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Key Intelligent Technologies for Wireless Communications and Internet of Things

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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 March 2024) | Viewed by 16149

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Special Issue Editors

Prof. Dr. Guan Gui

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Guest Editor

College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: intelligent signal analysis, signal sensing and recognition, AI-based wireless techniques
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yun Lin

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Guest Editor

College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: fault detection and recognition; machine learning and data analytics over wireless networks; signal processing and analysis; cognitive radio and software defined radio; artificial intelligence; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Haitao Zhao

E-Mail Website
Guest Editor

College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: signal processing; physical layer security; deep learning

Special Issue Information

Dear Colleagues,

The upcoming sixth-generation (6G) wireless communication technologies will provide higher data rates, more connections, and wider network coverage to meet the needs of various application domains, such as metaverse, intelligent transportation, industry 5.0, and so on. However, confronted with the highly dynamic wireless environment and the increasing demand for wireless communication, the modeling of wireless communication problems becomes more and more difficult, and the complexity of problem-solving increases exponentially. Due to the advantages of artificial intelligence (AI) technology in complex nonlinear problems, it is widely believed that various intelligent technologies can be applied into wireless communication systems and Internet of things systems for improving performance and efficiency.

The goal of this Special Issue is to provide an overview of the latest developments regarding key intelligent technologies for wireless communications and Internet of things. Both theoretical and technical aspects are of interest.

Topics of interest include, but are not limited to, the following:

AI-based signal detection, signal classification and signal processing;
AI-based channel modeling, channel estimation and feedback;
AI-based positioning, sensing and localization;
AI-based beamforming and resource allocation;
AI-based non-orthogonal multiple-access (NOMA);
AI for IoT and massive connectivity;
AI for integrated sensing and communications;
AI for reconfigurable intelligent surface (RIS)-aided wireless communication;
AI for massive MIMO and cell-free massive MIMO;
AI for mmWave and Terahertz communication;
AI for semantic communication;
AI for green communication;
AI for UAV communication;
AI for ultra-reliable and low latency communication;
AI-enabled techniques for robustness, security, and privacy in wireless communications and Internet of things.

Prof. Dr. Guan Gui
Prof. Dr. Yun Lin
Prof. Dr. Haitao Zhao
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

6G
artificial intelligence
wireless communication
Internet of things
signal processing

Published Papers (9 papers)

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Research

17 pages, 2344 KiB

Open AccessArticle

An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments

by Mostafa El Debeiki, Saba Al-Rubaye, Adolfo Perrusquía, Christopher Conrad and Juan Alejandro Flores-Campos

Future Internet 2024, 16(3), 89; https://0-doi-org.brum.beds.ac.uk/10.3390/fi16030089 - 06 Mar 2024

Viewed by 977

Abstract

The use of unmanned aerial vehicles (UAVs) is increasing in transportation applications due to their high versatility and maneuverability in complex environments. Search and rescue is one of the most challenging applications of UAVs due to the non-homogeneous nature of the environmental and communication landscapes. In particular, mountainous areas pose difficulties due to the loss of connectivity caused by large valleys and the volumes of hazardous weather. In this paper, the connectivity issue in mountainous areas is addressed using a path planning algorithm for UAV relay. The approach is based on two main phases: (1) the detection of areas of interest where the connectivity signal is poor, and (2) an energy-aware and resilient path planning algorithm that maximizes the coverage links. The approach uses a viewshed analysis to identify areas of visibility between the areas of interest and the cell-towers. This allows the construction of a blockage map that prevents the UAV from passing through areas with no coverage, whilst maximizing the coverage area under energy constraints and hazardous weather. The proposed approach is validated under open-access datasets of mountainous zones, and the obtained results confirm the benefits of the proposed approach for communication networks in remote and challenging environments. Full article

(This article belongs to the Special Issue Key Intelligent Technologies for Wireless Communications and Internet of Things)

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14 pages, 1090 KiB

Open AccessArticle

Refined Semi-Supervised Modulation Classification: Integrating Consistency Regularization and Pseudo-Labeling Techniques

by Min Ma, Shanrong Liu, Shufei Wang and Shengnan Shi

Future Internet 2024, 16(2), 38; https://0-doi-org.brum.beds.ac.uk/10.3390/fi16020038 - 23 Jan 2024

Viewed by 1382

Abstract

Automatic modulation classification (AMC) plays a crucial role in wireless communication by identifying the modulation scheme of received signals, bridging signal reception and demodulation. Its main challenge lies in performing accurate signal processing without prior information. While deep learning has been applied to AMC, its effectiveness largely depends on the availability of labeled samples. To address the scarcity of labeled data, we introduce a novel semi-supervised AMC approach combining consistency regularization and pseudo-labeling. This method capitalizes on the inherent data distribution of unlabeled data to supplement the limited labeled data. Our approach involves a dual-component objective function for model training: one part focuses on the loss from labeled data, while the other addresses the regularized loss for unlabeled data, enhanced through two distinct levels of data augmentation. These combined losses concurrently refine the model parameters. Our method demonstrates superior performance over established benchmark algorithms, such as decision trees (DTs), support vector machines (SVMs), pi-models, and virtual adversarial training (VAT). It exhibits a marked improvement in the recognition accuracy, particularly when the proportion of labeled samples is as low as 1–4%. Full article

(This article belongs to the Special Issue Key Intelligent Technologies for Wireless Communications and Internet of Things)

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22 pages, 5400 KiB

Open AccessArticle

Synchronization of Separate Sensors’ Data Transferred through a Local Wi-Fi Network: A Use Case of Human-Gait Monitoring

by Viktor Masalskyi, Dominykas Čičiurėnas, Andrius Dzedzickis, Urtė Prentice, Gediminas Braziulis and Vytautas Bučinskas

Future Internet 2024, 16(2), 36; https://0-doi-org.brum.beds.ac.uk/10.3390/fi16020036 - 23 Jan 2024

Viewed by 1586

Abstract

This paper addresses the challenge of synchronizing data acquisition from independent sensor systems in a local network. The network comprises microcontroller-based systems that collect data from physical sensors used for monitoring human gait. The synchronized data are transmitted to a PC or cloud storage through a central controller. The performed research proposes a solution for effectively synchronizing the data acquisition using two alternative data-synchronization approaches. Additionally, it explores techniques to handle varying amounts of data from different sensor types. The experimental research validates the proposed solution by providing trial results and stability evaluations and comparing them to the human-gait-monitoring system requirements. The alternative data-transmission method was used to compare the data-transmission quality and data-loss rate. The developed algorithm allows data acquisition from six pressure sensors and two accelerometer/gyroscope modules, ensuring a 24.6 Hz sampling rate and 1 ms synchronization accuracy. The obtained results prove the algorithm’s suitability for human-gait monitoring under its regular activity. The paper concludes with discussions and key insights derived from the obtained results. Full article

(This article belongs to the Special Issue Key Intelligent Technologies for Wireless Communications and Internet of Things)

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17 pages, 1832 KiB

Open AccessArticle

Diff-SwinT: An Integrated Framework of Diffusion Model and Swin Transformer for Radar Jamming Recognition

by Minghui Sha, Dewu Wang, Fei Meng, Wenyan Wang and Yu Han

Future Internet 2023, 15(12), 374; https://0-doi-org.brum.beds.ac.uk/10.3390/fi15120374 - 23 Nov 2023

Viewed by 1519

Abstract

With the increasing complexity of radar jamming threats, accurate and automatic jamming recognition is essential but remains challenging. Conventional algorithms often suffer from sharply decreased recognition accuracy under low jamming-to-noise ratios (JNR).Artificial intelligence-based jamming signal recognition is currently the main research directions for this issue. This paper proposes a new radar jamming recognition framework called Diff-SwinT. Firstly, the time-frequency representations of jamming signals are generated using Choi-Williams distribution. Then, a diffusion model with U-Net backbone is trained by adding Gaussian noise in the forward process and reconstructing in the reverse process, obtaining an inverse diffusion model with denoising capability. Next, Swin Transformer extracts hierarchical multi-scale features from the denoised time-frequency plots, and the features are fed into linear layers for classification. Experiments show that compared to using Swin Transformer, the proposed framework improves overall accuracy by 15% to 10% at JNR from −16 dB to −8 dB, demonstrating the efficacy of diffusion-based denoising in enhancing model robustness. Compared to VGG-based and feature-fusion-based recognition methods, the proposed framework has over 27% overall accuracy advantage under JNR from −16 dB to −8 dB. This integrated approach significantly enhances intelligent radar jamming recognition capability in complex environments. Full article

(This article belongs to the Special Issue Key Intelligent Technologies for Wireless Communications and Internet of Things)

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19 pages, 7742 KiB

Open AccessArticle

Implementation of In-Band Full-Duplex Using Software Defined Radio with Adaptive Filter-Based Self-Interference Cancellation

by Wei-Shun Liao, Ou Zhao, Keren Li, Hikaru Kawasaki and Takeshi Matsumura

Future Internet 2023, 15(11), 360; https://0-doi-org.brum.beds.ac.uk/10.3390/fi15110360 - 03 Nov 2023

Cited by 1 | Viewed by 1432

Abstract

For next generation wireless communication systems, high throughput, low latency, and large user accommodation are popular and important required characteristics. To achieve these requirements for next generation wireless communication systems, an in-band full-duplex (IBFD) communication system is one of the possible candidate technologies. However, to realize IBFD systems, there is an essential problem that there exists a large self-interference (SI) due to the simultaneous signal transmission and reception in the IBFD systems. Therefore, to implement the IBFD system, it is necessary to realize a series of effective SI cancellation processes. In this study, we implemented a prototype of SI cancellation processes with our designed antenna, analog circuit, and digital cancellation function using an adaptive filter. For system implementation, we introduce software-defined radio (SDR) devices in this study. By using SDR devices, which can be customized by users, the evaluations of complicated wireless access systems like IBFD can be realized easily. Besides the validation stage of system practicality, the system development can be more effective by using SDR devices. Therefore, we utilize SDR devices to implement the proposed IBFD system and conduct experiments to evaluate its performance. The results show that the SI cancellation effect can reach nearly 100 dB with

10^{- 3}

order bit error rate (BER) after signal demodulation. From the experiment results, it can be seen obviously that the implemented prototype can effectively cancel the large amount of SI and obtain satisfied digital demodulation results, which validates the effectiveness of the developed system. Full article

(This article belongs to the Special Issue Key Intelligent Technologies for Wireless Communications and Internet of Things)

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24 pages, 1339 KiB

Open AccessArticle

Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas

by Hsiao-Ching Chang, Hsing-Tsung Lin and Pi-Chung Wang

Future Internet 2023, 15(9), 301; https://0-doi-org.brum.beds.ac.uk/10.3390/fi15090301 - 03 Sep 2023

Cited by 1 | Viewed by 1713

Abstract

With the rapid development of the Internet of Things, the number of wireless devices is increasing rapidly. Because of the limited battery capacity, these devices may suffer from the issue of power depletion. Radio frequency (RF) energy harvesting technology can wirelessly charge devices to prolong their lifespan. With the technology of beamforming, the beams generated by an antenna array can select the direction for wireless charging. Although a good charging-time schedule should be short, energy efficiency should also be considered. In this work, we propose two algorithms to optimize the time consumption for charging devices. We first present a greedy algorithm to minimize the total charging time. Then, a differential evolution (DE) algorithm is proposed to minimize the energy overflow and improve energy efficiency. The DE algorithm can also gradually increase fully charged devices. The experimental results show that both the proposed greedy and DE algorithms can find a schedule of a short charging time with the lowest energy overflow. The DE algorithm can further improve the performance of data transmission to promote the feasibility of potential wireless sensing and charging applications by reducing the number of fully charged devices at the same time. Full article

(This article belongs to the Special Issue Key Intelligent Technologies for Wireless Communications and Internet of Things)

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16 pages, 3844 KiB

Open AccessArticle

LoRa Communication Using TVWS Frequencies: Range and Data Rate

by Anjali R. Askhedkar, Bharat S. Chaudhari, Maha Abdelhaq, Raed Alsaqour, Rashid Saeed and Marco Zennaro

Future Internet 2023, 15(8), 270; https://0-doi-org.brum.beds.ac.uk/10.3390/fi15080270 - 14 Aug 2023

Cited by 1 | Viewed by 1871

Abstract

Low power wide area network (LPWAN) is a wireless communication technology that offers large coverage, low data rates, and low power consumption, making it a suitable choice for the growing Internet of Things and machine-to-machine communication applications. Long range (LoRa), an LPWAN technology, has recently been used in the industrial, scientific and medical (ISM) band for various low-power wireless applications. The coverage and data rate supported by these devices in the ISM band is well-studied in the literature. In this paper, we study the usage of TV white spaces (TVWS) for LoRa transmissions to address the growing spectrum demand. Additionally, the range and data rate of TVWS-based LoRa, for different transmission parameter values using different path-loss models and for various scenarios such as free space, outdoor and indoor are investigated. A path-loss model for TVWS-based LoRa is also proposed and explored, and the evaluations show that TVWS offers a longer range. This range and data rate study would be useful for efficient network planning and system design for TVWS-based LoRa LPWANs. Full article

(This article belongs to the Special Issue Key Intelligent Technologies for Wireless Communications and Internet of Things)

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28 pages, 22171 KiB

Open AccessArticle

A Cyber-Physical System for Wildfire Detection and Firefighting

by Pietro Battistoni, Andrea Antonio Cantone, Gerardo Martino, Valerio Passamano, Marco Romano, Monica Sebillo and Giuliana Vitiello

Future Internet 2023, 15(7), 237; https://0-doi-org.brum.beds.ac.uk/10.3390/fi15070237 - 06 Jul 2023

Cited by 7 | Viewed by 2489

Abstract

The increasing frequency and severity of forest fires necessitate early detection and rapid response to mitigate their impact. This project aims to design a cyber-physical system for early detection and rapid response to forest fires using advanced technologies. The system incorporates Internet of Things sensors and autonomous unmanned aerial and ground vehicles controlled by the robot operating system. An IoT-based wildfire detection node continuously monitors environmental conditions, enabling early fire detection. Upon fire detection, a UAV autonomously surveys the area to precisely locate the fire and can deploy an extinguishing payload or provide data for decision-making. The UAV communicates the fire’s precise location to a collaborative UGV, which autonomously reaches the designated area to support ground-based firefighters. The CPS includes a ground control station with web-based dashboards for real-time monitoring of system parameters and telemetry data from UAVs and UGVs. The article demonstrates the real-time fire detection capabilities of the proposed system using simulated forest fire scenarios. The objective is to provide a practical approach using open-source technologies for early detection and extinguishing of forest fires, with potential applications in various industries, surveillance, and precision agriculture. Full article

(This article belongs to the Special Issue Key Intelligent Technologies for Wireless Communications and Internet of Things)

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17 pages, 7028 KiB

Open AccessArticle

A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things

by Mian Wang , Cong’an Xu, Yun Lin, Zhiyi Lu, Jinlong Sun and Guan Gui

Future Internet 2023, 15(5), 171; https://0-doi-org.brum.beds.ac.uk/10.3390/fi15050171 - 30 Apr 2023

Cited by 5 | Viewed by 1770

Abstract

The Industrial Internet of Things (IIoT) refers to the application of the IoT in the industrial field. The development of fifth-generation (5G) communication technology has accelerated the world’s entry into the era of the industrial revolution and has also promoted the overall optimization of the IIoT. In the IIoT environment, challenges such as complex operating conditions and diverse data transmission have become increasingly prominent. Therefore, studying how to collect and process a large amount of real-time data from various devices in a timely, efficient, and reasonable manner is a significant problem. To address these issues, we propose a three-level networking model based on distributed sensor self-networking and cloud server platforms for networking. This model can collect monitoring data for a variety of industrial scenarios that require data collection. It enables the processing and storage of key information in a timely manner, reduces data transmission and storage costs, and improves data transmission reliability and efficiency. Additionally, we have designed a feature fusion network to further enhance the amount of feature information and improve the accuracy of industrial data recognition. The system also includes data preprocessing and data visualization capabilities. Finally, we discuss how to further preprocess and visualize the collected dataset and provide a specific algorithm analysis process using a large manipulator dataset as an example. Full article

(This article belongs to the Special Issue Key Intelligent Technologies for Wireless Communications and Internet of Things)

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