior and posterior knowledge of the location, context, and social objectives in the Internet of Things. LOCSKS exclusive and economical keys consider the context to service type mapping and risk levels to ensure the location privacy and trust in the system. The knowledge-based and inference rules identity the conformity and conflicting nodes. The conflict resolution approach blocks the invalid nodes, suspends the malicious nodes, and delays the suspicious nodes. Simulations indicate that the proposed LOCSKS scheme effectively identifies the node behavior and conflicting conditions, reduces the key violations, and enhances location privacy as compared to the existing schemes.