Edge computing

About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.

Edge Machine Learning: Enabling Smart Internet of Things Applications

Abstract: Machine learning has traditionally been solely performed on servers and high-performance machines. However, advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gap between the simple processors embedded in such things and their more complex cousins in personal computers. Thus, with the current advancement in these devices, in terms of processing power, energy storage and memory capacity, the opportunity has arisen to extract great value in having on-device machine learning for Internet of Things (IoT) devices. Implementing machine learning inference on edge devices has huge potential and is still in its early stages. However, it is already more powerful than most realise. In this paper, a step forward has been taken to understand the feasibility of running machine learning algorithms, both training and inference, on a Raspberry Pi, an embedded version of the Android operating system designed for IoT device development. Three different algorithms: Random Forests, Support Vector Machine (SVM) and Multi-Layer Perceptron, respectively, have been tested using ten diverse data sets on the Raspberry Pi to profile their performance in terms of speed (training and inference), accuracy, and power consumption. As a result of the conducted tests, the SVM algorithm proved to be slightly faster in inference and more efficient in power consumption, but the Random Forest algorithm exhibited the highest accuracy. In addition to the performance results, we will discuss their usability scenarios and the idea of implementing more complex and taxing algorithms such as Deep Learning on these small devices in more details.

Distributed edge processing of internet of things device data in co-location facilities

Abstract: Techniques are described for distributed processing of Internet of Things (IoT) device data by edge systems co-located within a globally-distributed set of co-location facilities deployed and managed by a co-location facility provider. For example, a method includes selecting, by at least one of a plurality of edge computing systems co-located within respective co-location facilities each deployed and managed by a single co-location facility provider, a selected edge computing system of the plurality of edge computing systems to process data associated with events generated by an IoT device. The method also includes provisioning, at the selected edge computing system, an application programming interface (API) endpoint for communication with the IoT device, receiving, by the selected edge computing system at the endpoint, the data associated with the events generated by the IoT device, and processing, by the selected edge computing system, the data associated with the events generated by the IoT device.

A Fog based system model for cooperative IoT node pairing using matching theory

Abstract: The revolutionized vision of IoT has united heterogeneous devices to foster the systems of cohesive intelligent things In addition, Fog computing has also envisioned a new form of cloud computing paradigm Therefore, Fog provides edge computing to such IoT devices with varied capabilities and resources However, a balanced and efficient pairing or matching strategy for edge IoT nodes is crucial to achieve the user requisite Hence, this paper addresses the utility based matching or pairing problem within the same domain of IoT nodes by using Irving's matching algorithm under the node specified preferences to endure a stable IoT node pairing We studied the performance of the proposed matching algorithm through simulation The simulation results show the higher utility gain of the node pairs through refined matching algorithm over greedy approach

When deep learning meets edge computing

Abstract: The state-of-the-art cloud computing platforms are facing challenges, such as the high volume of crowdsourced data traffic and highly computational demands, involved in typical deep learning applications. More recently, Edge Computing has been recently proposed as an effective way to reduce the resource consumption. In this paper, we propose an edge learning framework by introducing the concept of edge computing and demonstrate the superiority of our framework on reducing the network traffic and running time.

Adaptive Service Offloading for Revenue Maximization in Mobile Edge Computing With Delay-Constraint

Abstract: Mobile edge computing (MEC) is an important and effective platform to offload the computational services of modern mobile applications, and has gained tremendous attention from various research communities. For delay and resource constrained mobile devices, the important issues include: 1) minimization of the service latency; 2) optimal revenue maximization; and 3) high quality-of-service requirement to offload the computational service offloading. To address the above issues, an adaptive service offloading scheme is designed to provide the maximum revenue and service utilization to MEC. Unlike most of the existing works, we consider both the delay-tolerant and delay-constraint services in order to achieve the optimized service latency and revenue. Furthermore, we consider the different priorities to prioritize the edge services for optimal service offloading. We formulate the proposed scheme mathematically. Simulation results are presented to demonstrate the effectiveness of the proposed adaptive service offloading scheme over other existing state-of-the-art solutions, in terms of service latency, utility value, revenue, and utilization.