Emerging Edge Computing Technologies for Distributed IoT Systems

Abstract

The ever-increasing growth in the number of connected smart devices and various Internet of Things (IoT) verticals is leading to a crucial challenge of handling massive amount of raw data generated from distributed IoT systems and providing real-time feedback to the end-users. Although existing cloudcomputing paradigm has an enormous amount of virtual computing power and storage capacity, it is not suitable for latency-sensitive applications and distributed systems due to the involved latency and its centralized mode of operation. To this end, edge/fog computing has recently emerged as the next generation of computing systems for extending cloud-computing functions to the edges of the network.Despite several benefits of edge computing such as geo-distribution, mobility support and location awareness, various communication and computing related challenges need to be addressed in realizing edge computing technologies for future IoT systems. In this regard, this paper provides a holistic view on the current issues and effective solutions by classifying the emerging technologies in regard to the joint coordination of radio and computing resources, system optimization and intelligent resource management. Furthermore, an optimization framework for edge-IoT systems is proposed to enhance various performance metrics such as throughput, delay, resource utilization and energy consumption.Finally, a Machine Learning (ML) based case study is presented along with some numerical results to illustrate the significance of edge computing.

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“…However, this cloud-side processing is not suitable for the applications demanding low-latency and high QoS [34]. On the other hand, edge/fog computing at the devices/aggregators/access points can support the applications/services demanding lowlatency, location-awareness, high mobility and high QoS [103]. In this regard, collaborative edge/cloud processing [34], which can utilize the benefits of both the computing/processing paradigms, seems one promising enabler for wireless VR applications.…”

“…Edge/fog computing is considered as a promising paradigm to extend the cloud functionalities (computing, data storage, communication, and networking) to the edge of the network; and it is significantly useful to support lowlatency, mobility, location-awareness and geo-distributed applications [34], [103]. A fog computing platform can address various security and privacy issues in IoT networks including TI applications in various ways [206]: (i) Fog nodes can be useful to carry out cryptographic computations, which are not possible to perform at the IoT devices/sensors due the constraints on the required resources, (ii) Fog computing helps to preserve the privacy in IoT networks by minimizing the need of sending sensitive data to the cloud, (iii) Fog nodes can be used to identify vulnerabilities in IoT devices and to supply the required security updates to keep them secured, and (iv) Fog nodes may employ a a lightweight encryption algorithm for the efficient authentication of IoT devices.…”

“…One promising direction to manage the distributed resources is to employ edge/fog computing as well as the cooperation of distributed edge computing nodes having different levels of energy and computing resources [103]. In such distributed edge computing based TI systems, several challenges including the energy efficiency and computational efficiency of edge nodes, energy-efficient task offloading and service response time of the endusers, need to be addressed [221].…”

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“…NFV implements hardware functions within software applications, resulting in more efficient resource management. In recent years, edge/fog computing technology has been rapidly evolving, as an extension of cloud functionality at the ends of the network [47,48]. Areas of application include geo-distribution, mobility and localization support, and so forth.…”

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“…According to the IHS Markit perspective [1], the number of Internet of Things (IoT) devices is expected to reach 125 billion by 2030. Those IoT has attracted lots of attention in the industry and academia because they can be widely used in many applications [2].…”