Plausible reasoning and knowledge extraction in Cognitive IoT

One central area of current Internet of Things (IoT) research is developing methods for objects to sense their environment autonomously and then link them together so that they can exchange their findings quickly. In response to this urgent need, a new paradigm has emerged that is known as the Cognitive Internet of Things (CIoT). It adds cognitive capability in the form of sophisticated intelligence to the existing IoT. With the generation of huge amounts of data by CIoT applications, there is a compelling need to derive valuable insights from data in a computationally efficient way. Therefore, this research proposes the modified plausible reasoning for extracting knowledge from massive heterogeneous datasets. In the first step, the data is passed onto total variance regularizers to regularise the variance. Subsequently, the clusters are created with probabilistic clustering, and the plausibility theory is redefined at the cluster and the cluster-member levels. The experimental assessment of the environmental data spanning 21.25 years and the cross-validation using a variety of measures demonstrate that the proposed method is more effective than other competing approaches.