Artificial Intelligence (AI) and Big Data Technologies for Designing 6G Networks to Enable Future Networked Societies

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 5306

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, Islamic Azad University (IAU), Tehran, Iran
Interests: artificial intelligence (AI); machine learning (ML); deep learning (DL); Internet of Things (IoT); wireless networks; and digital transformation (DX); mostly focused on industrial; smart city and healthcare applications

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Guest Editor
Computer Science Faculty, Federal University of Pará, Belém 66075-110, Brazil
Interests: SDN; vehicular cloud; VANET
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The number of things connected to the internet are growing exponentially thanks to the technologies such as the Internet of Things (IoT). This has given rise to the rise of big data that is usually characterised by four or more Vs, volume, velocity, variety, and veracity. Computing resources have increasingly become larger in capacities, smaller in size, and cheaper in terms of cost, and this has facilitated the development of powerful artificial intelligence methods for automation, inference, decision making, autonomification (e.g., autonomous vehicles), etc. While management of big data poses major challenges, it offers unparalleled opportunities for fine-grained, dynamic, and real-time management of digital and physical infrastructures, made possible through fine-grained sensors to monitor environments, collect and process data using machine and deep learning, make and implement decisions, all within the time constraints. The fifth-Generation networks and wireless systems (5G) are gradually enabling ultrahigh definition multimedia communications without delays. However, the focus of 5G networks is mainly on providing high-speed communication with low latencies. Future networked societies require more than that, it requires a network infrastructure that is user and application-centric. The 6th Generation wireless systems (6G) promise to provide such network infrastructure that will intrinsically allow application and platform development and deployment, and dynamic network reconfigurability across cloud, fog, and edge layers based on requirements from various entities. The 6G wireless systems would provide next-generation connectivity for networked society using technologies including heterogeneous networks, advanced and dynamic reconfigurability, network softwarization, higher spectrum, and more. We call in this special issue for submissions related to artificial intelligence and big data technologies for designing 6G networks to enable future networked societies. The submissions could be on any aspect of networks, wireless systems, and future Internet design including the design of networked applications and platforms, as well as their economics and business models. The submissions can be research papers, position papers, or literature reviews. Contributions are welcome from engineers, scientists, academics, and practitioners regardless of their academic and industrial disciplines. Authors are welcome to contact the editors to discuss their planned submissions. 

The topics include, but are not limited, to the following.

  • Big Data and AI methods and technologies for 6G design;
  • Big Data and AI technologies for dynamic configurability of 6G networks;
  • Big Data and AI technologies for softwarization in 6G;
  • Big Data and AI methods for higher speeds in 6G;
  • Big Data and AI technologies for provisioning higher capacities in 6G;
  • Big Data and AI methods for latency reduction in 6G;
  • Big Data and AI methods and technologies for heterogeneity in 6G;
  • Big Data and AI methods and technologies for higher reliability in 6G;
  • Big Data and AI methods and technologies for higher efficiencies in 6G;
  • Big Data and AI methods for green 6G (lower energy consumption, etc.);
  • Edge, fog and cloud computing in 6G;
  • Big Data and AI technologies for designing or deploying networked applications;
  • Internet of Things (IoT) over 6G;
  • Designing or deploying healthcare applications over 6G;
  • Designing or deploying transportation applications over 6G;
  • Designing or deploying smart city and society applications over 6G;
  • Designing or deploying Industry 4.0 applications over 6G;
  • Designing or deploying digital twins, metaverse or other applications over 6G;
  • Evaluation and benchmark methods for the design of 6G networks;
  • Evaluation and benchmark methods for supported applications and platforms over 6G;
  • Big Data and AI technologies to reduce infrastructural and operational costs of 6G networks;
  • Security and privacy of 6G networks and systems;
  • Economics and business models for 6G design, deployment and operations. 

Prof. Dr. Rashid Mehmood
Dr. Mohsen Maadani
Prof. Dr. Eduardo Cerqueira
Prof. Dr. Gyu Myoung Lee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence
  • Big Data
  • 6G
  • Internet of Things
  • wireless systems
  • future Internet design

Published Papers (1 paper)

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Research

16 pages, 2242 KiB  
Article
Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
by Saleh M. Altowaijri
Future Internet 2022, 14(2), 35; https://doi.org/10.3390/fi14020035 - 21 Jan 2022
Cited by 22 | Viewed by 3899
Abstract
The Internet of Things (IoT) paradigm allows the integration of cyber and physical worlds and other emerging technologies. IoT-enabled wireless sensor networks (WSNs) are rapidly gaining interest due to their ability to aggregate sensing data and transmit it towards the central or intermediate [...] Read more.
The Internet of Things (IoT) paradigm allows the integration of cyber and physical worlds and other emerging technologies. IoT-enabled wireless sensor networks (WSNs) are rapidly gaining interest due to their ability to aggregate sensing data and transmit it towards the central or intermediate repositories, such as computational clouds and fogs. This paper presents an efficient multi-hop routing protocol (EMRP) for efficient data dissemination in IoT-enabled WSNs where hierarchy-based energy-efficient routing is involved. It considers a rank-based next-hop selection mechanism. For each device, it considers the residual energy to choose the route for data exchange. We extracted the residual energy at each node and evaluated it based on the connection degree to validate the maximum rank. It allowed us to identify the time slots for measuring the lifetime of the network. We also considered the battery expiry time of the first node to identify the network expiry time. We validated our work through extensive simulations using Network Simulator. We also implemented TCL scripts and C language code to configure low-power sensing devices, cluster heads and sink nodes. We extracted results from the trace files by utilizing AWK scripts. Results demonstrate that the proposed EMRP outperforms the existing related schemes in terms of the average lifetime, packet delivery ratio, time-slots, communication lost, communication area, first node expiry, number of alive nodes and residual energy. Full article
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