Decentralized knowledge discovery using massive heterogenous data in Cognitive IoT

Current Internet of Things (IoT) research focuses on inserting cognition into its system architecture and design. Therefore, Cognitive IoT (CIoT) has emerged. CIoT inherits several features and challenges from IoT. Since IoT generates huge amounts of heterogeneous data, a cognitively inspired technique is required to extract meaningful insight from these data in less computation time. Keeping this requirement as a main goal, this research proposes a novel algorithm which executes the total variance regularization, probabilistic clustering, and the alternating direction method of multiplier (ADMM) of robust principal component analysis (RPCA) at cluster node and rest of the computation, i.e., copula modelling, the measurement of the amount of information to each copula-modelled sensory data for interesting patterns extraction, and Bayesian network formation, is executed at the fusion centre. Experimental evaluation across 21 years of environmental data and the cross-validation with different measures reveals its efficacy over competing approaches.