6G-enabled Edge Intelligence for Ultra -Reliable Low Latency Applications: Vision and Mission

Gupta, Rajesh; Reebadiya, Dakshita; Tanwar, Sudeep

doi:10.1016/j.csi.2021.103521

Cited by 90 publications

(50 citation statements)

References 48 publications

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“…Internet end-to-end latency between two endpoints consists of latencies incurred in the first mile, middle mile, and the last mile [ 14 ], all of which can be mitigated by utilizing the edge efficiently [ 1 ]. We build Viseu as an Internet-scale overlay network to minimize the end-to-end latency through its public blockchain architecture.…”

Section: Viseu Architecturementioning

confidence: 99%

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Towards an internet-scale overlay network for latency-aware decentralized workflows at the edge

et al. 2022

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Section: Viseu Architecturementioning

confidence: 99%

“…Future Internet, as well as 5G and 6G networks, aim to reduce Internet latency by leveraging innovations in networking research and edge environments [ 1 ]. Edge computing argues that proximity to the end-user plays a significant role in a higher Quality of Experience (QoE) of latency-sensitive services [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Towards an internet-scale overlay network for latency-aware decentralized workflows at the edge

et al. 2022

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“…The proliferation of devices ranging from simple sensors to sophisticated ones and the different network scenarios as cellular, vehicular, Wi-Fi, Internet of Things (IoT) and Internet of Everything (IoE) systems, shape a fully connected network of millions of people and billions of machines. In this context edge intelligence [16][17][18] will be a key enabling factor for future networks to improve performances, functions and services. In the edge application development the distributed and complex approach will be represented by the microservices able to develop modular lightweight application components.…”

Section: Beyond 5g Network Towards 6g Revolutionmentioning

confidence: 99%

“…The 6G network is expected to change the whole perspective of mobile communication networks by making the transformation from "connected things" or Internet of Things (IoT) or Internet from Everything to the "connected intelligence". Complex systems and Edge Intelligence (EI) [10,[16][17][18] powered by algorithms and techniques, such as machine learning, deep neural networks, diffusion dynamics and social network structure, are already being considered to be key missing elements in 5G networks. The upcoming 6G has the ambition to introduce heterogeneous and interconnected elements that can be represented by nodes of a complex network and can dynamically interact with each other as well as with their environment in an unpredictable and unplanned way.…”

Section: A Complex and Dynamical Approach For A Context-based Evaluation Modelmentioning

confidence: 99%

CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario

et al. 2021

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The forthcoming 6G will attempt to rewrite the communication networks’ perspective focusing on a shift in paradigm in the way technologies and services are conceived, integrated and used. In this viewpoint, the Internet of Medical Things (IoMT) represents a merger of medical devices and health applications that are connected through networks, introducing an important change in managing the disease, treatments and diagnosis, reducing costs and faults. In 6G, the edge intelligence moves the innovative abilities from the central cloud to the edge and jointly with the complex systems approach will enable the development of a new category of lightweight applications as microservices. It requires edge intelligence also for the service evaluation in order to introduce the same degree of adaptability. We propose a new evaluation model, called CoKnowEMe (context knowledge evaluation model), by introducing an architectural and analytical scheme, modeled following a complex and dynamical approach, consisting of three inter-operable level and different networked attributes, to quantify the quality of IoMT microservices depending on a changeable context of use. We conduct simulations to display and quantify the structural complex properties and performance statistical estimators. We select and classify suitable attributes through a further detailed procedure in a supplementary information document.

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“…The importance of AI for reliable edge networks is covered by Gupta et al [56], specifically arguing for the synergy of EI and next-generation 6G networks to enable advanced, low-latency, ultra-reliable applications (e.g. IoV, drones, holographic communication).…”

Section: Organizing the Edge Through Aimentioning

confidence: 99%

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

Goethals¹,

Volckaert

Turck

2021

IEEE Open J. Commun. Soc.

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The use of AI in Smart applications and in the organization of the network edge presents a rapidly advancing research field, with a great variety of challenges and opportunities. This article aims to provide a holistic review of studies from 2019 to 2021 related to the Intelligent Edge, a concept comprising both the use of AI to organize edge networks (Edge Intelligence) and Smart applications in the edge. An introduction is given to the technologies required to understand the state of the art of AI in edge networks, and a taxonomy is provided with "Enabling Technology" for Edge Intelligence, "Organization" of the edge using AI, and AI "Applications" in the edge as its main topics. Research trend data from 2015 to 2020 is presented for various subdivisions of these topics, showing both absolute and relative research interest in each subtopic. The "Organization" aspect, being the main focus of this article, has a more fine-grained subdivision, explaining all contributing factors in detail. The trends indicate an exponential increase in research interest in nearly all subtopics, but significant differences between them. For each subdivision of the taxonomy a number of selected studies from 2019 to 2021 are gathered to form a high-level illustration of the state of the art of Edge Intelligence. From these selected studies and the trend data, a number of short-term challenges and high-level visions for Edge Intelligence are formulated, providing a basis for future work.

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6G-enabled Edge Intelligence for Ultra -Reliable Low Latency Applications: Vision and Mission

Cited by 90 publications

References 48 publications

Towards an internet-scale overlay network for latency-aware decentralized workflows at the edge

Towards an internet-scale overlay network for latency-aware decentralized workflows at the edge

CoKnowEMe: An Edge Evaluation Scheme for QoS of IoMT Microservices in 6G Scenario

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

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