Edge Computing for Internet of Things

The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond

The edge cloud: A holistic view of communication, computation and caching

The evolution of communication networks shows a clear shift of focus from just improving the communications aspects to enabling new important services, from Industry 4.0 to automated driving, virtual/augmented reality, Internet of Things (IoT), and so on. This trend is evident in the roadmap planned for the deployment of the fifth generation (5G) communication networks. This ambitious goal requires a paradigm shift towards a vision that looks at communication, computation and caching (3C) resources as three components of a single holistic system. The further step is to bring these 3C resources closer to the mobile user, at the edge of the network, to enable very low latency and high reliability services. The scope of this chapter is to show that signal processing techniques can play a key role in this new vision. In particular, we motivate the joint optimization of 3C resources. Then we show how graph-based representations can play a key role in building effective learning methods and devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing: Principles and Applications", P. Djuric and C. Richard Eds., Academic Press, Elsevier, 201

The Impact of Encoding and Transport for Massive Real-time IoT Data on Edge Resource Consumption

Edge microservice applications are becoming a viable solution for the execution of real-time IoT analytics, due to their rapid response and reduced latency. With Edge Computing, unlike the central Cloud, the amount of available resource is constrained and the computation that can be undertaken is also limited. Microservices are not standalone, they are devised as a set of cooperating tasks that are fed data over the network through specific APIs. The cost of processing these feeds of data in real-time, especially for massive IoT configurations, is however generally overlooked. In this work we evaluate the cost of dealing with thousands of sensors sending data to the edge with the commonly used encoding of JSON over REST interfaces, and compare this to other mechanisms that use binary encodings as well as streaming interfaces. The choice has a big impact on the microservice implementation, as a wrong selection can lead to excessive resource consumption, because using a less efficient encoding and transport mechanism results in much higher resource requirements, even to do an identical job

V-Edge: Virtual Edge Computing as an Enabler for Novel Microservices and Cooperative Computing

As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: Instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of kilometers and introducing delays lower-bounded by propagation speed, edge servers deployed in close proximity to the user (e.g., at some base station) serve as proxy for the cloud. This is particularly interesting for upcoming machine-learning-based intelligent services, which require substantial computational and networking performance for continuous model training. However, this promising idea is hampered by the limited number of such edge servers. In this article, we discuss a way forward, namely the V-Edge concept. V-Edge helps bridge the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available. Thus, V-Edge acts as an enabler for novel microservices as well as cooperative computing solutions in next-generation networks. We introduce the general V-Edge architecture, and we characterize some of the key research challenges to overcome in order to enable wide-spread and intelligent edge services

The Impact of Encoding and Transport for Massive Real-time IoT Data on Edge Resource Consumption

Edge microservice applications are becoming a viable solution for the execution of real-time IoT analytics, due to their rapid response and reduced latency. With Edge Computing, unlike the central Cloud, the amount of available resource is constrained and the computation that can be undertaken is also limited. Microservices are not standalone, they are devised as a set of cooperating tasks that are fed data over the network through specific APIs. The cost of processing these feeds of data in real-time, especially for massive IoT configurations, is however generally overlooked. In this work we evaluate the cost of dealing with thousands of sensors sending data to the edge with the commonly used encoding of JSON over REST interfaces, and compare this to other mechanisms that use binary encodings as well as streaming interfaces. The choice has a big impact on the microservice implementation, as a wrong selection can lead to excessive resource consumption, because using a less efficient encoding and transport mechanism results in much higher resource requirements, even to do an identical job