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Key Advances in Pervasive Edge Computing for Industrial Internet of Things in 5G and Beyond

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TLDR
This article surveys emerging technologies related to pervasive edge computing for industrial internet-of-things (IIoT) enabled by fifth-generation (5G) and beyond communication networks and reinforces the perspective that the PEC paradigm is an extremely suitable and important deployment model for industrial communication networks.
Abstract
This article surveys emerging technologies related to pervasive edge computing (PEC) for industrial internet-of-things (IIoT) enabled by fifth-generation (5G) and beyond communication networks. PEC encompasses all devices that are capable of performing computational tasks locally, including those at the edge of the core network (edge servers co-located with 5G base stations) and in the radio access network (sensors, actuators, etc.). The main advantages of this paradigm are core network offloading (and benefits therefrom) and low latency for delay-sensitive applications (e.g., automatic control). We have reviewed the state-of-the-art in the PEC paradigm and its applications to the IIoT domain, which have been enabled by the recent developments in 5G technology. We have classified and described three important research areas related to PEC—distributed artificial intelligence methods, energy efficiency, and cyber security. We have also identified the main open challenges that must be solved to have a scalable PEC-based IIoT network that operates efficiently under different conditions. By explaining the applications, challenges, and opportunities, our paper reinforces the perspective that the PEC paradigm is an extremely suitable and important deployment model for industrial communication networks, considering the modern trend toward private industrial 5G networks with local operations and flexible management.

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Surveying 5G Techno-Economic Research to Inform the Evaluation of 6G Wireless Technologies

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References
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Journal ArticleDOI

Edge Computing: Vision and Challenges

TL;DR: The definition of edge computing is introduced, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge Computing.
Posted Content

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Federated Machine Learning: Concept and Applications

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Federated Learning: Challenges, Methods, and Future Directions

TL;DR: In this paper, the authors discuss the unique characteristics and challenges of federated learning, provide a broad overview of current approaches, and outline several directions of future work that are relevant to a wide range of research communities.
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