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The Internet of senses: building on semantic communications and edge intelligence

The Internet of senses: building on semantic communications and edge intelligence
The Internet of senses: building on semantic communications and edge intelligence
The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human ‘receptors’ and therefore blurs the difference of virtual and real environments. We commence by highlighting the compelling use cases empowered by the IoS and also the key network requirements. We then elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms along with 6G technologies may satisfy the requirements of IoS use cases. On one hand, semantic communications can be applied for extracting meaningful and significant information and hence efficiently exploit the resources and for harnessing a priori information at the receiver to satisfy IoS requirements. On the other hand, AI/ML facilitates frugal network resource management by making use of the enormous amount of data generated in IoS edge nodes and devices, as well as by optimizing the IoS performance via intelligent agents. However, the intelligent agents deployed at the edge are not completely aware of each others’ decisions and the environments of each other, hence they operate in a partially rather than fully observable environment. Therefore, we present a case study of Partially Observable Markov Decision Processes (POMDP) for improving the User Equipment (UE) throughput and energy consumption, as they are imperative for IoS use cases, using Reinforcement Learning for astutely activating and deactivating the component carriers in carrier aggregation. Finally, we outline the challenges and open issues of IoS implementations and employing semantic communications, edge intelligence as well as learning under partial observability in the IoS context.
0890-8044
Joda, Roghayeh
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Elsayed, Medhat
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Abou-zeid, Hatem
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Atawia, Ramy
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Sediq, Akram Bin
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Boudreau, Gary
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Erol-Kantarci, Melike
a369a49c-ce8b-4058-b6e8-4cf5eb9cf1ec
Hanzo, Lajos
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Joda, Roghayeh
c4b2f0da-b2ce-48f9-981e-51cfa81b7bb6
Elsayed, Medhat
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Abou-zeid, Hatem
d3556d93-766f-4a7b-b0ad-a9de46b29fbe
Atawia, Ramy
8995891a-e80e-4a4e-907d-85160c4333bb
Sediq, Akram Bin
07c76a1d-c751-473a-987c-a1a98e7bbd7b
Boudreau, Gary
6168abd4-e1a1-4ba5-aec6-abad1f0d1170
Erol-Kantarci, Melike
a369a49c-ce8b-4058-b6e8-4cf5eb9cf1ec
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Joda, Roghayeh, Elsayed, Medhat, Abou-zeid, Hatem, Atawia, Ramy, Sediq, Akram Bin, Boudreau, Gary, Erol-Kantarci, Melike and Hanzo, Lajos (2022) The Internet of senses: building on semantic communications and edge intelligence. IEEE Network. (doi:10.1109/MNET.107.2100627).

Record type: Article

Abstract

The Internet of Senses (IoS) holds the promise of flawless telepresence-style communication for all human ‘receptors’ and therefore blurs the difference of virtual and real environments. We commence by highlighting the compelling use cases empowered by the IoS and also the key network requirements. We then elaborate on how the emerging semantic communications and Artificial Intelligence (AI)/Machine Learning (ML) paradigms along with 6G technologies may satisfy the requirements of IoS use cases. On one hand, semantic communications can be applied for extracting meaningful and significant information and hence efficiently exploit the resources and for harnessing a priori information at the receiver to satisfy IoS requirements. On the other hand, AI/ML facilitates frugal network resource management by making use of the enormous amount of data generated in IoS edge nodes and devices, as well as by optimizing the IoS performance via intelligent agents. However, the intelligent agents deployed at the edge are not completely aware of each others’ decisions and the environments of each other, hence they operate in a partially rather than fully observable environment. Therefore, we present a case study of Partially Observable Markov Decision Processes (POMDP) for improving the User Equipment (UE) throughput and energy consumption, as they are imperative for IoS use cases, using Reinforcement Learning for astutely activating and deactivating the component carriers in carrier aggregation. Finally, we outline the challenges and open issues of IoS implementations and employing semantic communications, edge intelligence as well as learning under partial observability in the IoS context.

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Accepted/In Press date: 19 December 2022
e-pub ahead of print date: 26 December 2022

Identifiers

Local EPrints ID: 473725
URI: http://eprints.soton.ac.uk/id/eprint/473725
ISSN: 0890-8044
PURE UUID: b47a39dd-47f8-40d0-a227-4159595e535d
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 30 Jan 2023 19:29
Last modified: 18 Mar 2024 02:36

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Contributors

Author: Roghayeh Joda
Author: Medhat Elsayed
Author: Hatem Abou-zeid
Author: Ramy Atawia
Author: Akram Bin Sediq
Author: Gary Boudreau
Author: Melike Erol-Kantarci
Author: Lajos Hanzo ORCID iD

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