Search Results (13,520)

This comprehensive article explores the massive MIMO (M-MIMO) design and its associated concepts, focusing on the seamless integration requirements for Beyond 5G (B5G) and 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, cell-free MIMO, and security considerations, this article delves into the intricacies of M-MIMO in the evolving landscape of B5G. Moreover, the emerging MIMO concepts in this article include AI-enabled M-MIMO three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, and THz spectrum utilization. This review highlights the challenges and open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Antenna Arrays, Integrated Sensing with M-MIMO, and the application of federated learning in M-MIMO systems. By examining these cutting-edge developments, this article aims to advance knowledge in the field and inspire future research directions in the exciting realm of B5G and 6G networks. Full article

As Autonomous Vehicles continue to advance and Intelligent Transportation Systems are implemented globally, vehicular ad hoc networks (VANETs) are increasingly becoming a part of the Internet, creating the Internet of Vehicles (IoV). In an IoV framework, vehicles communicate with each other, roadside units (RSUs), and the surrounding infrastructure, leveraging edge, fog, and cloud computing for diverse tasks. These networks must support dynamic vehicular mobility and meet strict Quality of Service (QoS) requirements, such as ultra-low latency and high throughput. Terrestrial wireless networks often fail to satisfy these needs, which has led to the integration of Unmanned Aerial Vehicles (UAVs) into IoV systems. UAV transceivers provide superior line-of-sight (LOS) connections with vehicles, offering better connectivity than ground-based RSUs and serving as mobile RSUs (mRSUs). UAVs improve IoV performance in several ways, but traditional optimization methods are inadequate for dynamic vehicular environments. As a result, recent studies have been incorporating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into UAV-assisted IoV systems to enhance network performance, particularly in complex areas like resource allocation, routing, and mobility management. This survey paper reviews the latest AI/ML research in UAV-IoV networks, with a focus on resource and trajectory management and routing. It analyzes different AI techniques, their training features, and architectures from various studies; addresses the limitations of AI methods, including the demand for computational resources, availability of real-world data, and the complexity of AI models in UAV-IoV contexts; and considers future research directions in UAV-IoV. Full article

With the rapid development of wireless communication technologies and the miniaturization trend in the electronics industry, the reduction of electromagnetic interference has become an important issue. To solve this problem, a lot of attention has been focused on polymer composites with combined functional fillers. In this paper, we report a method for creating an acrylonitrile butadiene styrene (ABS) plastic composite with a low amount of conductive carbon and magnetic fillers preparation. Also, we investigate the mechanical, thermophysical, and electrodynamic characteristics of the resulting composites. Increasing the combined filler amount in the ABS composite from 1 to 5 wt % leads to a composite conductivity growth of almost 50 times. It is necessary to underline the temperature decrease of 5 wt % mass loss and, accordingly, the composite heat resistance reduction with an increase in the combined filler from 1 to 5 wt %, while the thermal conductivity remains almost constant. It was established that electrodynamic and physical–mechanical characteristics depend on the agglomeration of fillers. This work is expected to reveal the potential of combining commercially available fillers to construct effective materials with good electromagnetic interference (EMI) protection using mass production methods (extrusion and injection molding). Full article

In the power grid, inspection robots enhance operational efficiency and safety by inspecting power lines for information sharing and interaction. Edge computing improves computational efficiency by positioning resources close to the data source, supporting real-time fault detection and line monitoring. However, large data volumes and high latency pose challenges. Existing offloading strategies often neglect task divisibility and priority, resulting in low efficiency and poor system performance. This paper constructs a power grid inspection offloading scenario using Python 3.11.2 to study and improve various offloading strategies. Implementing a game-theory-based distributed computation offloading strategy, simulation analysis reveals issues with high latency and low resource utilization. To address these, an improved game-theory-based strategy is proposed, optimizing task allocation and priority settings. By integrating local and edge computing resources, resource utilization is enhanced, and latency is significantly reduced. Simulations show that the improved strategy lowers communication latency, enhances system performance, and increases resource utilization in the power grid inspection context, offering valuable insights for related research. Full article

In the digital age, spreading false information has a far-reaching impact on various areas, such as society, politics, and the economy. With the popularization of applications of text generation models, the cost of producing false information has significantly decreased, making it challenging for human beings to screen it. Therefore, research on detection screening and early warning control for model-generated false information becomes particularly important. In this paper, we propose a model-generated false information detection and control system based on blockchain. Firstly, we design a model-generated false information detection method combining model-generated text discrimination based on a self-attention network and text similarity detection based on a twin network. Secondly, we construct a blockchain-based model-generated false information control and traceability system. It utilizes the proposed detection algorithm to provide early warning and control of model-generated false information involving important and sensitive events before social network release. For information judged to be model-generated false, the stored data on the blockchain is utilized to track and trace the publisher. Ultimately, experimental tests prove that the proposed detection method improves the accuracy of false information detection. In addition, the operational efficiency of the prototype system can meet quality of service requirements. Full article

Modular integrated construction (MiC) is now widely adopted by industry and governments. However, its fragile and delicate logistics are still a concern for impeding project performance. MiC logistic operations involve rigorous multimode transportation, loading-unloading, and stacking during storage. Such processes may induce latent and intrinsic damage to the module. This damage causes safety hazards during assembly and deteriorates the module’s structural health during the building use phase. Also, additional inspection and repairs before assembly cause uncertainties and can delay the whole supply chain. Therefore, continuous monitoring of the module’s structural response during MiC logistics and the building use phase is vital. An IoT-based multi-sensing system is developed, integrating an accelerometer, gyroscope, and strain sensors to measure the module’s structural response. The compact, portable, wireless sensing devices are designed to be easily installed on modules during the logistics and building use phases. The system is tested and calibrated to ensure its accuracy and efficiency. Then, a detailed field experiment is demonstrated to assess the damage, safety, and structural health during MiC logistic operations. The demonstrated damage assessment methods highlight the application for decision-makers to identify the module’s structural condition before it arrives on site and proactively avoid any supply chain disruption. The developed sensing system is directly helpful for the industry in monitoring MiC logistics and module structural health during the use phase. The system enables the researchers to investigate and improve logistic strategies and module design by accessing detailed insights into the dynamics of MiC logistic operations. Full article

A Wireless Body Area Network (WBAN), introduced into the healthcare sector to improve patient care and enhance the efficiency of medical services, also brings the risk of the leakage of patients’ privacy. Therefore, maintaining the communication security of patients’ data has never been more important. However, WBAN faces issues such as open medium channels, resource constraints, and lack of infrastructure, which makes the task of designing a secure and economical communication scheme suitable for WBAN particularly challenging. Signcryption has garnered attention as a solution suitable for resource-constrained devices, offering a combination of authentication and confidentiality with low computational demands. Although the advantages offered by existing certificateless signcryption schemes are notable, most of them only have proven security within the random oracle model (ROM), lack public ciphertext authenticity, and have high computational overheads. To overcome these issues, we propose a certificateless anonymous signcryption (CL-ASC) scheme suitable for WBAN, featuring anonymity of the signcrypter, public verifiability, and public ciphertext authenticity. We prove its security in the standard model, including indistinguishability, unforgeability, anonymity of the signcrypter, and identity identifiability, and demonstrate its superiority over relevant schemes in terms of security, computational overheads, and storage costs. Full article

Compared to the traditional wet-mate underwater power supply method, Magnetic Coupling Resonant Wireless Power Transfer (MCR-WPT) technology boasts advantages such as excellent insulation, high safety, and convenient operation, showing promising application prospects in the field of power supply for underwater vehicles and other mobile underwater devices. In order to explore the transmission characteristics of this technology underwater, this article first establishes a traditional mathematical model, and then modifies the underwater model through analysis of changes in coil self-inductance and mutual inductance, as well as the impact of eddy current losses. Using the modified mathematical model of the underwater MCR-WPT system, the transmission characteristics are analyzed, and simulations and experimental validations are performed using MATLAB R2022a software. In the study of frequency characteristics, it is found that the system operates optimally when both ends of the circuit work at the resonant state; that is, when f_input = f_resonance = 100 kHz, the output performance is at its best, and the optimal resonant frequency significantly improves power and transmission efficiency. When the input frequency is less than 87.3 kHz or greater than 122.9 kHz, the output power decreases to less than half of the maximum power. In the investigation of load effects, the optimal load for maximizing system output power was identified, but the load that maximizes transmission efficiency is different from this optimal load. This study provides strong theoretical support and guidance for improving the performance of underwater wireless power transmission systems. Full article

In this paper, a single-feed microstrip antenna (MA) equipped with a transmission-mode focusing metasurface (MS) is proposed to achieve dual-polarization capabilities and superior high-gain radiation performance. The original-feed MA comprises two distinct layers of coaxial-fed tangential patches, enabling it to emit a circular polarization (CP) wave with a gain of 3.5 dBic at 5.6 GHz and linear polarization (LP) radiation with a gain of 4 dBi at 13.7 GHz. To improve the performance of the single-feed MA, a dual-polarization transmission focusing MS is proposed and numerically substantiated. By positioning the originally designed MA at the focal point of the MS, we create a transmission-mode MS antenna system capable of achieving CP and LP radiations with the significantly higher gains of 12.9 dBic and 14.8 dBi at 5.6 GHz and 13.7 GHz, respectively. Measurements conducted on the fabricated dual-polarization focusing MS antenna closely align with the simulation results, validating the effectiveness of our approach. This work underscores the significant potential of dual-polarization high-speed data systems and offers a practical solution for enhancing antenna gains in contemporary wireless communication systems. Full article

The transmission environment of underwater wireless sensor networks is open, and important transmission data can be easily intercepted, interfered with, and tampered with by malicious nodes. Malicious nodes can be mixed in the network and are difficult to distinguish, especially in time-varying underwater environments. To address this issue, this article proposes a GAN-based trusted routing algorithm (GTR). GTR defines the trust feature attributes and trust evaluation matrix of underwater network nodes, constructs the trust evaluation model based on a generative adversarial network (GAN), and achieves malicious node detection by establishing a trust feature profile of a trusted node, which improves the detection performance for malicious nodes in underwater networks under unlabeled and imbalanced training data conditions. GTR combines the trust evaluation algorithm with the adaptive routing algorithm based on Q-Learning to provide an optimal trusted data forwarding route for underwater network applications, improving the security, reliability, and efficiency of data forwarding in underwater networks. GTR relies on the trust feature profile of trusted nodes to distinguish malicious nodes and can adaptively select the forwarding route based on the status of trusted candidate next-hop nodes, which enables GTR to better cope with the changing underwater transmission environment and more accurately detect malicious nodes, especially unknown malicious node intrusions, compared to baseline algorithms. Simulation experiments showed that, compared to baseline algorithms, GTR can provide a better malicious node detection performance and data forwarding performance. Under the condition of 15% malicious nodes and 10% unknown malicious nodes mixed in, the detection rate of malicious nodes by the underwater network configured with GTR increased by 5.4%, the error detection rate decreased by 36.4%, the packet delivery rate increased by 11.0%, the energy tax decreased by 11.4%, and the network throughput increased by 20.4%. Full article

The wide-ranging applications of the Internet of Things (IoT) show that it has the potential to revolutionise industry, improve daily life, and overcome global challenges. This study aims to evaluate the performance scalability of mature industrial wireless sensor networks (IWSNs). A new classification approach for IoT in the industrial sector is proposed based on multiple factors and we introduce the integration of 6LoWPAN (IPv6 over low-power wireless personal area networks), message queuing telemetry transport for sensor networks (MQTT-SN), and ContikiMAC protocols for sensor nodes in an industrial IoT system to improve energy-efficient connectivity. The Contiki COOJA WSN simulator was applied to model and simulate the performance of the protocols in two static and moving scenarios and evaluate the proposed novelty detection system (NDS) for network intrusions in order to identify certain events in real time for realistic dataset analysis. The simulation results show that our method is an essential measure in determining the number of transmissions required to achieve a certain reliability target in an IWSNs. Despite the growing demand for low-power operation, deterministic communication, and end-to-end reliability, our methodology of an innovative sensor design using selective surface activation induced by laser (SSAIL) technology was developed and deployed in the FTMC premises to demonstrate its long-term functionality and reliability. The proposed framework was experimentally validated and tested through simulations to demonstrate the applicability and suitability of the proposed approach. The energy efficiency in the optimised WSN was increased by 50%, battery life was extended by 350%, duplicated packets were reduced by 80%, data collisions were reduced by 80%, and it was shown that the proposed methodology and tools could be used effectively in the development of telemetry node networks in new industrial projects in order to detect events and breaches in IoT networks accurately. The energy consumption of the developed sensor nodes was measured. Overall, this study performed a comprehensive assessment of the challenges of industrial processes, such as the reliability and stability of telemetry channels, the energy efficiency of autonomous nodes, and the minimisation of duplicate information transmission in IWSNs. Full article

Foreign object debris (FOD) includes any unwanted and unintentional material lying on the charging lane or parking lots, posing a risk to the wireless charging system, the vehicle, or the people inside. FOD in an Electric Vehicle (EV) wireless charging system can cause problems, including decreased charging efficiency, safety risks, charging system damage, communication issues, and health risks. To address this problem, this paper proposes the deep learning object detection network approach of using YOLOv4 (You Only Look Once), which is a single-shot detector. Additionally, for real-time implementation, YOLOv4-Tiny is suggested, which is a compressed version of YOLOv4 designed for devices with low computational power. YOLOv4-Tiny enables faster inferences and facilitates the deployment of FOD detectors on edge devices. The algorithm is trained using the FOD dataset, consisting of images of common debris on runways or taxiways. Furthermore, utilizing the concept of transfer learning, the last few layers of the pre-trained YOLOv4 model are modified using the COCO (Common Objects in Context) dataset to transfer features to the new network and retrain the model on the FOD dataset. The results obtained using this YOLOv4 model yielded a precision rate of 99.05%, while the results from YOLOv4-Tiny achieved a precision rate of 97.74%, with an average inference time of 150 ms under the ambient light and weather conditions. Full article

Interest in deploying deep reinforcement learning (DRL) models on low-power edge devices, such as Autonomous Mobile Robots (AMRs) and Internet of Things (IoT) devices, has seen a significant rise due to the potential of performing real-time inference by eliminating the latency and reliability issues incurred from wireless communication and the privacy benefits of processing data locally. Deploying such energy-intensive models on power-constrained devices is not always feasible, however, which has led to the development of model compression techniques that can reduce the size and computational complexity of DRL policies. Policy distillation, the most popular of these methods, can be used to first lower the number of network parameters by transferring the behavior of a large teacher network to a smaller student model before deploying these students at the edge. This works well with deterministic policies that operate using discrete actions. However, many real-world tasks that are power constrained, such as in the field of robotics, are formulated using continuous action spaces, which are not supported. In this work, we improve the policy distillation method to support the compression of DRL models designed to solve these continuous control tasks, with an emphasis on maintaining the stochastic nature of continuous DRL algorithms. Experiments show that our methods can be used effectively to compress such policies up to 750% while maintaining or even exceeding their teacher’s performance by up to 41% in solving two popular continuous control tasks. Full article

This paper presents an energy-efficient and high-accuracy sampling synchronization approach for real-time synchronous data acquisition in wireless sensor networks (saWSNs). A proprietary protocol based on time-division multiple access (TDMA) and deep energy-efficient coding in sensor firmware is proposed. A real saWSN model based on 2.4 GHz nRF52832 system-on-chip (SoC) sensors was designed and experimentally tested. The obtained results confirmed significant improvements in data synchronization accuracy (even by several times) and power consumption (even by a hundred times) compared to other recently reported studies. The results demonstrated a sampling synchronization accuracy of 0.8 μs and ultra-low power consumption of 15 μW per 1 kb/s throughput for data. The protocol was well designed, stable, and importantly, lightweight. The complexity and computational performance of the proposed scheme were small. The CPU load for the proposed solution was <2% for a sampling event handler below 200 Hz. Furthermore, the transmission reliability was high with a packet error rate (PER) not exceeding 0.18% for TXPWR ≥ −4 dBm and 0.03% for TXPWR ≥ 3 dBm. The efficiency of the proposed protocol was compared with other solutions presented in the manuscript. While the number of new proposals is large, the technical advantage of our solution is significant. Full article

This article presents a coordinated control method used for wireless power transfer (WPT) systems. This method can improve WPT system transmission efficiency while maintaining the constant output voltage. First, the topology of the DC–DC converter is selected and the equivalent circuit model of the WPT system is established. Then, the WPT system characteristics are discussed and the mutual inductance estimation process is presented. Furthermore, the coordinated control method is proposed, where the constant voltage output is achieved by connecting the Buck–Boost converter after the diode rectifier. Meanwhile, the optimal phase shift angle is calculated and sent to the controller to achieve maximum transmission efficiency tracking control, according to the measured load voltage and current. Finally, simulations and experiments are adopted to verify the proposed coordinated control method. The experimental results indicate that the average system transmission efficiency is increased by 1.80% and the efficiency fluctuation is decreased by 2.67% when the system load resistance varies, while the average system transmission efficiency is increased by 1.80%, and the efficiency fluctuation is decreased by 3.14% when the mutual inductance changes. This means the proposed coordinated control method is effective under the conditions of the WPT load and mutual inductance variations. Full article