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Wireless Edge Computing With Latency and Reliability Guarantees
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| Pengarang | : | Mohammed S. Elbamby |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 107 (No. 8) |
| Halaman | : | 1717-1737 |
| Abstrak | : | Edge computing is an emerging concept based on distributed 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 the current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This paper will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultrareliable and low-latency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so on. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work. |
Edge Computing for Autonomous Driving: Opportunities and Challenges
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 107 (No. 8) |
| Halaman | : | 1697-1716 |
| Abstrak | : | Safety is the most important requirement for autonomous vehicles; hence, the ultimate challenge of designing an edge computing ecosystem for autonomous vehicles is to deliver enough computing power, redundancy, and security so as to guarantee the safety of autonomous vehicles. Specifically, autonomous driving systems are extremely complex; they tightly integrate many technologies, including sensing, localization, perception, decision making, as well as the smooth interactions with cloud platforms for high-definition (HD) map generation and data storage. These complexities impose numerous challenges for the design of autonomous driving edge computing systems. First, edge computing systems for autonomous driving need to process an enormous amount of data in real time, and often the incoming data from different sensors are highly heterogeneous. Since autonomous driving edge computing systems are mobile, they often have very strict energy consumption restrictions. Thus, it is imperative to deliver sufficient computing power with reasonable energy consumption, to guarantee the safety of autonomous vehicles, even at high speed. Second, in addition to the edge system design, vehicle-to-everything (V2X) provides redundancy for autonomous driving workloads and alleviates stringent performance and energy constraints on the edge side. With V2X, more research is required to define how vehicles cooperate with each other and the infrastructure. Last, safety cannot be guaranteed when security is compromised. Thus, protecting autonomous driving edge computing systems against attacks at different layers of the sensing and computing stack is of paramount concern. In this paper, we review state-of-the-art approaches in these areas as well as explore potential solutions to address these challenges. |
Edge Video Analytics for Public Safety: A Review
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 107 (No. 8) |
| Halaman | : | 1675-1696 |
| Abstrak | : | With the installation of enormous public safety and transportation infrastructure cameras, video analytics has come to play an essential part in public safety. Typically, video analytics is to collectively leverage the advanced computer vision (CV) and artificial intelligence (AI) to solve the four-W problem. That is to identify Who has done something (What) at a specific place (Where) at some time (When). According to the difference of latency requirements, video analytics can be applied to postevent retrospective analysis, such as archive management, search, forensic investigation and real-time live video stream analysis, such as situation awareness, alerting, and interested object (criminal suspect/missing vehicle) detection. The latter is characterized as having higher requirements on hardware resources as the sophisticated image processing algorithms under the hood. However, analyzing large-scale live video streams on the Cloud is impractical as the edge solution that conducts the video analytics on (or close to) the camera provides a silvering light. Analyzing live video streams on the edge is not trivial due to the constrained hardware resources on edge. The AI-dominated video analytics requires higher bandwidth, consumes considerable CPU/GPU resources for processing, and demands larger memory for caching. In this paper, we review the applications, algorithms, and solutions that have been proposed recently to facilitate edge video analytics for public safety. |
Deep Learning With Edge Computing: A Review
| Pengarang | : | Jiasi Chen |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 107 (No. 8) |
| Halaman | : | 1655-1674 |
| Abstrak | : | Deep learning is currently widely used in a variety of applications, including computer vision and natural language processing. End devices, such as smartphones and Internet-of-Things sensors, are generating data that need to be analyzed in real time using deep learning or used to train deep learning models. However, deep learning inference and training require substantial computation resources to run quickly. Edge computing, where a fine mesh of compute nodes are placed close to end devices, is a viable way to meet the high computation and low-latency requirements of deep learning on edge devices and also provides additional benefits in terms of privacy, bandwidth efficiency, and scalability. This paper aims to provide a comprehensive review of the current state of the art at the intersection of deep learning and edge computing. Specifically, it will provide an overview of applications where deep learning is used at the network edge, discuss various approaches for quickly executing deep learning inference across a combination of end devices, edge servers, and the cloud, and describe the methods for training deep learning models across multiple edge devices. It will also discuss open challenges in terms of systems performance, network technologies and management, benchmarks, and privacy. The reader will take away the following concepts from this paper: understanding scenarios where deep learning at the network edge can be useful, understanding common techniques for speeding up deep learning inference and performing distributed training on edge devices, and understanding recent trends and opportunities. |
Privacy Techniques for Edge Computing Systems
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 107 (No. 8) |
| Halaman | : | 1632-1654 |
| Abstrak | : | In an edge-enabled data management and computing environment, it is critical to ensure the privacy of the information acquired, processed, and exchanged among the different parties. The problem is complex because of the large scale, mobility, device, and protocol heterogeneity. Also, unlike in conventional environments, communication may be fragmented and portions of the environment can be physically unprotected. To date, there are several privacy-enhancing techniques, such as secure multiparty computation techniques, private information retrieval (PIR), and data sanitization techniques. However, there is not a single technique that works for all possible uses of the data in edge systems. In addition, these techniques are computationally expensive and thus may not be suitable for edge devices. In this paper, we first cover basic privacy building blocks, including differential privacy and homomorphic encryption. We then discuss privacy solutions specific to three different types of data use that are relevant for edge-based applications: data aggregation techniques, point-of-interest (POI) services and traffic information services, and crowdsourcing. These applications have been selected as they provide a broad spectrum of edge computing applications. Throughout this paper, we outline open research directions. |
Edge Computing Security: State of the Art and Challenges
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 107 (No. 8) |
| Halaman | : | 1608-1631 |
| Abstrak | : | The rapid developments of the Internet of Things (IoT) and smart mobile devices in recent years have been dramatically incentivizing the advancement of edge computing. On the one hand, edge computing has provided a great assistance for lightweight devices to accomplish complicated tasks in an efficient way; on the other hand, its hasty development leads to the neglection of security threats to a large extent in edge computing platforms and their enabled applications. In this paper, we provide a comprehensive survey on the most influential and basic attacks as well as the corresponding defense mechanisms that have edge computing specific characteristics and can be practically applied to real-world edge computing systems. More specifically, we focus on the following four types of attacks that account for 82% of the edge computing attacks recently reported by Statista: distributed denial of service attacks, side-channel attacks, malware injection attacks, and authentication and authorization attacks. We also analyze the root causes of these attacks, present the status quo and grand challenges in edge computing security, and propose future research directions. |
Computation Offloading Toward Edge Computing
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 107 (No. 8) |
| Halaman | : | 1584-1607 |
| Abstrak | : | We are living in a world where massive end devices perform computing everywhere and everyday. However, these devices are constrained by the battery and computational resources. With the increasing number of intelligent applications (e.g., augmented reality and face recognition) that require much more computational power, they shift to perform computation offloading to the cloud, known as mobile cloud computing (MCC). Unfortunately, the cloud is usually far away from end devices, leading to a high latency as well as the bad quality of experience (QoE) for latency-sensitive applications. In this context, the emergence of edge computing is no coincidence. Edge computing extends the cloud to the edge of the network, close to end users, bringing ultra-low latency and high bandwidth. Consequently, there is a trend of computation offloading toward edge computing. In this paper, we provide a comprehensive perspective on this trend. First, we give an insight into the architecture refactoring in edge computing. Based on that insight, this paper reviews the state-of-the-art research on computation offloading in terms of application partitioning, task allocation, resource management, and distributed execution, with highlighting features for edge computing. Then, we illustrate some disruptive application scenarios that we envision as critical drivers for the flourish of edge computing, such as real-time video analytics, smart “things” (e.g., smart city and smart home), vehicle applications, and cloud gaming. Finally, we discuss the opportunities and future research directions. |
Ecosystem of Things: Hardware, Software, and Architecture
| Pengarang | : | Lu Chao |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 107 (No. 8) |
| Halaman | : | 1563-1583 |
| Abstrak | : | Edge computing is a continuum that includes the computing resources from cloud to things. Ecosystem of things (EoT) is a subsystem of the ecosystem of edge computing, which potentially contains trillions of devices of things and directly interacts with the physical world. This paper surveys the state of the art of EoT by focusing on the computing infrastructure aspect with a forward-looking perspective. We point out a trend of smart edge computing with four types of smartness and intelligence. We address three fundamental questions. 1) What capabilities and how much energy efficiency are the hardware providing? What is the future growth potential? 2) What abstractions are provided by the system software? Are they adequate to support smart edge computing? 3) What ecosystem architectures have been proposed for the coordination of things, the edge, and the cloud? Are they meeting the needs to encourage innovation but avoid unnecessary ecosystem fragmentation? We examine advances from both industry and academia, including research results, visions, and project concepts. We also point out future research directions. |
A Survey on Edge Computing Systems and Tools
| Pengarang | : | Fang Liu |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 107 (No. 8) |
| Halaman | : | 1537-1562 |
| Abstrak | : | Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage, and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. At present, the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open-source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this paper. |
Dependable Resource Coordination on the Edge at Runtime
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Proceedings of the IEEE |
| Volume / Edisi | : | 107 (No. 8) |
| Halaman | : | 1520-1536 |
| Abstrak | : | Software components within heterogeneous devices of the Internet of Things (IoT) systems use resources representing various computational capabilities, including sensing or actuation end points. However, components do not live in isolation and must be able to coordinate with others to fulfill their goals. Satisfaction of requirements-capturing their goals-must persist in environments that are changing, unpredictable, and potentially unknown at system design time. Edge computers placed near IoT devices can be leveraged for this sort of control-providing resource management for end devices within their operational context. We propose a methodology and technical framework for engineering resource coordination at runtime, tailored for the decentralized, pervasive systems of today. Our approach represents a paradigm shift in marrying distributed systems and formal aspects of software engineering. We adopt goal modeling to capture objectives within the system and use bounded model checking as the foundational technique to compute coordination plans that satisfy device goals. This occurs opportunistically at runtime without any knowledge about the operational status or presence of resources, but always in accordance with the edge's own goals. Our technical framework exhibits dependability guarantees regarding optimality and correctness of generated plans. We evaluate the resource coordination performance and its feasibility on low-powered ARM-based edge devices. |