MACHINE LEARNING-BASED DATA ANALYTICS FOR IOT-ENABLED INDUSTRY AUTOMATION

Abstract

The virtual network which is regarded as a bridge between real-world applications and computerized systems is termed Internet of Things (IoT). Internet of Things can access the real-world application by considering the wireless sensor network and internet facility as its main technology. The Internet o Things (IoT) comprises a global network that connects sensors, electronic devices, and software. Fog computing requires managing services among the various fog nodes. Fog computing plays a major role in the reduction in latency and energy consumption. Traces of fog nodes help to identify the location awareness to the IoT destination. As the fog nodes are geographically distributed, they can support high availability and scalability factors in large amounts of data provided by various sensors in industries. The heterogeneity issues can be handled by the proposed cognitive fog of things system by supporting interoperability and flexibility in sensors connected to machinery. The proposed work comprises of reduction in energy efficiency and latency reduction in the industrial sector with the fault analysis from the data received from sensors in machinery. The proposed system consists of the newly developed cognitive fog of things with optimization techniques. This work determines the impact of data transmission in cloud computing with the fog computing layer to improve the energy efficiency, delay time, and throughput.