Context Aware Data Perception in Cognitive Internet of Things - Cognitive Agent Approach

In the past, the existing Internet of Things caused traffic congestion and receiver uncertainty problems due to insufficient data transfer between the nodes or devices for data perception. The authors have proposed the method for context-aware data perception in the cognitive internet of things environment. The proposed context-aware data perception is described in the following stages, initially nodes in Cognitive Internet of Things network are clustered effectively using adaptive pillar ‘K' means clustering algorithm. After the formation of effective clusters, the cognitive agent performs the effective context-aware data learning using support-based convolutional neural networks. Finally, adaptive fuzzy logic defines the effective decision for data perception. The experimental results show that the proposed method outperforms the cognitive agent approaches of data perception in terms of network lifetime, energy consumption, data perception accuracy, and throughput in the cognitive internet of things.