Search Results (34)

This study presents a new intelligent aquatic farming surveillance system that tackles real-time monitoring challenges in the industry. The main technical break-throughs of this system are evident in four key aspects: First, it achieves the smooth integration of remotely operated vehicles (ROVs), sensors, and real-time data transmission. Second, it uses a mobile communication architecture with buoy relay stations for distributed edge computing. This design supports future upgrades to Beyond 5G and satellite networks for deep-sea applications. Third, it features a multi-terminal control system that supports computers, smartphones, smartwatches, and centralized hubs, effectively enabling monitoring anytime, anywhere. Fourth, it incorporates a cost-effective modular design, utilizing commercial hardware and innovative system integration solutions, making it particularly suitable for farms with limited resources. The data indicates that the system’s 4G connection is both stable and reliable, demonstrating excellent performance in terms of data transmission success rates, control command response delays, and endurance. It has successfully processed 324,800 data transmission events, thoroughly validating its reliability in real-world production environments. This system integrates advanced technologies such as the Internet of Things, mobile communications, and multi-access control, which not only significantly enhance the precision oversight capabilities of marine farming but also feature a modular design that allows for future expansion into satellite communications. Notably, the system reduces operating costs while simultaneously improving aquaculture efficiency, offering a practical and intelligent solution for small farmers in resource-limited areas. Full article

The safe operation of freight train equipment is crucial to the stability of the transportation system. With the advancement of intelligent monitoring technology, vision-based anomaly detection methods have gradually become an essential approach to train equipment condition monitoring. However, due to the complexity of train equipment inspection scenarios, existing methods still face significant challenges in terms of accuracy and generalization capability. Freight trains defect detection models are deployed on edge computing devices, onboard terminals, and fixed monitoring stations. Therefore, to ensure the efficiency and lightweight nature of detection models in industrial applications, we have improved the YOLOv8 model structure and proposed a network architecture better suited for train equipment anomaly detection. We adopted the lightweight MobileNetV4 as the backbone to enhance computational efficiency and adaptability. By comparing it with other state-of-the-art lightweight networks, we verified the superiority of our approach in train equipment defect detection tasks. To enhance the model’s ability to detect objects of different sizes, we introduced the Content-Guided Attention Fusion (CGAFusion) module, which effectively strengthens the perception of both global context and local details by integrating multi-scale features. Furthermore, to improve model performance while meeting the lightweight requirements of industrial applications, we incorporated a staged knowledge distillation strategy on large-scale datasets. This approach significantly reduces model parameters and computational costs while maintaining high detection accuracy. Extensive experiments demonstrate the effectiveness and efficiency of our method, proving its competitiveness compared with other state-of-the-art approaches. Full article

Nowadays, the third-generation semiconductor led by GaN has brought great changes to the semiconductor industry. Utilizing its characteristics of a wide bandgap, high breakdown Electric field, and high electron mobility, GaN material is widely applied in areas such as 5G communication and electric vehicles to improve energy conservation and reduce emissions. However, with the progress in the development of GaN electronics, surface and interface defects have become a main problem that limits the further promotion of their performance and stability, increasing leakage current and causing degradation in breakdown voltage. Thus, to reduce the damage, Plasma treatment technologies are introduced in the fabrication process of GaN electronics. Up to now, designs like the high-resistivity p-GaN cap Layer, passivating termination, and surface recovery process have been established via Plasma treatment, reaching the goals of normally-off transistors, diodes with high breakdown voltage and high-reliability GaN electronics, etc. In this article, hydrogen, fluorine, oxygen, and nitrogen Plasma treatment technologies will be discussed, and their application in GaN electronics will be reviewed and compared. Full article

Durban Port in South Africa is the largest container port and the busiest shipping terminal in sub-Saharan Africa. Approximately 60% of the country’s containerised cargo and 40% of break-bulk cargo transit through Durban. The port is near the central business district, which has a positive spin-off in terms of tourism, recreation, and accessibility to transport and other business activities. The juxtaposition of industry, the port, and the community has resulted in sustained public health implications, a relic of the apartheid era. Like most ports in Africa, Durban Port lacks proper quantification of emissions from marine mobile sources. This study is aimed at estimating atmospheric emissions from ocean-going vessels (OGVs) in and around Durban Port for a period of one year from 1 January 2018 to 31 December 2018 using a mid-tier (activity-based) approach to supplement existing understandings of emissions from local industries. Emission estimates were then inputted to the AERMOD atmospheric dispersion model to allow for a comparison between ambient concentrations and national ambient air quality standards to assess potential health impacts. The study is an advancement in understanding the impact of mobile sources, particularly shipping, on air quality and health, and offers an example for other African ports to follow. Full article

The rapid development of artificial intelligence (AI) and 5G paradigm brings infinite possibilities for data annotation for new applications in the industrial Internet of Things (IIoT). However, the problem of data annotation consistency under distributed architectures and growing concerns about issues such as data privacy and cybersecurity are major obstacles to improving the quality of distributed data annotation. In this paper, we propose a reputation-based asynchronous federated learning approach for digital twins. First, this paper integrates digital twins into an asynchronous federated learning framework, and utilizes a smart contract-based reputation mechanism to enhance the interconnection and internal interaction of asynchronous mobile terminals. In addition, in order to enhance security and privacy protection in the distributed smart annotation system, this paper introduces blockchain technology to optimize the data exchange, storage, and sharing process to improve system security and reliability. The data results show that the consistency of our proposed FedDTrep distributed intelligent labeling system reaches 99%. Full article

Under the auspice of further developing 5G mobile communication technology and integrating it with the latest advancements in the field of Industrial Internet-of-Things, this study conducts in-depth research and detailed analysis on the combination of 5G with industrial systems based on composite structures, communication network architectures, wireless application scenarios, and communication protocols. The status quo, development trend, and necessity of 5G mobile communication technology are explored and its potential in industrial applications is analyzed. Based on the current practical development level of 5G technology, by considering different requirements for bandwidth, real-time performance, and reliability in communication networks of industrial systems, this study proposes three feasible paths for the integration between 5G and industrial systems, including the method to use 5G in place of field buses. Finally, by introducing real-world cases, this study has successfully demonstrated the integration of 5G and industrial systems by extending 5G terminals as field bus gateways. This study provides valuable references for research and practice in related fields. Full article

In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of $O\left(n^2\right)$ to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution. Full article

With the vigorous development of industries such as self-driving, edge intelligence, and the industrial Internet of Things (IoT), the amount and type of data generated are unprecedentedly large, and users’ demand for high-quality services continues to increase. Edge computing has emerged as a new paradigm, providing storage, computing, and networking resources between traditional cloud data centers and end devices with solid timeliness. Therefore, the resource allocation problem in the online task offloading process is the main area of research. It is aimed at the task offloading problem of delay-sensitive customers under capacity constraints in the online task scenario. In this paper, a new edge collaborative online task offloading management algorithm based on the deep reinforcement learning method OTO-DRL is designed. Based on that, a large number of simulations are carried out on synthetic and real data sets, taking obstacle recognition and detection in unmanned driving as a specific task and experiment. Compared with other advanced methods, OTO-DRL can well realize the increase in the number of tasks requested by mobile terminal users in the field of edge collaboration while guaranteeing the service quality of task requests with higher priority. Full article

Driving behavior recognition can provide an important reference for the intelligent vehicle industry and probe vehicle-based traffic estimation. The identification of driving behavior using mobile sensing techniques such as smartphone- and vehicle-mounted terminals has gained significant attention in recent years. The present work proposed the monitoring of longitudinal driving behavior using a machine learning approach with the support of an on-board unit (OBU). Specifically, based on velocity, three-axis acceleration and three-axis angular velocity data were collected by a mobile vehicle terminal OBU; through the process of data preprocessing and feature extraction, seven machine learning algorithms, including support vector machine (SVM), random forest (RF), k-nearest neighbor algorithm (KNN), logistic regression (LR), BP neural network (BPNN), decision tree (DT), and the Naive Bayes (NB), were applied to implement the classification and monitoring of the longitudinal driving behavior of probe vehicles. The results show that the three classifiers SVM, RF and DT achieved good performances in identifying different longitudinal driving behaviors. The outcome of the present work could contribute to the fields of traffic management and traffic safety, providing important support for the realization of intelligent transport systems and the improvement of driving safety. Full article

The construction of the Industrial Internet has become a concrete implementation and an essential starting point in accelerating the digital transformation and intelligent upgrading of industrial manufacturing enterprises. The problem of addressing and forwarding for resource-constrained devices restricts data acquisition and dissemination on the Industrial Internet. In order to retrieve content reasonably, we propose IDEANE, an identity-differentiated content acquisition and multipath forwarding scheme with network coding and edge computing. In IDEANE, content requests are disseminated based on the identity and location information carried in a multi-identifier network, which can improve the efficiency of interest message requests and reduce idle links in the network. Moreover, the content acquisition computation is offloaded to multiple edge nodes, and the encoded data are transmitted to the edge nodes for recovery. IDEANE offloads the less demanding computing tasks to the edge nodes close to the content requesters, to relieve the pressure on providers. In addition, a collaboration method among multiple edge nodes is also studied. Multiple edge nodes collaborate to support the mobility of content requesters, save energy consumption in terminal devices, reduce transmission latency, and ensure data security. The experimental results show that IDEANE can avoid duplicated transmission, reduce the network link overhead, improve network throughput, and enhance network robustness and reliability. Full article

Industrial Internet mobile edge computing (MEC) deploys edge servers near base stations to bring computing resources to the edge of industrial networks to meet the energy-saving requirements of Industrial Internet terminal devices. This paper considers a wireless MEC system in an intelligent factory that has multiple edge servers and mobile smart industrial terminal devices. In this paper, the terminal device has the choice of either offloading the task in whole or in part to the edge server, or performing it locally. Through combined optimization of the task offload ratio, number of subcarriers, transmission power, and computing frequency, the system can achieve minimum total energy consumption. A computing offloading and resource allocation approach that combines federated learning (FL) and deep reinforcement learning (DRL) is suggested to address the optimization problem. According to the simulation results, the proposed algorithm displays fast convergence. Compared with baseline algorithms, this algorithm has significant advantages in optimizing the performance of energy consumption. Full article

Visible light communication (VLC) has the advantages of rich spectrum resources, endogenous safety and anti-electromagnetic interference, so the application of VLC to the Industrial Internet of Things is one of its current development directions. Due to the limited coverage of light emitting diodes (LEDs), dense placement is often required in industrial manufacturing scenarios. However, mobile users will face frequent handover between these LEDs, and reliable reception of signals on mobile terminals requires receivers with a large detection area. In this paper, we used a cubic receiver to increase the detection area, which is more conducive to signal reception than a single photo-detector plane receiver. Then we studied the handover scheme for a cubic receiver to ensure the performance of the communication link. We also rotated the receiver to further improve signal quality and obtain the optimal rotation angles using a genetic algorithm with low complexity. The results show that a cubic receiver has a better received signal quality than a plane receiver, and rotation can enhance the signal quality more. In addition, it can be seen that the handover for the cubic receiver depends not only on the distance, but also on its structure, angle and position. Considering these factors jointly when performing a handover, users can connect to LEDs, which provide the best quality of service, and system communication performance can be improved. Full article

The fifth-generation communication technology (5G) and information-centric networks (ICNs) are acquiring more and more attention. Cache plays a significant part in the 5G-ICN architecture that the industry has suggested. 5G mobile terminals switch between different base stations quickly, creating a significant amount of traffic and a significant amount of network latency. This brings great challenges to 5G-ICN mobile cache. It appears urgent to improve the cache placement strategy. This paper suggests a hybrid caching strategy called time segmentation-based hybrid caching (TSBC) strategy, based on the 5G-ICN bearer network infrastructure. A base station’s access frequency can change throughout the course of the day due to the “tidal phenomena” of mobile networks. To distinguish the access frequency, we split each day into periods of high and low liquidity. To maintain the diversity of cache copies during periods of high liquidity, we replace the path’s least-used cache copy. We determine the cache value of each node in the path and make caching decisions during periods of low liquidity to make sure users can access the content they are most interested in quickly. The simulation results demonstrate that the proposed strategy has a positive impact on both latency and the cache hit ratio. Full article

Emerging electric vehicle (EV) technology requires high-voltage energy storage systems, efficient electric motors, electrified power trains, and power converters. If we consider forecasts for EV demand and driving applications, this article comprehensively reviewed power converter topologies, control schemes, output power, reliability, losses, switching frequency, operations, charging systems, advantages, and disadvantages. This article is intended to help engineers and researchers forecast typical recharging/discharging durations, the lifetime of energy storage with the help of control systems and machine learning, and the performance probability of using AlGaN/GaN heterojunction-based high-electron-mobility transistors (HEMTs) in EV systems. The analysis of this extensive review paper suggests that the Vienna rectifier provides significant performance among all AC–DC rectifier converters. Moreover, the multi-device interleaved DC–DC boost converter is best suited for the DC–DC conversion stage. Among DC–AC converters, the third harmonic injected seven-level inverter is found to be one of the best in EV driving. Furthermore, the utilization of multi-level inverters can terminate the requirement of the intermediate DC–DC converter. In addition, the current status, opportunities, challenges, and applications of wireless power transfer in hybrid and all-electric vehicles were also discussed in this paper. Moreover, the adoption of wide bandgap semiconductors was considered. Because of their higher power density, breakdown voltage, and switching frequency characteristics, a light yet efficient power converter design can be achieved for EVs. Finally, the article’s intent was to provide a reference for engineers and researchers in the automobile industry for forecasting calculations. Full article

The use of wireless communications systems on the factory shop floor is becoming an appealing solution with many advantages compared to cable-based solutions, including low cost, easy deployment, and flexibility. This, combined with the continuous growth of low-cost mobile devices, creates opportunities to develop innovative and powerful applications that, in many cases, rely on computing and memory-intensive algorithms and low-latency requirements. However, as the density of connected wireless devices increases, the spectral noise density rises, and, consequently, the radio interference between radio devices increase. In this paper, we discuss how the density of AR/VR mobile applications with high throughput and low latency affect industrial environments where other wireless devices use the same frequency channel. We also discuss how the growing number of these applications may have an impact on the radio interference of wireless networks. We present an agnostic methodology to assess the radio interferences between wireless communication systems on the factory floor by using appropriate radio and system models. Several interference scenarios are simulated between commonly used radio systems: Bluetooth, Wi-Fi, and WirelessHART, using SEAMCAT. For a 1% probability of interference and considering a criterion of C/I = 14 dB, the simulations on an 80 m × 80 m factory shop floor show that low-bandwidth systems, such as Bluetooth and WirelessHART, can coexist with high-bandwidth and low-latency AR/VR applications running on Wi-Fi mobile terminals if the number of 11 Wi-Fi access points and 80 mobile AR/VR devices transmitting simultaneously is not exceeded. Full article