Research

27 pages, 3597 KiB

Open AccessArticle

A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks

by Ronghao Ma, Jianhong Zhou and Maode Ma

Sensors 2024, 24(7), 2331; https://doi.org/10.3390/s24072331 - 06 Apr 2024

Viewed by 421

In the realm of the fifth-generation (5G) wireless cellular networks, renowned for their dense connectivity, there lies a substantial facilitation of a myriad of Internet of Things (IoT) applications, which can be supported by the massive machine-type communication (MTC) technique, a fundamental communication [...] Read more.

In the realm of the fifth-generation (5G) wireless cellular networks, renowned for their dense connectivity, there lies a substantial facilitation of a myriad of Internet of Things (IoT) applications, which can be supported by the massive machine-type communication (MTC) technique, a fundamental communication framework. In some scenarios, a large number of machine-type communication devices (MTCD) may simultaneously enter the communication coverage of a target base station. However, the current handover mechanism specified by the 3rd Generation Partnership Project (3GPP) Release 16 incurs high signaling overhead within the access and core networks, which may have negative impacts on network efficiency. Additionally, other existing solutions are vulnerable to malicious attacks such as Denial of Service (DoS), Distributed Denial of Service (DDoS) attacks, and the failure of Key Forward Secrecy (KFS). To address this challenge, this paper proposes an efficient and secure handover authentication protocol for a group of MTCDs supported by blockchain technology. This protocol leverages the decentralized nature of blockchain technology and combines it with certificateless aggregate signatures to mutually authenticate the identity of a base station and a group of MTCDs. This approach can reduce signaling overhead and avoid key escrow while significantly lowering the risk associated with single points of failure. Additionally, the protocol protects device anonymity by encrypting device identities with temporary anonymous identity markers with the Elliptic Curve Diffie–Hellman (ECDH) to abandon serial numbers to prevent linkage attacks. The resilience of the proposed protocol against predominant malicious attacks has been rigorously validated through the application of the BAN logic and Scyther tool, underscoring its robust security attributes. Furthermore, compared to the existing solutions, the proposed protocol significantly reduces the authentication cost for a group of MTCDs during handover, while ensuring security, demonstrating commendable efficiency. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2023)

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14 pages, 7577 KiB

Open AccessArticle

Optimizing Wireless Connectivity: A Deep Neural Network-Based Handover Approach for Hybrid LiFi and WiFi Networks

by Mohammad Usman Ali Khan, Mohammad Inayatullah Babar, Saeed Ur Rehman, Dan Komosny and Peter Han Joo Chong

Sensors 2024, 24(7), 2021; https://doi.org/10.3390/s24072021 - 22 Mar 2024

Viewed by 447

Abstract

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making [...] Read more.

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals’ line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2023)

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20 pages, 534 KiB

Open AccessArticle

A Neural Network-Based Random Access Protocol for Crowded Massive MIMO Systems

by Felipe Augusto Dutra Bueno, Cézar Fumio Yamamura, Paulo Rogério Scalassara, Taufik Abrão and José Carlos Marinello

Sensors 2023, 23(24), 9805; https://doi.org/10.3390/s23249805 - 13 Dec 2023

Viewed by 602

Abstract

Fifth-generation (5G) and beyond networks are expected to serve large numbers of user equipments (UEs). Grant-based random access (RA) protocols are efficient when serving human users, typically with large data volumes to transmit. The strongest user collision resolution (SUCRe) is the first protocol [...] Read more.

Fifth-generation (5G) and beyond networks are expected to serve large numbers of user equipments (UEs). Grant-based random access (RA) protocols are efficient when serving human users, typically with large data volumes to transmit. The strongest user collision resolution (SUCRe) is the first protocol that effectively uses the many antennas at the 5G base station (BS) to improve connectivity performance. In this paper, our proposal involves substituting the retransmission rule of the SUCRe protocol with a neural network (NN) to enhance the identification of the strongest user and resolve collisions in a decentralized manner on the UEs’ side. The proposed NN-based procedure is trained offline, admitting different congestion levels of the system, aiming to obtain a single setup able to operate with different numbers of UEs. The numerical results indicate that our method attains substantial connectivity performance improvements compared to other protocols without requiring additional complexity or overhead. In addition, the proposed approach is robust regarding variations in the number of BS antennas and transmission power while improving energy efficiency by requiring fewer attempts on the RA stage. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2023)

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17 pages, 3748 KiB

Open AccessArticle

Orthogonal Space-Time Block Coding for Double Scattering V2V Links with LOS and Ground Reflections

by Miguel Gutiérrez Gaitán, Gowhar Javanmardi and Ramiro Sámano-Robles

Sensors 2023, 23(23), 9594; https://doi.org/10.3390/s23239594 - 03 Dec 2023

Viewed by 841

Abstract

This work presents the performance analysis of space-time block codes (STBCs) for vehicle-to-vehicle (V2V) fast-fading channels in scenarios with modified line-of-sight (LOS). The objective is to investigate how the V2V MIMO (multiple-input multiple-output) system performance is influenced by two important impairments: deterministic ground [...] Read more.

This work presents the performance analysis of space-time block codes (STBCs) for vehicle-to-vehicle (V2V) fast-fading channels in scenarios with modified line-of-sight (LOS). The objective is to investigate how the V2V MIMO (multiple-input multiple-output) system performance is influenced by two important impairments: deterministic ground reflections and an increased Doppler frequency (time-variant channels). STBCs of various coding rates (using an approximation model) are evaluated by assuming antenna elements distributed over the surface of two contiguous vehicles. A multi-ray model is used to study the multiple constructive/destructive interference patterns of the transmitted/received signals by all pairs of Tx–Rx antenna links considering ground reflections. A double scattering model is used to include the effects of stochastic channel components that depend on the Doppler frequency. The results show that STBCs are capable of counteracting fades produced by destructive self-interference components across a range of inter-vehicle distances and for a range of Doppler frequency values. Notably, the effectiveness of STBCs in deep fades is shown to outperform schemes with exclusive receive diversity, despite the interference created by the loss of orthogonality in time-varying channels with a moderate increase of Doppler frequency (mainly due to higher vehicle speeds, higher frequency or shorter time slots). Higher-order STBCs with rate losses are also evaluated using an approximation model, showing interesting gains even for low coding rate performance, particularly when accompanied by a multiple antenna receiver. Overall, these results can shed light on how to exploit transmit diversity in time-varying vehicular channels with modified LOS. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2023)

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25 pages, 837 KiB

Open AccessArticle

On Indoor Localization Using WiFi, BLE, UWB, and IMU Technologies

by Samuel G. Leitch, Qasim Zeeshan Ahmed, Waqas Bin Abbas, Maryam Hafeez, Pavlos I. Laziridis, Pradorn Sureephong and Temitope Alade

Sensors 2023, 23(20), 8598; https://doi.org/10.3390/s23208598 - 20 Oct 2023

Cited by 4 | Viewed by 2480

Abstract

Indoor localization is a key research area and has been stated as a major goal for Sixth Generation (6G) communications. Indoor localization faces many challenges, such as harsh wireless propagation channels, cluttered and dynamic environments, non-line-of-sight conditions, etc. There are various technologies that [...] Read more.

Indoor localization is a key research area and has been stated as a major goal for Sixth Generation (6G) communications. Indoor localization faces many challenges, such as harsh wireless propagation channels, cluttered and dynamic environments, non-line-of-sight conditions, etc. There are various technologies that can be applied to address these issues. In this paper, four major technologies for implementing an indoor localization system are reviewed: Wireless Fidelity (Wi-Fi), Ultra-Wide Bandwidth Radio (UWB), Bluetooth Low Energy (BLE), and Inertial Measurement Units (IMU). Sections on Data Fusion (DF) and Machine Learning (ML) have been included as well due to their key role in Indoor Positioning Systems (IPS). These technologies have been categorized based on the techniques that they employ and the associated errors in localization. A brief comparison between these technologies is made based on specific performance metrics. Finally, the limitations of these techniques are identified to aid future research. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2023)

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18 pages, 2160 KiB

Open AccessArticle

Time-Sensitive Networking to Improve the Performance of Distributed Functional Safety Systems Implemented over Wi-Fi

by Alberto Morato, Stefano Vitturi, Federico Tramarin, Claudio Zunino and Manuel Cheminod

Sensors 2023, 23(18), 7825; https://doi.org/10.3390/s23187825 - 12 Sep 2023

Cited by 1 | Viewed by 774

Abstract

Industry 4.0 has significantly improved the industrial manufacturing scenario in recent years. The Industrial Internet of Things (IIoT) enables the creation of globally interconnected smart factories, where constituent elements seamlessly exchange information. Industry 5.0 has further complemented these achievements, as it focuses on [...] Read more.

Industry 4.0 has significantly improved the industrial manufacturing scenario in recent years. The Industrial Internet of Things (IIoT) enables the creation of globally interconnected smart factories, where constituent elements seamlessly exchange information. Industry 5.0 has further complemented these achievements, as it focuses on a human-centric approach where humans become part of this network of things, leading to a robust human–machine interaction. In this distributed, dynamic, and highly interconnected environment, functional safety is essential for adequately protecting people and machinery. The increasing availability of wireless networks makes it possible to implement distributed and flexible functional safety systems. However, such networks are known for introducing unwanted delays that can lead to safety performance degradation due to their inherent uncertainty. In this context, the Time-Sensitive Networking (TSN) standards present an attractive prospect for enhancing and ensuring acceptable behaviors. The research presented in this paper deals with the introduction of TSN to implement functional safety protocols for wireless networks. Among the available solutions, we selected Wi-Fi since it is a widespread network, often considered and deployed for industrial applications. The introduction of a reference functional safety protocol is detailed, along with an analysis of how TSN can enhance its behavior by evaluating relevant performance indexes. The evaluation pertains to a standard case study of an industrial warehouse, tested through practical simulations. The results demonstrate that TSN provides notable advantages, but it requires meticulous coordination with the Wi-Fi MAC layer protocol to guarantee improved performance. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2023)

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25 pages, 6171 KiB

Open AccessArticle

A Modular Framework for Data Processing at the Edge: Design and Implementation

by Lubomir Urblik, Erik Kajati, Peter Papcun and Iveta Zolotova

Sensors 2023, 23(17), 7662; https://doi.org/10.3390/s23177662 - 04 Sep 2023

Viewed by 1154

Abstract

There is a rapid increase in the number of edge devices in IoT solutions, generating vast amounts of data that need to be processed and analyzed efficiently. Traditional cloud-based architectures can face latency, bandwidth, and privacy challenges when dealing with this data flood. [...] Read more.

There is a rapid increase in the number of edge devices in IoT solutions, generating vast amounts of data that need to be processed and analyzed efficiently. Traditional cloud-based architectures can face latency, bandwidth, and privacy challenges when dealing with this data flood. There is currently no unified approach to the creation of edge computing solutions. This work addresses this problem by exploring containerization for data processing solutions at the network’s edge. The current approach involves creating a specialized application compatible with the device used. Another approach involves using containerization for deployment and monitoring. The heterogeneity of edge environments would greatly benefit from a universal modular platform. Our proposed edge computing-based framework implements a streaming extract, transform, and load pipeline for data processing and analysis using ZeroMQ as the communication backbone and containerization for scalable deployment. Results demonstrate the effectiveness of the proposed framework, making it suitable for time-sensitive IoT applications. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2023)

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22 pages, 40388 KiB

Open AccessArticle

An IoT Sensing Platform and Serious Game for Remote Martial Arts Training

by Karlos Ishac, Katia Bourahmoune and Marc Carmichael

Sensors 2023, 23(17), 7565; https://doi.org/10.3390/s23177565 - 31 Aug 2023

Cited by 2 | Viewed by 877

Abstract

We propose a system for self-supported martial arts training using an IoT sensing platform and Serious Game that can also be extended for general sports training. In martial arts, it is important that the practitioner is correctly performing each technique to accurately learn [...] Read more.

We propose a system for self-supported martial arts training using an IoT sensing platform and Serious Game that can also be extended for general sports training. In martial arts, it is important that the practitioner is correctly performing each technique to accurately learn and prevent injury. A common stance in all martial arts, but especially in Shaolin Kung Fu, is the horse stance or Mabu. With the pandemic, many more people adopted remote training without the presence of a professional trainer to give advice. Our developed LifeMat system, which is a novel IoT pressure-sensitive training mat, uses pressure maps and pattern recognition to accurately classify key martial arts postures, provide feedback on form, and detect when the user performs the technique incorrectly. This is presented in the form of a Serious Game we have developed named Kung Future that focuses on the Mabu stance as a case study. We tested 14 participants with three different feedback conditions and demonstrated that, on average, participants had higher performance, duration, engagement, and motivation when game feedback was active. Furthermore, user responses from the surveys suggested positive feedback for real-world and long-term use and scalability. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2023)

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13 pages, 3653 KiB

Open AccessArticle

Period Estimation of Spread Spectrum Codes Based on ResNet

by Han-Qing Gu, Xia-Xia Liu, Lu Xu, Yi-Jia Zhang and Zhe-Ming Lu

Sensors 2023, 23(15), 7002; https://doi.org/10.3390/s23157002 - 07 Aug 2023

Viewed by 809

Abstract

In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance [...] Read more.

In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from −20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2023)

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15 pages, 1689 KiB

Open AccessArticle

DSSS Signal Detection Based on CNN

by Han-Qing Gu, Xia-Xia Liu, Lu Xu, Yi-Jia Zhang and Zhe-Ming Lu

Sensors 2023, 23(15), 6691; https://doi.org/10.3390/s23156691 - 26 Jul 2023

Viewed by 1240

Abstract

With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, [...] Read more.

With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2023)

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18 pages, 1617 KiB

Open AccessArticle

RIS-Aided Proactive Mobile Network Downlink Interference Suppression: A Deep Reinforcement Learning Approach

by Yingze Wang, Mengying Sun, Qimei Cui, Kwang-Cheng Chen and Yaxin Liao

Sensors 2023, 23(14), 6550; https://doi.org/10.3390/s23146550 - 20 Jul 2023

Cited by 1 | Viewed by 901

Abstract

A proactive mobile network (PMN) is a novel architecture enabling extremely low-latency communication. This architecture employs an open-loop transmission mode that prohibits all real-time control feedback processes and employs virtual cell technology to allocate resources non-exclusively to users. However, such a design also [...] Read more.

A proactive mobile network (PMN) is a novel architecture enabling extremely low-latency communication. This architecture employs an open-loop transmission mode that prohibits all real-time control feedback processes and employs virtual cell technology to allocate resources non-exclusively to users. However, such a design also results in significant potential user interference and worsens the communication’s reliability. In this paper, we propose introducing multi-reconfigurable intelligent surface (RIS) technology into the downlink process of the PMN to increase the network’s capacity against interference. Since the PMN environment is complex and time varying and accurate channel state information cannot be acquired in real time, it is challenging to manage RISs to service the PMN effectively. We begin by formulating an optimization problem for RIS phase shifts and reflection coefficients. Furthermore, motivated by recent developments in deep reinforcement learning (DRL), we propose an asynchronous advantage actor–critic (A3C)-based method for solving the problem by appropriately designing the action space, state space, and reward function. Simulation results indicate that deploying RISs within a region can significantly facilitate interference suppression. The proposed A3C-based scheme can achieve a higher capacity than baseline schemes and approach the upper limit as the number of RISs increases. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2023)

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20 pages, 1277 KiB

Open AccessArticle

Deep-Learning-Based Classification of Digitally Modulated Signals Using Capsule Networks and Cyclic Cumulants

by John A. Snoap, Dimitrie C. Popescu, James A. Latshaw and Chad M. Spooner

Sensors 2023, 23(12), 5735; https://doi.org/10.3390/s23125735 - 20 Jun 2023

Cited by 2 | Viewed by 1645

Abstract

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then [...] Read more.

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper outperformed alternative approaches for classifying digitally modulated signals that included conventional classifiers that employed CSP-based techniques, as well as alternative DL-based classifiers that used convolutional neural networks (CNNs) or residual networks (RESNETs) with the in-phase/quadrature (I/Q) data used for training and classification. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2023)

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