Fog based qos aware framework for urban air mobility systems

Abstract

In today s industrial world, the IIoT market segment is aiming at a major transformation cycle digitally. Leveraging IoT in today s business becomes a crucial factor in exploring new business opportunities and creating new business models. IIoT enabled systems operate on the data set received from a variety of Edge devices and nodes. The major reason for rapid growth of IIoT in the recent years is due to the increased need to solve data driven problem in connected systems for applications such as Predictive Maintenance, Remote Monitoring and other advancements in Operations Technology Domains. Organizations are aiming at Digital transformation to make the best out of their business models. To realize the potential of IIoT in today s world, the consolidated data from digital systems can be used for inferring intelligent behaviour and making insightful decisions with minimal or no human intervention. The strength of analytics lies in analysing the raw data and infer from them to avoid alarm conditions. The data collected for various business use cases can be applied in a variety of applications like remote monitoring, event control, command, control, prediction and forecasting. Major organizations are moving towards digital transformation in deploying IoT in Industrial environments to reap several benefits in their businesses. Automation using Industrial IoT offers numerous advantages such as reduction in errors, increase in productivity and efficiency, safety improvements, cost reduction and predictive maintenance. As most of the data ingested into the IIoT platforms are real time, performing analytics and decision making at the Cloud will lead to increase in latency, computation costs, transmission costs and storage costs. Edge Analytics brings data processing, storage and analytics closer to the Edge devices and can significantly minimize latency, improve network bandwidth and provide better analysis newline