Supporting Next-Generation Network Management with Intelligent Moving Devices

Document Type

Article

Source of Publication

IEEE Network

Publication Date

1-1-2022

Abstract

The concept of fixed infrastructures capable of fulfilling the requirements of moving devices in terms of connectivity and reliability has been the optimal solution for the past few decades. Today, such a solution is no longer feasible in the Internet of Things (IoT) era. All things are now connected, and a significant number of them are mobile, hence leading to connectivity and reliability issues. Connected and autonomous vehicles, in addition to more contemporary flying and moving devices such as unmanned aerial vehicles (UAVs) and IoT devices, will play a significant role in next-generation networks (NGNs). Node-to-node communication will also play a key role in NGNs and will provide alternative solutions toward connectivity in many complex environments for applications such as smart transportation. With that said, today's wide availability of smart moving devices provides a wider set of alternatives to autonomy for NGNs. In this article, we discuss some of the existing solutions that use connected vehicles, UAVs, and other moving intelligent devices to not only provide connectivity support, but also perform on-location data collection, anal-ysis, and decision making to enable the management of moving NGNs for intelligent services and applications. We envision a solution that is capa-ble of adapting generalized and decentralized learning on mobile devices, such as federated learning, with the advances in deep learning to support the autonomy and configurability aspects of moving NGNs.

ISSN

0890-8044

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

36

Issue

3

First Page

8

Last Page

15

Disciplines

Computer Sciences

Keywords

Performance evaluation, Deep learning, Connected vehicles, 5G mobile communication, Mobile handsets, Smart transportation, Telecommunication network reliability, Intelligent systems, Next generation networking

Scopus ID

85135345897

Indexed in Scopus

yes

Open Access

no

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