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The rapid deployment of Internet of Things (IoT) applications leads to massive data that need to be processed. These IoT applications have specific communication requirements on latency and bandwidth, and present new features on their…
The ubiquity of smartphone cameras and IoT cameras, together with the recent boom of deep learning and deep neural networks, proliferate various computer vision driven mobile and IoT applications deployed on the edge. This paper focuses on…
Edge computing is the practice of placing computing resources at the edges of the Internet in close proximity to devices and information sources. This, much like a cache on a CPU, increases bandwidth and reduces latency for applications but…
Edge computing is an emerging concept based on distributing computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth generation (5G) wireless systems and beyond. While current…
Deploying deep neural networks (DNNs) on IoT and mobile devices is a challenging task due to their limited computational resources. Thus, demanding tasks are often entirely offloaded to edge servers which can accelerate inference, however,…
In the Internet of Things (IoT) environment, edge computing can be initiated at anytime and anywhere. However, in an IoT, edge computing sessions are often ephemeral, i.e., they last for a short period of time and can often be discontinued…
Fog computing has become an attractive research topic in recent years. As an extension of the cloud, fog computing provides computing resources for Internet of Things (IoT) applications through communicative fog nodes located at the network…
The rapid technological advances in the Internet of Things (IoT) allows the blueprint of Smart Cities to become feasible by integrating heterogeneous cloud/fog/edge computing paradigms to collaboratively provide variant smart services in…
The ever-increasing number of Internet of Things (IoT) devices has created a new computing paradigm, called edge computing, where most of the computations are performed at the edge devices, rather than on centralized servers. An edge device…
Edge computing has become a popular paradigm where services and applications are deployed at the network edge closer to the data sources. It provides applications with outstanding benefits, including reduced response latency and enhanced…
In edge computing deployments, where devices may be in close proximity to each other, these devices may offload similar computational tasks (i.e., tasks with similar input data for the same edge computing service or for services of the same…
In recent years, there is an emerging trend that some computing services are moving from cloud to the edge of the networks. Compared to cloud computing, edge computing can provide services with faster response, lower expense, and more…
Edge computing seeks to enable applications with strict latency requirements by utilizing compute resources deployed closer to the users. The diverse, dynamic, and constrained nature of edge infrastructures necessitates a flexible…
The increased use of Internet of Things (IoT) devices -- from basic sensors to robust embedded computers -- has boosted the demand for information processing and storing solutions closer to these devices. Edge computing has been established…
An increasing amount of data is being injected into the network from IoT (Internet of Things) applications. Many of these applications, developed to improve society's quality of life, are latency-critical and inject large amounts of data…
Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are…
This project aims to study the feasibility and cost-effectiveness of using edge computing for stream data processing in the context of Internet of Things (IoT) in manufacturing in Europe. Two scenarios were considered: using edge computing…
Whilst computational resources at the cloud edge can be leveraged to improve latency and reduce the costs of cloud services for a wide variety mobile, web, and IoT applications; such resources are naturally constrained. For distributed…
With growing deployment of Internet of Things (IoT) and machine learning (ML) applications, which need to leverage computation on edge and cloud resources, it is important to develop algorithms and tools to place these distributed…
The paper presents an efficient real-time scheduling algorithm for intelligent real-time edge services, defined as those that perform machine intelligence tasks, such as voice recognition, LIDAR processing, or machine vision, on behalf of…