最新刊期
卷 9 ， 期 2 ， 2025
- 摘要：Artificial intelligence (AI), cloud computing, big data, and other technologies can not only improve the perception and connection of the railway Internet of things (RIoT) but also provide support for the intelligent analysis of data in the RIoT system. Firstly, based on the theory of smart Internet of things and RIoT, the basic concept and architecture of smart railway Internet of things (SRIoT) were introduced, and the key technologies in SRIoT in detail were expounded. The application scenarios of smart Internet of things in the railway industry were summarized from the perspectives of railway construction, safety monitoring, traffic scheduling, and so on. Focusing on technology and safety, the problems and challenges faced by the railway Internet of things were sorted out. Finally, potential research directions with high value were explored.关键词：internet of things;smart railway;key technology;application scenario;potential direction312|539|0更新时间：2025-09-12
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Outage performance analysis for vehicular communications assisted by intelligent reflecting surfaces
摘要：To address the vehicle-to-vehicle (V2V) link failure caused by severe shadowing in vehicular networks, roadside-based intelligent reflecting surfaces (IRS) were considered to assist the V2V communications, and the outage performance of the vehicular networks with such communication mode was analyzed. Under the Rayleigh fading model, the closed-form expression and asymptotic expression of the outage probability for the perfect phase-shift IRS-assisted V2V communication were derived respectively. The closed-form expression of the outage probability for the non-perfect phase-shift IRS-assisted V2V communication was derived based on an approximate transformation of the moment generating function. Meanwhile, the co-channel interference caused by IRS to the nearby vehicle-to-infrastructure (V2I) communication was considered, and it was proved that the interference signal follows a zero-mean complex Gaussian distribution. Then the closed-form expression of the outage probability of the V2I communication was derived. Simulation results verified the correctness of the theoretical derivations and it was shown that, with minor phase shift error, IRS could effectively enhance the reliability of the V2V communication while causing extremely weak co-channel interference.关键词：Internet of vehicles;IRS;outage probability;co-channel interference;reliability210|222|0更新时间：2025-09-12 - 摘要：With the help of base station cellular networking, the integrated sensing and communications (ISAC) key technologies for 6G can communicate with low-altitude unmanned aerial vehicle (UAV) in a wide range of reliable low-time delay and large bandwidth, and detect, track and identify non-cooperative UAV. Firstly, the key technologies for single base station sensing UAV were analyzed. Then, based on the advantages of cellular base station networking, the key technologies of multi-mode accurate sensing UAV with multi-base station collaboration and vision fusion wireless were proposed. The beam optimization and resource management of multi-base station networking were studied. Finally, an ISAC hardware prototype platform was developed, which could perform UAV tracking and bird identification while maintaining communication services.关键词：integrated sensing and communications;dynamic target sensing;multi-base station collaboration;multi-modal sensing1048|645|0更新时间：2025-09-12
- 摘要：In the highway scenarios, existing offloading models often overlook the network dynamics caused by the high-speed movement of vehicles, leading to increased latency and energy consumption, and exhibit insufficient effectiveness in reducing latency and energy consumption. To address these challenges, an offloading strategy utilizing the prioritized double-buffer pool experience replay twin delayed deep deterministic policy gradient (PD-TD3) algorithm was proposed. Initially, a three-layer distributed offloading model tailored for highway environments was developed. Subsequently, the computation offloading problem was formulated as a Markov decision process (MDP), with the reward function designed to optimize the trade-off between latency and energy consumption, aiming to maximize the reward. To address the limitations of the traditional TD3 algorithm, including slow convergence, Q-value underestimation bias, and inefficient experience sampling, the PD-TD3 algorithm was introduced to solve the optimization problem. Simulation results indicate that, compared with the TD3 algorithm, the PD-TD3 algorithm can effectively improve the efficiency of early algorithm exploration and effectively reduces computation offloading latency by approximately 50% and energy consumption by about 70%.关键词：mobile edge computing offloading;deep reinforcement learning;intelligent vehicle;side-lane synergy;time delay;energy loss149|179|0更新时间：2025-09-12
Frontiers and Progress of Intelligent Transportation IoT
- 摘要：In recent years, the electric vehicle (EV) industry has seen vigorous growth. However, deficiencies in charging infrastructure layout, structure and operation have become obstacles to further market expansion. Thus, relevant policies have listed the Internet of vehicles (IoV), vehicle-grid bidirectional interaction, distributed energy storage, and other charging facilities alongside cutting-edge technological innovations in the smart energy sector as key development priorities. Since traditional grid to vehicle (G2V) charging model struggles to meet the large-scale parallel charging demands of EVs under limited charging infrastructure conditions, the existing structure of energy consumption needs to adapt to the dynamic changes in demand. Against this backdrop, the concept of vehicle to vehicle (V2V) charging has been proposed to alleviate the limitations of G2V charging mode in terms of time and space domains, exploiting the potential of smart EVs as mobile distributed energy storage units. This facilitates flexible energy supply, offering a new approach to optimizing EV charging services and supporting the development of future intelligent transportation system (ITS). Focusing on the optimization direction centered around V2V charging, the relevant researches in recent years were reviewed. Firstly, the charging services in the intelligent transportation scenarios were classified, and an overview of the V2V charging mode was provided. Then, the proposed V2V charging management schemes from a technical emphasis perspective were categorized, and the optimization strategies were elaborated in detail. Finally, the development prospects of V2V charging in ITS were exlored, and open research topics for future studies were discussed.关键词：EV;V2V;ITS;charging service optimization;charging management227|232|0更新时间：2025-09-12
- 摘要：Under the rapid development of new power systems and the high penetration of distributed energy resources, three-phase unbalance issues in distribution networks have become increasingly prominent. This problem not only causes additional losses, but also triggers equipment damage and power supply interruptions, posing significant threats to the secure and stable operation of smart distribution networks. To this end, a method for active location and control recovery of unbalanced problems in smart distribution networks named PowerCause was proposed. The time-series causal inference was introduced into distribution network anomaly analysis, establishing a comprehensive "detection-localization-regulation" solution framework for the first time. By integrating Granger causality tests with adaptive interval detection algorithms, the method achieves unbalanced root cause localization without requiring pre-training or physical topology dependencies. The active regulation system built on OpenDSS incorporates anomaly simulation, multi-dimensional metric collection, and regulation decision-making, forming a closed-loop control system with self-perception, self-diagnosis, and self-recovery capabilities. Simulation results demonstrate the method's competitive performance in root cause localization accuracy and time efficiency, along with strong robustness against environmental disturbances such as measurement noise and data loss errors.关键词：distribution network;unbalance positioning;root cause analysis;active regulation152|170|0更新时间：2025-09-12
- 摘要：With the ongoing proliferation of 5G networks, there has been a surge in the deployment of base station equipment. This trend has not only given rise to new demands within the telecommunications industry for enhancing the overall energy efficiency of 5G base stations and achieving energy conservation and emission reduction but also imposed higher standards on related manufacturers. While traditional reinforcement learning (RL) techniques hold promise for optimizing energy-saving strategies for 5G base stations, they require extensive environmental interactions and model training time. Moreover, in the face of dynamically changing base station operating environments, the variability in state and action spaces can make it difficult for RL to learn effective strategies, and the generalization ability of traditional RL models is also limited. To address these challenges, a new framework for optimizing 5G base station energy-saving control strategies based on the Decision Transformer (DT) model was innovatively proposed. The framework decouples the state and action spaces corresponding to each base station for different scenario tasks and improves the original DT model based on the trajectory priors to optimize the expected return of the model through the prior information of the trajectory data. Simulation results demonstrate that compared to other RL algorithms, the proposed method can significantly reduce system power consumption while ensuring the quality of service for users, and it can adapt to unknown tasks without retraining, showcasing the distinct advantages and application potential of our approach in the context of 5G base station energy-saving decision-making.关键词：5G;energy saving of base station;strategy optimization;reinforcement learning;Decision Transformer model322|184|0更新时间：2025-09-12
- 摘要：Direct current (DC) power supply is the infrastructure of the guarantee of Internet of energy communication. It has many problems, such as heavy workload, high complexity, high risk and waste of electric energy when checking discharge. The remote charge and discharge control based on the architecture of Internet of things by using thyristor chain step-down module was studied. The discharge current compensation control unit was introduced to realize the on-line constant current discharge of the real load. By analyzing the remote measurement and control business requirements of DC power supply, a communication networking scheme for real-time control of charge and discharge was proposed. Aiming at different application scenarios, three automatic online full-capacity capacity checking methods for batteries were proposed. By building a simulation model of the system and control module, the effectiveness of the capacity checking method was verified, and the system energy was fully utilized in the process of capacity checking, which realized the clean and efficient utilization of energy.关键词：Internet of energy;DC power;remote capacity checking;current adaptive compensation;constant current discharge106|190|0更新时间：2025-09-12
- 摘要：The training of semantic models typically consumes a large amount of energy and time, which hinders the implementation of semantic transmission on power Internet of things (IoT) terminals with limited resources. To reduce the energy and time consumption at the terminal, a novel semantic communication architecture was proposed. Firstly, the data to be transmitted was uploaded to a machine learning as a service (MLaaS) platform. Then, the semantic model training was performed on the MLaaS platform, and the model parameters were sent back to the terminal. Finally, semantic reasoning was carried out through the terminal. However, this architecture faces the risk of the MLaaS platform leaking semantic model parameters, leading to the potential eavesdropping of semantic information. Therefore, an anti-eavesdropping method based on feature obfuscation was designed to address the security communication issue of the MLaaS platform during the semantic inference phase. Experimental results show that the proposed method is effective against passive eavesdroppers. The effectiveness of the proposed anti-eavesdropping method was demonstrated. Additionally, the computational overhead and latency of the data obfuscation module on terminals have been preliminarily verified, showing that the method is feasible for practical application on resource-constrained power IoT terminals.关键词：power IoT terminal;semantic communication;machine learning as a service;anti-eavesdropping130|156|0更新时间：2025-09-12
- 摘要：With the in-depth advancement of energy IoT and power IoT, the application of wireless communication technology in the power industry has received more and more attention. Combined with the future new power system control service communication demand, the evolution direction of power wireless private network bearing control service was analyzed. The key technologies of power wireless private network based on its own frequency band and based on unauthorized frequency band were discussed respectively. High-quality service communication and high-efficiency spectrum utilization were taken by the former as the purpose of evolution, and technologies such as low-latency forwarding, interference suppression, and spectrum sharing mechanism needed to be introduced. The edge control network was focused on by the latter, and the emerging IoT technologies for the evolution were integrated, including but not limited to, the terminal pass-through, and narrowband cellular IoT technologies. Finally, the key open propositions of emerging technologies adapted to power wireless private network control services were analyzed, and potential research countermeasures were explored.关键词：power wireless private network;power own frequency;power unlicensed frequency;control services;internet of things84|147|0更新时间：2025-09-12
Internet of Energy Things
- 摘要：Integrated sensing and communication (ISAC) technology aims to alleviate the conflict between communication systems and radar systems over frequency band resources, presenting broad prospects in future 6G. In bistatic scenarios, ISAC systems offer advantages such as resistance to strong electromagnetic interference environments and high concealment. In the existing design of ISAC systems based on linear frequency modulation (LFM) signals in bistatic scenarios, issues such as excessively high range sidelobes and severe false target identification arise, primarily because the radar sensing is affected by the embedded communication information. In order to suppress the sidelobe interference while ensuring the accurate and complete extraction of communication information, thereby enhancing radar sensing performance, the optimization based on an integrated communication and sensing system utilizing LFM was studied. Focusing on modulation schemes and receiver architecture as key points of investigation, two innovative methods were proposed: phase reduction modulation and receiver structure optimization. By employing performance metrics such as ambiguity function and bit error rate, and through comparative simulation analysis, the feasibility and effectiveness of the optimization methods were verified.关键词：integrated sensing and communication;bistatic;waveform optimization;false target identification;linear frequency modulation signal142|179|0更新时间：2025-09-12
- 摘要：Industrial control system (ICS) is related to the normal operation of national key infrastructure. With the increase of system openness, industrial control systems are facing dual risks in the cyber domain and the physical domain. It was no longer applicable to only conduct risk assessment on one aspect of safety or security. A risk assessment method for the security integration of industrial control systems was proposed to address this. In the process of quantitative risk assessment for security integration, risk propagation path analysis, calculation of the likelihood of risk propagation paths, and quantification of the loss value of security risks are the key elements that affect the accuracy of the assessment. Firstly, the method combined the respective advantages of Petri Net and the bow-tie model to analyze both security risk propagation paths, safety risk propagation paths, and risk cross-domain propagation paths. Then, the expert knowledge, trigonometric fuzzy number and centroid formula was used to calculate the possibility of safety risk propagation, and the probability of information security risk propagation was calculated based on the vulnerability scoring system and correction function. Finally, based on the idea of mass factor, a quantitative model of key event loss was given. By quantitatively assessing the risk value of critical events, the effectiveness of the proposed methodology can be further validated in a chemical plant simulation environment.关键词：industrial control system;safety;security;bow-tie;Petri Net;integrated security risk assessment122|176|0更新时间：2025-09-12
- 摘要：In the future wireless cellular networks, the massive access supporting of the Internet of things (IoT) and machine type communication (MTC) has gradually become a pivotal requirement. To reduce collisions and signaling overhead generated by devices during access, grant-free random access (GF-RA) method has been proposed. In GF-RA, the critical task is the joint active device detection and channel estimation (JADCE). However, in practical scenarios, low-cost IoT devices are usually equipped with inexpensive crystal oscillators to reduce costs, thus the frequency offsets are inevitable and seriously degrade the JADCE performance. The sporadic activity pattern of the devices enables the JADCE to be formulated as a large-scale sparsity constraint problem. In order to avoid the non-convexity introduced by the nonlinear between the frequency offsets and the channels, firstly, tensor decomposition was used to model the received signal from the perspective of the preamble sequence, channel, and frequency offset. Then, the alternating least square (ALS) method was exploited to solve the decomposed subproblems in parallel, and the estimated values of device activities, channel response and frequency offsets could be obtained simultaneously. Moreover, in order to make the subproblem strictly convex, the proximal minimization (PM) method was used to add the regularization constraints, which improved the convergence and stability of the proposed JADCE algorithm. Finally, the detection performance of the proposed algorithm was evaluated based on the number of antennas and the length of the preamble sequence. The simulation results show that the proposed JADCE algorithm achieves a missed detection probability close to 1.0×10-3 within the given range of antenna numbers and the preamble sequence length variation, and approaches the normalized mean square error (NMSE) of channel estimation to 1.0×10-6. Compared with the existing algorithms under the frequency offsets, the proposed algorithm has a significant improvement in detection performance.关键词：IoT;massive access;JADCE;tensor decomposition71|109|0更新时间：2025-09-12
- 摘要：As the Internet of things evolves towards low-power consumption and high speed, full duplex cell-free massive multiple input multiple output (CF-mMIMO) networks have gained significant attention, because of the potential for huge channel capacities. In order to effectively reduce the high infrastructure costs and additional power consumption resulting from self-interference cancellation and perfect hardware in full-duplex networks, the research on the performance of CF-mMIMO systems based on network-assisted full duplexing (NAFD) technology under Rician fading channels was explored. Considering imperfect channel estimation, the lower bounds of closed-form expressions for the total achievable rate and spectral efficiency (SE) under low-resolution digital-to-analog converter (DAC) were derived and validated across varying transmission powers and numbers of radio frequency (RF) antennas. With the help of this analytical solution, the effect of parameters, such as DAC resolution and number of terminals on transmission performance, were quantitatively analyzed. Simulation results indicate that equipping a 4~6 bit DAC at the access unit instead of a perfect DAC meets the urgent need for the low-power transmission of the Internet of energy things with only a minimal loss in capacity.关键词：low-power Internet of things;CF-mMIMO;NAFD;SE;low-resolution DAC142|153|0更新时间：2025-09-12
- 摘要：With the rapid advancement of technologies, such as artificial intelligence, the research and application scope of methods such as deep learning in the medical field are expanding increasingly, and the combination of new technologies and clinical practice has become a research hotspot in recent years, demonstrating great potential in the auxiliary diagnosis of ophthalmic diseases. Deep learning algorithms based on convolutional neural networks have shown great potential in tasks, such as disease screening, lesion detection, and tissue segmentation, and have been gradually applied to the diagnosis and screening of various ophthalmic diseases, including glaucoma, age-related macular degeneration, diabetic retinopathy, and cataracts. Firstly, the related works and applications of deep learning in the auxiliary diagnosis of ophthalmic diseases were reviewed, focusing on the datasets, evaluation metrics, and current research progress of various ophthalmic diseases. Secondly, it was found that deep learning achieved remarkable results in the diagnosis of ophthalmic diseases, but it still faced challenges such as small dataset size, class imbalance, and insufficient model interpretability. Finally, the future prospects and development were also discussed.关键词：artificial intelligence;deep learning;ophthalmic diseases;assisted diagnosis176|126|0更新时间：2025-09-12
- 摘要：In the edge computing scenarios, resource-constrained and particiption of the dynamically terminal devices of network in federated learning cause high latency and high energy consumption. An efficient and environmentally friendly federated learning algorithm based on a three-tier cloud-edge-terminal architecture was proposed. Firstly, by introducing model compression techniques into the three-tier federated learning structure, a theoretical analysis was conducted on the model convergence rate, training latency, and energy consumption. Subsequently, based on the theoretical analysis, a problem was formulated to minimize the global model training latency and energy consumption under a certain model convergence rate by jointly optimizing the terminal devices' transmission power, computing power, and model compression rate. Finally, by decomposing the problem into three sub-optimization problems and solving them alternately, a joint alternating optimization algorithm was designed to obtain the optimal solution for the original problem. Experimental results demonstrate that the proposed algorithm is adaptable to large-scale edge computing scenarios. It achieves reductions of 71.54% and 48.76%, respectively, in latency and energy consumption compared with traditional three-layer federated learning algorithms, while ensuring the convergence rate of the model, and effectively reduces the latency and energy consumption generated by global model training.关键词：edge computing;federated learning;model compression;resource allocation;convergence rate311|163|0更新时间：2025-09-12
- 摘要：To optimize the siting of public charging stations for electric vehicles, a method based on the k-means clustering algorithm (K-Means) and randomly occurring distributedly delayed particle swarm optimization (RODDPSO) algorithm was proposed. By integrating K-Means with a variation randomly occurring distributedly delayed particle swarm optimization (VRODDPSO) algorithm, the approach could determine the optimal locations for charging stations according to the charging demands of electric vehicles. Firstly, the RODDPSO algorithm was enhanced by incorporating an adaptive variation strategy. Then, the VRODDPSO algorithm was used to optimize the positions of the K-Means clustering centers. After clustering, the center points of each region were considered as the optimal locations for charging stations. Compared with using the K-Means algorithm alone for three iterations, the improved clustering model effectively addressed the issue of the K-Means algorithm potentially falling into local minima due to inappropriate initial cluster centers, which could result in suboptimal clustering. Finally, in an empirical study on the optimization of public charging station locations in Nanjing, a new evaluation method was proposed. This method assessed the siting quality of charging stations based on their comprehensive utilization rates. The analysis confirms that the integrated K-Means and VRODDPSO algorithm effectively optimizes the positions of the clustered centers, i.e., the locations of charging piles and stations.关键词：K-Means;VRODDPSO;public charging station;site selection81|82|0更新时间：2025-09-12
- 摘要：Sparse network coding, achieved by linearly combining several data blocks before storage, can enhance the cache space utilization of mobile edge caching (MEC) networks, reduce content scheduling overhead and complexity. However, as MEC networks utilize ordinary user devices as cache nodes to store this encoded content, the departure or failure of these user devices undermines the robustness of this approach in the network. In order to address the recovery of encoded content following cache nodes' failures, firstly, a lookup table-based encoding content management method was proposed. This method effectively organized the sparse encoding content within cache nodes. Secondly, a mathematical problem model for content recovery from failures was established, which was demonstrated that this problem belonged to the NP class. Finally, considering the complexity of problem-solving, a heuristic algorithm for recovery of failed nodes was presented to maintain the robustness of the MEC network. This algorithm initially seeked a feasible set of alternative cache nodes, and then proceeded to recover the failed content. Simulation results further validate the effectiveness and good performance of this algorithm in terms of recovery latency and energy consumption.关键词：MEC network;network coding;robustness;recovery algorithm80|141|0更新时间：2025-09-12
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