LAUSR

The ever-increasing growth of connected smart devices and IoT verticals is leading to the crucial challenges of handling the massive amount of raw data generated by distributed IoT systems and providing timely feedback to the end-users. Although the existing cloud computing paradigm has an enormous amount of virtual computing power and storage capacity, it might not be able to satisfy delay-sensitive applications since computing tasks are usually processed at the distant cloud-servers. To this end, edge/fog computing has recently emerged as a new computing paradigm that helps to extend cloud functionalities to the network edge. Despite several benefits of edge computing including geo-distribution, mobility support and location awareness, various communication and computing related challenges need to be addressed for future IoT systems. In this regard, this article provides a comprehensive view of the current issues encountered in distributed IoT systems and effective solutions by classifying them into three main categories, namely, radio and computing resource management, intelligent edge-IoT systems, and flexible infrastructure management. Furthermore, an optimization framework for edge-IoT systems is proposed by considering the key performance metrics including throughput, delay, resource utilization and energy consumption. Finally, an ML based case study is presented along with some numerical results to illustrate the significance of ML in edge- IoT computing.