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Intelligent Systems, Communications, and Networks for the Next-Generation Internet of...
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Intelligent Systems, Communications, and Networks for the Next-Generation Internet of Things

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 4303

Special Issue Editors

Dr. Nhu-Ngoc Dao

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Interests: edge computing; network virtualization; intelligent communication; Intelligence of Things; B5G/6G
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sungrae Cho

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Chung-Ang University, Seoul 06974, Korea
Interests: queuing system; wireless networking; ubiquitous computing; ICT convergence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world has recently witnessed significant success of the Internet of Things (IoT) paradigm, where the problem of communication and resource constraints have been addressed to provide IoT devices and services with Internet connectivity. For the next-generation IoT, enabling the intelligence in IoT systems, communications, and networks is expected to efficiently accelerate and popularize the utilization of IoT technology in our daily life activities. In particular, the macro shift of computing capability from the cloud to the edge allows various machine-learning algorithms to be appropriately deployed in the proximity of IoT devices such as federated learning, split learning, and early exiting models. Despite the potential of the mentioned approaches, optimizing operation processes and resource utilization is challenging to improve the effectiveness and performance of such intelligent IoT systems. Hence, comprehensively theoretical and mathematical analyses as well as experimental verifications are of necessity to maturize the development and implementation of machine-learning approaches in the next-generation IoT.

Dr. Nhu-Ngoc Dao
Prof. Dr. Sungrae Cho
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • Internet of Things
  • intelligent systems
  • edge computing
  • federated learning
  • distributed learning
  • cellular IoT
  • critical IoT
  • massive IoT
  • broadband IoT
  • intelligent IoT
  • medical IoT
  • wearable IoT
  • system automation and control
  • wireless sensor networks

Published Papers (3 papers)

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Research

16 pages, 5032 KiB  
Article
Multipath Routing Scheme for Optimum Data Transmission in Dense Internet of Things
by Abdelhamied A. Ateya, Sergey Bushelenkov, Ammar Muthanna, Alexander Paramonov, Andrey Koucheryavy, Samia Allaoua Chelloug and Ahmed A. Abd El-Latif
Mathematics 2023, 11(19), 4168; https://doi.org/10.3390/math11194168 - 05 Oct 2023
Viewed by 1542
Abstract
The Internet of Things (IoT) is an emerging technology that has recently gained significant interest, especially with the dramatic increase in connected devices. However, IoT networks are not yet standardized, and the design of such networks faces many challenges, including scalability, flexibility, reliability, [...] Read more.
The Internet of Things (IoT) is an emerging technology that has recently gained significant interest, especially with the dramatic increase in connected devices. However, IoT networks are not yet standardized, and the design of such networks faces many challenges, including scalability, flexibility, reliability, and availability of such networks. Routing is among the significant problems facing IoT network design because of the dramatic increase in connected devices and the network requirements regarding availability, reliability, latency, and flexibility. To this end, this work investigates deploying a multipath routing scheme for dense IoT networks. The proposed method selects a group of routes from all available routes to forward data at a maximum rate. The choice of data transmission routes is a complex problem for which numerical optimization methods can be used. A novel method for selecting the optimum group of routes and coefficients of traffic distribution along them is proposed. The proposed method is implemented using dynamic programming. The proposed method outperforms the traditional route selection methods, e.g., random route selection, especially for dense IoT networks. The model significantly reduced the number of intermediate nodes involved in routing paths over dense IoT networks by 34%. Moreover, it effectively demonstrated a significant decrease of 52% in communication overhead and 40% in data delivery time in dense IoT networks compared to traditional models. Full article
(This article belongs to the Special Issue Intelligent Systems, Communications, and Networks for the Next-Generation Internet of Things)
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13 pages, 16557 KiB  
Communication
Spectral Efficiency Analysis for IRS-Assisted MISO Wireless Communication: A Metaverse Scenario Proposal
by Md Habibur Rahman, Mohammad Abrar Shakil Sejan, Md Abdul Aziz, Dong-Sun Kim, Young-Hwan You and Hyoung-Kyu Song
Mathematics 2023, 11(14), 3181; https://doi.org/10.3390/math11143181 - 20 Jul 2023
Cited by 3 | Viewed by 855
Abstract
The metaverse is emerging as a next-generation internet paradigm that will enhance human interaction and connectivity. Digital twinning, a fundamental strategy used in the metaverse, allows for the virtualization of real-world items, people, actions, and settings. A virtual world called the metaverse is [...] Read more.
The metaverse is emerging as a next-generation internet paradigm that will enhance human interaction and connectivity. Digital twinning, a fundamental strategy used in the metaverse, allows for the virtualization of real-world items, people, actions, and settings. A virtual world called the metaverse is built on a variety of technologies. Wireless communication is an important part of these technologies. In particular, wireless 6G communication can be essential for the growth of the metaverse. In line with the goal of achieving higher rates in the next-generation wireless network for the metaverse, in this paper, a novel conceptualization of intelligent reflecting surface (IRS)-assisted multiple-input single output-based wireless communication in physical world environments is proposed. More specifically, this paper proposes that in the physical world, the IRS-assisted communication between a communication network and users can be reflected in the metaverse through the virtual world (such as digital avatars and the virtual environment). In the simulations, the bit-error rate and spectral efficiency of the receiver terminal were performed and calculated in the metaverse engine for future consideration. Full article
(This article belongs to the Special Issue Intelligent Systems, Communications, and Networks for the Next-Generation Internet of Things)
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23 pages, 797 KiB  
Article
HAP-Assisted RSMA-Enabled Vehicular Edge Computing: A DRL-Based Optimization Framework
by Tri-Hai Nguyen and Laihyuk Park
Mathematics 2023, 11(10), 2376; https://doi.org/10.3390/math11102376 - 19 May 2023
Cited by 6 | Viewed by 1271
Abstract
In recent years, the demand for vehicular edge computing (VEC) has grown rapidly due to the increasing need for low-latency and high-throughput applications such as autonomous driving and smart transportation systems. Nevertheless, offering VEC services in rural locations remains a difficulty owing to [...] Read more.
In recent years, the demand for vehicular edge computing (VEC) has grown rapidly due to the increasing need for low-latency and high-throughput applications such as autonomous driving and smart transportation systems. Nevertheless, offering VEC services in rural locations remains a difficulty owing to a lack of network facilities. We tackle this issue by taking advantage of high-altitude platforms (HAPs) and rate-splitting multiple access (RSMA) techniques to propose an HAP-assisted RSMA-enabled VEC system, which can enhance connectivity and provide computational capacity in rural locations. We also introduce a deep deterministic policy gradient (DDPG)-based framework that optimizes the allocation of resources and task offloading by jointly considering the offloading rate, splitting rate, transmission power, and decoding order parameters. Via results from extensive simulations, the proposed framework shows superior performance in comparison with conventional schemes regarding task success rate and energy consumption. Full article
(This article belongs to the Special Issue Intelligent Systems, Communications, and Networks for the Next-Generation Internet of Things)
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