Special Issue "Artificial Intelligence/Machine Learning in Wireless Communications and Networking"

A special issue of Telecom (ISSN 2673-4001).

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 9070

Special Issue Editors

School of Computer Science, Qufu Normal University, Rizhao 276827, China
Interests: big data; recommender system; service computing; privacy
Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad 826004, India
Interests: machine learning; big data; blockchain; cloud computing
Dr. Osamah Ibrahim
E-Mail Website
Guest Editor
Head of Energy Management Department, Al-Nahrain Nanorenewable Energy Research Center, AI-Nahrain University, Baghdad, Iraq
Interests: big data; computer networks

Special Issue Information

Dear Colleagues,

Breakthroughs in artificial intelligence (AI) and machine learning (ML), including deep neural networks and the availability of powerful computing platforms, have recently received much attention as a key enabler for future 5G and beyond wireless networks. The recent win of AlphaGo over the world champion Mr. Lee Sedol has demonstrated the power of AI/ML beyond what many of us can imagine. AI/ML has become one of the key technologies to realize intelligent mobile networks, intelligent services, and an intelligent Internet of Things (IoT). It can be found that AI/ML could provide many new opportunities in the way we manage and optimize wireless communications and networks, and the way we manage different user services and user content.

However, the evolution toward learning-based networks and communications is still in its early days, and much of the realization of the promised benefits requires thorough research and development. Fundamental questions such as where and how AI/ML can really complement the well-established, well-tested wireless communication systems still remain. Additionally, adaptation of AI/ML-based methods is likely needed to realize their full potential in the wireless context and networks. Moreover, research on security problems, hardware aspects, and network edge in wireless communications and networking is also necessary to establish quality-of-service guarantees that are common in communication system design.

The topics of interest include but are not limited to:

  • Advanced AI/ML algorithms for wireless networks;
  • AI/ML-based mobile networks design;
  • AI/ML-based energy efficient communication/networking techniques;
  • AI/ML-based sensor networks and IoT applications;
  • AI/ML-based network resource allocation and optimization;
  • AI/ML-based secure communications and networking;
  • AI/ML-based computing on network edge;
  • Service performance optimization in wireless networks;
  • Security, privacy, and trust in wireless networks;
  • The design of AI-enabled hardware aspects of wireless networks;
  • Distributed and decentralized signal processing via AI/ML algorithms.

Prof. Dr. Lianyong Qi
Dr. Dharavath Ramesh
Dr. Osamah Ibrahim
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. Telecom is an international peer-reviewed open access quarterly 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 1000 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
  • machine learning
  • security
  • privacy
  • trust
  • wireless networks

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 2205 KiB  
Article
Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty
Telecom 2023, 4(4), 678-692; https://doi.org/10.3390/telecom4040030 - 03 Oct 2023
Viewed by 1098
Abstract
In recent decades, wireless sensor networks (WSNs) have become a popular ambient sensing and model-based solution for various applications. WSNs are now achievable due to the developments of micro electro mechanical and semiconductors logic circuits with rising computational power and wireless communication technology. [...] Read more.
In recent decades, wireless sensor networks (WSNs) have become a popular ambient sensing and model-based solution for various applications. WSNs are now achievable due to the developments of micro electro mechanical and semiconductors logic circuits with rising computational power and wireless communication technology. The most difficult issues concerning WSNs are related to their energy consumption. Since communication typically requires a significant amount of energy, there are some techniques/ways to reduce energy consumption during the operation of the sensor’s communication systems. The topology control technique is one such effective method for reducing WSNs’ energy usage. A cluster head (CH) is usually selected using a topology control technique known as clustering to control the entire network. A single factor is inadequate for CH selection. Additionally, with the traditional clustering method, each round exhibits a new batch of head nodes. As a result, when using conventional techniques, nodes decay faster and require more energy. Furthermore, the inceptive energy of nodes, the range between sensor nodes and base stations, the size of data packets, voltage and transmission energy measurements, and other factors linked to sensor nodes are also completely unexpected due to irregular or hazardous natural circumstances. Here, unpredictability represented by Triangular Fuzzy Numbers (TFNs). The associated parameters of nodes were converted into crisp ones via the defuzzification of fuzzy numbers. The fuzzy number has been defuzzified using the well-known signed distance approach. Here, we have employed a multi-criteria decision-making (MCDM) approach to choosing the CHs depending on a bunch of characteristics of each node (i) residual energy, (ii) the number of neighbors, (iii) distance from the sink, (iv) average distance of cluster node, (v) distance ratio, and (vi) reliability. This study used the entropy-weighted Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) approach to select the CH in WSNs. For experiments, we have used the NSG2.1 simulator, and based on six characteristics comprising residual energy, number of neighbor nodes, distance from the sink or base station (BS), average distance of cluster nodes, distance ratio, and reliability, optimal CHs have been selected. Finally, experimental results have been presented and compared graphically with the existing literature. A statistical hypothesis test has also been conducted to verify the results that have been provided. Full article
Show Figures

Figure 1

35 pages, 3392 KiB  
Article
AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
Telecom 2023, 4(2), 334-368; https://doi.org/10.3390/telecom4020020 - 19 Jun 2023
Viewed by 1266
Abstract
Recent times have seen a significant rise in interest from mobile operators, vendors, and research projects toward achieving more energy-efficient and sustainable networks. Not surprisingly, it comes at a time when higher traffic demand and more stringent and diverse network requirements result in [...] Read more.
Recent times have seen a significant rise in interest from mobile operators, vendors, and research projects toward achieving more energy-efficient and sustainable networks. Not surprisingly, it comes at a time when higher traffic demand and more stringent and diverse network requirements result in diminishing benefits for operators using complex AI-driven network optimization solutions. In this paper, we propose the idea of tower companies that facilitate radio access network (RAN) infrastructure sharing between operators and evaluate the additional energy savings obtained in this process. In particular, we focus on the RAN-as-a-Service (RANaaS) implementation, wherein each operator leases and controls an independent logical RAN instance running on the shared infrastructure. We show how an AI system can assist operators in optimizing their share of resources under multiple constraints. This paper aims to provide a vision, a quantitative and qualitative analysis of the RANaaS paradigm, and its benefits in terms of energy efficiency. Through simulations, we show the possibility to achieve up to 75 percent energy savings per operator over 24 h compared to the scenario where none of the energy-saving features are activated. This is an additional 55 percent energy savings from sharing the RAN infrastructure compared to the baseline scenario where the operators use independent hardware. Full article
Show Figures

Figure 1

14 pages, 2877 KiB  
Article
Digital Twins: Enabling Interoperability in Smart Manufacturing Networks
Telecom 2023, 4(2), 265-278; https://doi.org/10.3390/telecom4020016 - 11 May 2023
Cited by 3 | Viewed by 2597
Abstract
As Industry 4.0 networks continue to evolve at a rapid pace, they are becoming increasingly complex and distributed. These networks incorporate a range of technologies that are integrated into smart manufacturing systems, requiring adaptability, security, and resilience. However, managing the complexity of Industry [...] Read more.
As Industry 4.0 networks continue to evolve at a rapid pace, they are becoming increasingly complex and distributed. These networks incorporate a range of technologies that are integrated into smart manufacturing systems, requiring adaptability, security, and resilience. However, managing the complexity of Industry 4.0 networks presents significant challenges, particularly in terms of security and the integration of diverse technologies into a functioning and efficient infrastructure. To address these challenges, emerging digital twin standards are enabling the connection of various systems by linking individual digital twins, creating a system of systems. The objective is to develop a “universal translator” that can interpret inputs from both the real and digital worlds, merging them into a seamless cyber-physical reality. It will be demonstrated how the myriad of technologies and systems in Industry 4.0 networks can be connected through the use of digital twins to create a seamless “system of systems”. This will improve interoperability, resilience, and security in smart manufacturing systems. The paper will also outline the potential benefits and limitations of digital twins in addressing the challenges of Industry 4.0 networks. Full article
Show Figures

Figure 1

17 pages, 4180 KiB  
Article
Design and Implementation of a Smart Intercom System through Web Services on Web of Things
Telecom 2022, 3(4), 675-691; https://doi.org/10.3390/telecom3040036 - 11 Nov 2022
Cited by 1 | Viewed by 3574
Abstract
In this paper, an embedded system is used as a host for the intercom and as a chatbot server for this system. The chatbot server controls door locks, cameras, buzzers, and related devices through web services on the WoT (Web of Things) to [...] Read more.
In this paper, an embedded system is used as a host for the intercom and as a chatbot server for this system. The chatbot server controls door locks, cameras, buzzers, and related devices through web services on the WoT (Web of Things) to provide residents and visitors with better functionality and integrational services. This system can greatly improve the security and convenience of the system compared with the traditional intercom system. The resident uses the instant messaging software of the smartphone to replace the handset function, and there is no need to install and learn new apps, reducing the cost of the handset and the wiring indoors and outdoors. Whether or not the residents are at home, they can check whether there are visitors and check the status of their doors through their smartphones. Conversely, any visitor can also contact the resident through this intercom, while there is no way to confirm whether the resident is at home or not, which enhances the security of the house. This system provides flexibility in wireless installation and use and sufficient mobility for residents. The system architecture strikes a good balance between user convenience and home security and between performance and cost, effectively improving home security and reducing costs. Full article
Show Figures

Figure 1

Back to TopTop