Edge computing

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

ssHealth: Toward Secure, Blockchain-Enabled Healthcare Systems

Abstract: The future of healthcare systems is being shaped by incorporating emerged technological innovations to drive new models for patient care. By acquiring, integrating, analyzing, and exchanging medical data at different system levels, new practices can be introduced, offering a radical improvement to healthcare services. This article presents a novel smart and secure Healthcare system (ssHealth), which, leveraging advances in edge computing and blockchain technologies, permits epidemics discovering, remote monitoring, and fast emergency response. The proposed system also allows for secure medical data exchange among local healthcare entities, thus realizing the integration of multiple national and international entities and enabling the correlation of critical medical events for, for example, emerging epidemics management and control. In particular, we develop a blockchain-based architecture and enable a flexible configuration thereof, which optimize medical data sharing between different health entities and fulfil the diverse levels of Quality of Service (QoS) that ssHealth may require. Finally, we highlight the benefits of the proposed ssHealth system and possible directions for future research.

Design of Distributed Cyber–Physical Systems for Connected and Automated Vehicles With Implementing Methodologies

Abstract: With the development of communication and control technology, intelligent transportation systems (ITS) have received increasing attention from both industry and academia. However, plenty of studies providing different formulations for ITS depend on Master Control Center and require a high level of hardware configuration. The systematized technologies for distributed architectures are still not explored in detail. In this paper, we proposed a novel distributed cyber–physical system for connected and automated vehicles, and related methodologies are illustrated. Every vehicle in this system is modeled as a double-integrator and supposed to travel along a desired trajectory for maintaining a rigid formation geometry. The desired trajectory is generated by reference leading vehicles using information from multiple sources, while ordinary following vehicles use velocity and position information from their nearest neighbors and sensor information from on-board sensors to correct their own performance. Information graphs are used to illustrate the interaction topology between connected and automated vehicles. Edge computing technology is used to analyze and process information, such that the risk of privacy leaks can be greatly reduced. The performance scaling laws for the network with a one-dimensional information graph are generalized to networks with D -dimensional information graphs, and the results of the experiments show that the performance of the connected and automated vehicles matches very well with analytic predictions. Some design guidelines and open questions are provided for the future study.

A Unified Trustworthy Environment Establishment Based on Edge Computing in Industrial IoT

Abstract: Under the combination of Internet-of-Things (IoT) technology and traditional industry, the Industrial IoT (IIoT) came into being and received wide attention from all walks of life. With the increase of the number of IIOT devices in industrial environments, security threats, and quality of service (QoS) issues increase drastically. Internal attack is one type of important security threat that makes service environment worse and less reliable. However, there is no unified and fine-grained trust evaluation mechanism to deal with the threats of internal attack and improve QoS of IIoT. To this end, a unified trustworthy environment based on edge computing is established and maintained, which can timely detect malicious service providers and service consumers, filter unreal information, and recommend credible service providers. Edge computing is introduced as an effective service access point, since it supports collecting service records to perform trust evaluations. Moreover, a service selection method is designed to choose the corresponding trustworthy and reliable service providers based on the trust evaluation and the recording criterion, which has distinctive advantages in the succinct trust management, convenient searching service, and accurate service matching. Experiments validated the feasibility of the proposed trustworthy environment.

DARE: Dynamic Adaptive Mobile Augmented Reality with Edge Computing

Abstract: Mobile augmented reality (MAR) is a killer application of mobile edge computing because of its high computation demand and stringent latency requirement. Since edge networks and computing resources are highly dynamic, handling such dynamics is essential for providing high-quality MAR services. In this paper, we design a new network protocol named DARE (dynamic adaptive AR over the edge) that enables mobile users to dynamically change their AR configurations according to wireless channel conditions and computation workloads in edge servers. The dynamic configuration adaptations reduce the service latency of MAR users and maximize the quality of augmentation (QoA) under varying network conditions and computation workloads. Considering the video frame size and computation model, i.e., object detection algorithms, as two key parameters in adapting the AR configuration, we develop analytical models to characterize the impact of these parameters on QoA and the service latency. Then, we design optimization mechanisms on both the edge server and AR devices to guide the AR configuration adaptation and server computation resource allocation. The performance of the DARE protocol is validated through a small-scale testbed implementation.

Real-Time Mask Identification for COVID-19: An Edge-Computing-Based Deep Learning Framework

Abstract: During the outbreak of the Coronavirus disease 2019 (COVID-19), while bringing various serious threats to the world, it reminds us that we need to take precautions to control the transmission of the virus. The rise of the Internet of Medical Things (IoMT) has made related data collection and processing, including healthcare monitoring systems, more convenient on the one hand, and requirements of public health prevention are also changing and more challengeable on the other hand. One of the most effective nonpharmaceutical medical intervention measures is mask wearing. Therefore, there is an urgent need for an automatic real-time mask detection method to help prevent the public epidemic. In this article, we put forward an edge computing-based mask (ECMask) identification framework to help public health precautions, which can ensure real-time performance on the low-power camera devices of buses. Our ECMask consists of three main stages: 1) video restoration; 2) face detection; and 3) mask identification. The related models are trained and evaluated on our bus drive monitoring data set and public data set. We construct extensive experiments to validate the good performance based on real video data, in consideration of detection accuracy and execution time efficiency of the whole video analysis, which have valuable application in COVID-19 prevention.