Next-Generation Green Wireless Networks and Industrial IoT

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 10415

Special Issue Editors


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Guest Editor
Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh
Interests: cellular mobile networks (6G/5G); deep learning for wireless networking; IoT and M2M communications; smart grid communications; wireless sensor networks; underwater communications

Special Issue Information

Dear Colleagues,

The next generation of wireless networks (i.e., 6G) will connect billions of machines and millions of people and is envisioned to support high-speed communication in the three-dimensional space by integrating space, aerial, terrestrial, and underwater networks. The overall goal is to provide ubiquitous and unlimited connectivity to a massive number of Internet of Things (IoT) and machine-type devices/users with diverse qualities of service requirements, supporting substantial and heterogeneous traffic demands, and reducing the energy consumption with the help of highly energy-efficient communication protocols, transceivers, and computing technologies. To successfully enable IoT applications, wireless systems must simultaneously provide ultra-low latency, high reliability, and high data rates for heterogeneous devices, through downlink and uplink. In addition, end-to-end co-designs of computing, control and communication functionalities are also required by the emerging IoT services. The ever-increasing demand for advanced and ubiquitous applications over the recent years has shifted the paradigm of self-organizing networks to self-sustainable networks (SSNs). To meet these requirements, next-generation networks are expected to address unique challenges to transform wireless systems into self-sustaining and intelligent systems, in order to dynamically provision and orchestrate computing, control, and communication tailored to the IoE services’ requirements. Symmetry is an extraordinary characteristic which has widely been deployed in the research fields of wireless communication. Green industrial Internet of Things (G-IIoT) covers the broad domain of smart grid, smart manufacturing, intelligent transport and smart cities. G-IIoT refers to the combination of IoT technology with big data resulting from intelligent processes in those domains. Among other objectives, this Special Issue focuses on original research that investigates the symmetry/asymmetry characteristics in wireless communications and smart industries. 

With the rapid development of machine learning and wireless communication technologies, intelligent applications and services have gained widespread popularity and large-scale implementation in our daily life, including applications in mobile entertainment, automotive, healthcare, education and industrial manufacture. These intelligent applications bring about unparalleled levels of transformation and benefits to human societies and national economies. However, in most intelligent applications (e.g., intelligent transportation, smart finance), a large amount of useful data may be generated on multiple nodes and stored by multiple distributed devices, such as vehicles, smart phones and robots. Machine intelligence and green communications bring artificial intelligence (AI) to smart industries among other domains, to enable better management of network resources, optimize operations and efficiency, and provide a more sustainable environment with user-centric services. Built on the advancing wireless infrastructures, the key question is how to make use of artificial intelligence in green communications and networking to better support smart industries. AI will bring many key research directions for next-generation networks, driven by the massive availability of data which is transitioning from big and centralized data towards distributed, massive, and small data. Furthermore, providing network sustainability, as well as energy-efficient and environment-aware connections to the massive number of devices and users with different levels of processing capabilities is one of the key challenges. Moreover, how to handle the massive unstructured/semi-structured data generated by the resource-constrained devices/sensors through the resource-limited communication infrastructure in an energy-efficient manner is another important issue to be addressed. Although next-generation intelligent applications can dramatically enhance the life experience of humans and revolutionize modern business, there are still many open challenges that are unresolved when applying the fusion of ML and emerging technologies for next-generation intelligent applications.

Given the strong interest in both industry and academia, this Special Issue aims to disseminate the latest theoretical and experimental works in the domain of energy-efficient communication and computing technologies towards enabling massively connected, fully machine-intelligent, and sustainable green wireless networks and industrial IoT. The main topics of interest for this Special Issue include, but are not limited to, the following:

  • Energy-efficient machine-to-machine communications
  • Energy- and spectral-efficient access technologies for modern wireless networks
  • Scalable machine learning for next-generation wireless networks
  • AI/ML techniques for green communications and computing in 5G/B5G networks and smart industries
  • Scalable network infrastructures for smart cities
  • Smart energy management techniques for balancing energy demand–supply in 5G/B5G
  • Analytical, optimization and experimental approaches for green communications and computing in 5G/B5G networks
  • Energy harvesting and green communications for smart industries
  • AI-enabled energy-efficient data acquisition for smart industries
  • Green computing systems, networking protocols and ML algorithms for sustainable growth
  • Intelligent and green IoT systems for smart industries
  • Enabling technologies for IoT–cloud integration in smart industries
  • Breakthrough technologies, protocols and network architectures for resource allocation and energy efficiency in massive IoT
  • Smart infrastructure including smart transport, smart grids and green communications for smart cities
  • 6G/5G/LTE/WiFi-enabled mobile edge computing for scalable IoT
  • AI-enabled IoT applications: smart cities, smart grid, smart home, smart E-health, smart mobility, smart living, etc.
  • Security and privacy for AI-enabled IIoT management and interworking for next-generation wireless networks

We hope this Special Issue will achieve a precise, concrete and concise conclusion that contributes significantly to opening new horizons for future research directions. Please note that all submitted papers should be in the scope of the Symmetry journal.

Dr. Mohammed H. Alsharif 
Prof. Dr. Md. Farhad Hossain
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. Symmetry 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 2400 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

  • 5G/6G
  • green communications
  • energy-efficient wireless communications
  • smart cities
  • sustainable networks
  • smart grid communications
  • energy harvesting
  • machine learning
  • IoT

Published Papers (3 papers)

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Research

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19 pages, 4121 KiB  
Article
A High-Efficiency Diplexer for Sustainable 5G-Enabled IoT in Metaverse Transportation System and Smart Grids
by Mohammad (Behdad) Jamshidi, Salah I. Yahya, Leila Nouri, Hamed Hashemi-Dezaki, Abbas Rezaei and Muhammad Akmal Chaudhary
Symmetry 2023, 15(4), 821; https://doi.org/10.3390/sym15040821 - 29 Mar 2023
Cited by 11 | Viewed by 1589
Abstract
Symmetry is essential in the design of complex systems like the Metaverse Transportation Sys-tem (MTS) and helps ensure that all components work together effectively. In the development of a microstrip diplexer for 5G-enabled IoT and MTS, maintaining symmetry is crucial to achieving flat [...] Read more.
Symmetry is essential in the design of complex systems like the Metaverse Transportation Sys-tem (MTS) and helps ensure that all components work together effectively. In the development of a microstrip diplexer for 5G-enabled IoT and MTS, maintaining symmetry is crucial to achieving flat responses with low group delays. By integrating transportation technology and the Metaverse, the Metaverse Transportation System (MTS) can greatly improve the effectiveness and intellect of transportation systems in reality. To establish a dependable network, it is essential to include 5G-enabled Internet of Things (IoT) and sensor networks with a sustainable design that focuses on efficiency and energy conservation. A three-channel microstrip lowpass-bandpass diplexer has been developed for 5G-enabled IoT and MTS implementation. Multi-channel designs are rare due to the complex design process, but this diplexer is very compact at only 0.004 λg2. All channels have flat responses with group delays of 0.34 ns, 1.7 ns, and 0.34 ns at the lower, middle, and upper passbands, respectively. The lowpass channel has a cut-off frequency of 1.22 GHz, suitable for mid-band 5G applications. Compared to previous work, this diplexer achieves the smallest size, lowest group delay, and insertion and return losses at the lower channel. It consists of a lowpass-bandpass section connected to a band-pass filter analyzed mathematically, and its performance has been verified through simulation and measurement with good accuracy. Full article
(This article belongs to the Special Issue Next-Generation Green Wireless Networks and Industrial IoT)
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Review

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23 pages, 504 KiB  
Review
Energy-Efficient Deep Neural Networks for EEG Signal Noise Reduction in Next-Generation Green Wireless Networks and Industrial IoT Applications
by Arun Kumar, Sumit Chakravarthy and Aziz Nanthaamornphong
Symmetry 2023, 15(12), 2129; https://doi.org/10.3390/sym15122129 - 30 Nov 2023
Cited by 1 | Viewed by 1124
Abstract
Wireless electroencephalography (EEG) has emerged as a critical interface between human cognitive processes and machine learning technologies in the burgeoning field of sensor communications. This paper presents a comprehensive review of advancements in wireless EEG communication and analysis, with an emphasis on their [...] Read more.
Wireless electroencephalography (EEG) has emerged as a critical interface between human cognitive processes and machine learning technologies in the burgeoning field of sensor communications. This paper presents a comprehensive review of advancements in wireless EEG communication and analysis, with an emphasis on their role in next-generation green wireless networks and industrial IoT. The review explores the efficacy of modulation techniques, such as amplitude-shift keying (ASK) and frequency-shift keying (FSK) in EEG data transmission, and emphasizes the transformative role of deep learning in the joint transmission and restoration of EEG signals. In addition, we propose a novel, energy-efficient approach to deep learning-based EEG analytics, designed to enhance wireless information transfer for industrial IoT applications. By applying an autoencoder to sample the EEG data and incorporating a hidden layer to simulate a noisy communication channel, we assessed the energy efficiency and reliability of the transmission. Our results demonstrate that the chosen network topology and parameters significantly affect not only data fidelity but also energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. A key aspect of our study is related to symmetry. Our results demonstrate that the chosen network topology and parameters significantly impact not only data fidelity but also energy fidelity and energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. Furthermore, we realized that the EEG data showed mildly marked symmetry. Neural networks must also exhibit asymmetric behavior for better performance. Full article
(This article belongs to the Special Issue Next-Generation Green Wireless Networks and Industrial IoT)
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37 pages, 8111 KiB  
Review
Green IoT: A Review and Future Research Directions
by Mohammed H. Alsharif, Abu Jahid, Anabi Hilary Kelechi and Raju Kannadasan
Symmetry 2023, 15(3), 757; https://doi.org/10.3390/sym15030757 - 19 Mar 2023
Cited by 28 | Viewed by 6963
Abstract
The internet of things (IoT) has a significant economic and environmental impact owing to the billions or trillions of interconnected devices that use various types of sensors to communicate through the internet. It is well recognized that each sensor requires a small amount [...] Read more.
The internet of things (IoT) has a significant economic and environmental impact owing to the billions or trillions of interconnected devices that use various types of sensors to communicate through the internet. It is well recognized that each sensor requires a small amount of energy to function; but, with billions of sensors, energy consumption can be significant. Therefore, it is crucial to focus on developing energy-efficient IoT technology and sustainable solutions. The contribution of this article is to support the implementation of eco-friendly IoT solutions by presenting a thorough examination of energy-efficient practices and strategies for IoT to assist in the advancement of sustainable and energy-efficient IoT technologies in the future. Four framework principles for achieving this are discussed, including (i) energy-efficient machine-to-machine (M2M) communications, (ii) energy-efficient and eco-sustainable wireless sensor networks (WSN), (iii) energy-efficient radio-frequency identification (RFID), and (iv) energy-efficient microcontroller units and integrated circuits (IC). This review aims to contribute to the next-generation implementation of eco-sustainable and energy-efficient IoT technologies. Full article
(This article belongs to the Special Issue Next-Generation Green Wireless Networks and Industrial IoT)
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