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Article


Title

Special issue on Distributed Intelligence at the Edge for the Future Internet of Things

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2023

Published in

Journal of Parallel and Distributed Computing

Journal year: 2023 | Journal volume: vol. 171

Article type

editorial

Publication language

english

Keywords
EN
  • cloud computing
  • edge computing
  • artificial intelligence
Abstract

EN Recent years have witnessed the proliferation of mobile computing and Internet-of-Things (IoT), where billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Edge-Cloud Computing, a continuously emerging parallel & distributed computing paradigm, has received a tremendous amount of attention. By pushing data storage and computing closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability and energy efficiency. Edge intelligence, aiming to facilitate the deployment of neural networks on edge computing, has received significant attention, since hierarchical architecture of end devices proposes a possible solution to meet the high computation and low-latency requirement for the training and inference of AI algorithms. However, there are many challenges existing for novel designs of edge-cloud computing architectures for AI applications, and their co-optimization. On one hand, the high resource requirements of AI applications should be accommodated on a set of less powerful edge compute resources. Therefore, efficient, parallel & distributed and resource-conserving AI algorithms should be revisited in the edge-cloud computing environments . On the other hand, the system design should also support the efficient and scalable execution of AI algorithms, including efficient parallel & distributed execution mode, optimal scheduling strategies, etc.

Date of online publication

20.10.2022

Pages (from - to)

157 - 162

DOI

10.1016/j.jpdc.2022.09.014

URL

https://www.sciencedirect.com/science/article/pii/S074373152200209X?via%3Dihub

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