Energy efficient IoT-based informative analysis for edge computing environment

Internet of Things (IoT) is an intelligent technology that interconnection of everything at any time over an edge computing network. Because of pervasiveness and ubiquity, IoT technology is exhausting energy resources over edge computing platforms. As a result, IoT energy efficiency has become a prominent research domain. Conspicuously, an energy-efficient IoT framework for the edge computing paradigm has been proposed in the present research. Specifically, the proposed framework comprises three layers including perception and control layer, data processing layer, and application and visualization layer. The presented framework predicts inactive sensor intervals based on respective battery percentage, historical utility, and data accuracy needed for edge-based service delivery. Specifically, when the sensing nodes are in inactivity mode, the predicted value increases edge resource usage by re-provisioning the allotted resources. Moreover, the presented technique enables energy-efficient utilization of IoT resources. For validation purposes, the performance comparison is performed with state-of-the-art techniques. It shows that the presented framework is significantly enhanced in terms of energy consumption efficacy (30.45 mJ/K nodes), packet loss ratio (0.51%), statistical performance, and system stability (84.69%).