The Emergence of Edge Computing

By Mahadev Satyanarayanan. 2017

2,367 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Human-Computer Interaction

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Citations in the corpus

A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, background): “Different from cloud computing, edge computing [123-126] as a decentralized paradigm is proposed to support real-time services, location awareness and mobility, which is appropriate for various real-time application scenarios, such as smart gird and smart traffic [127], etc.”
SAND: Towards High-Performance Serverless Computing (2018, background): “In addition, the cloud industry is increasingly interested in moving infrastructure to the network edge to further reduce network latency to users [18, 26, 36, 50, 52].”
The Future of Healthcare Internet of Things: A Survey of Emerging Technologies (2020, background): “Apart from ensuring the functions mentioned above, edge computing enables optimizing the network performance along with ensuring network security [137].”
IoT in the Fog: A Roadmap for Data-Centric IoT Development (2018, methodology): “Earlier work by M. Satyanarayanan, overviewed in [1], presents a detailed account on the motivation behind cloud-let inherently more flexible, modular, and distributed in contrast to cloud platforms.”
Deep Learning With Edge Computing: A Review (2019, background): “strict end-to-end low-latency requirements needed for real time, interactive applications; for example, real experiments have shown that offloading a camera frame to an Amazon Web Services server and executing a computer vision task take more than 200-ms end-to-end [2].”
Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, methodology): “The concept of EC was borrowed from the cloud computing infrastructure to expand the concept of CDN [26].”
Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning (2018, background): “The tension between computation-intensive applications and resource-constrained mobile devices creates a bottleneck for having a satisfactory quality-of-service (QoS) and QoE, and is hence driving a revolution in computing infrastructure [2].”
A Review of Machine Learning and IoT in Smart Transportation (2019, background): “So, as the processing partially moves to the end network elements, a new computing model is introduced, called “edge computing” [5].”
Survey on Multi-Access Edge Computing for Internet of Things Realization (2018, background): “mented reality (AR) technologies thereby enabling humans to interact more naturally with the virtual worlds based on data aggregated by IoT devices [69].”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, methodology): “In Reference [129], the authors state that real-time cognitive assistance, e.g., face and speech recognition, can be the “killer app” for the cloudlet, which demands timely response to unobtrusively guide users’ attention.”
Machine Learning at the Network Edge: A Survey (2019, background): “Edge computing refers to computations being performed as close to data sources as possible, instead of on far-off, remote locations [93, 17].”
Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges (2019, methodology): “In this context, edge computing [4] as an extension of the cloud is introduced.”
A Survey on Security and Privacy Issues in Edge-Computing-Assisted Internet of Things (2020, background): “Although there is a tight overlap between EC and FC architectures [43], FC focuses more on the network infrastructure layer while EC focuses more on the things layer [44].”
Computation Offloading Toward Edge Computing (2019, background): “To crystallize the idea of edge computing, the ETSI launched an introductory technical white paper of MEC in 2014.”
Grape detection, segmentation and tracking using deep neural networks and three-dimensional association (2019, background): “Such vision systems can be easily integrated in tractors, implements, service vehicles, robots and UAVs, possibly employing high performance processing units (GPUs and TPUs) with low energy consumption or even edge computing (Satyanarayanan, 2017).”
An Overview on Edge Computing Research (2020, background): “…at Carnegie Mellon university in the United States, describes edge computing as: ‘‘Edge computing is a new computing model that deploys computing and storage resources (such as cloudlets, micro data centers, or fog nodes, etc.) at the edge of the network closer to mobile devices or sensors’’ [4].”
Orbital Edge Computing: Nanosatellite Constellations as a New Class of Computer System (2020, background): “cameras, lidar) proliferate, streaming all data to central cloud systems for processing becomes infeasible [78, 79, 97].”
Edge-Computing-Enabled Smart Cities: A Comprehensive Survey (2019, neutral): “All of these three terms used in the literature refer to edge computing [24], [38], [56].”