Search Results (1,359)

This paper reviews the state-of-the art technologies and techniques for integrating satellite and terrestrial networks within a 5G and Beyond Networks (5GBYNs). It highlights key limitations in existing architectures, particularly in addressing interoperability, resilience, and Quality of Service (QoS) for real-time applications. In response, this work proposes a novel Software-Defined Networking (SDN)-based framework for reliable satellite–terrestrial integration. The proposed framework leverages intelligent traffic steering and dynamic access network selection to optimise real-time communications. By addressing gaps in the literature with a distributed SDN control approach spanning terrestrial and space domains, the framework enhances resilience against disruptions, such as natural disasters, while maintaining low latency and jitter. Future research directions are outlined to refine the design and explore its application in 6G systems. Full article

After the large-scale access of distributed power sources to the distribution network, significant high/low voltage problems have emerged. Using a virtual power plant to provide reactive power voltage regulation as an ancillary service effectively addresses voltage issues. However, since a third party manages the virtual power plant and contains both discrete and continuous regulation devices internally, there is a need to consider privacy protection. To address this, a training method that requires minimal boundary information and reward–penalty information for interaction between discrete and continuous action agents is proposed. This method uses distributed two-layer multi-agent deep reinforcement learning for the virtual power plant’s reactive power voltage support strategy. By utilizing actual engineering data and comparing it with the “centralized training” framework algorithm, this study proves the effectiveness of the deep reinforcement learning training method and reactive power voltage control strategy. It demonstrates advantages such as protecting the privacy of the virtual power plant and low training difficulty. Full article

This study presents a novel approach to enhancing the security and accuracy of photovoltaic (PV) power generation predictions through secure aggregation techniques. The research focuses on key stages of the PV data lifecycle, including data collection, transmission, storage, and analysis. To safeguard against potential attacks and prevent data leakage across these critical processes, Paillier and Brakerski–Gentry–Vaikuntanathan (BGV) homomorphic encryption methods are employed. By integrating the transport layer security (TLS) protocol with edge computing during data transmission, this study not only bolsters data security but also minimizes latency and mitigates threats. Robust strategies for key management, access control, and auditing are implemented to ensure monitored and restricted access, further enhancing system security. In the analysis phase, advanced models such as Long Short-Term Memory (LSTM) networks and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) are utilized for precise time-series predictions of PV power output. The findings demonstrate the effectiveness of these methods in managing large-scale PV datasets while maintaining high prediction accuracy and strong security measures. Full article

Intentional controlled islanding (ICI) is a crucial strategy to avert power system collapse and blackouts caused by severe disturbances. This paper introduces an innovative IoT-based ICI strategy that identifies the optimal location for system segmentation during emergencies. Initially, the algorithm transmits essential data from phasor measurement units (PMUs) to the IoT cloud. Subsequently, it calculates the coherency index among all pairs of generators. Leveraging IoT technology increases system accessibility, enabling the real-time detection of changes in network topology post-disturbance and allowing the coherency index to adapt accordingly. A novel algorithm is then employed to group coherent generators based on relative coherency index values, eliminating the need to transfer data points elsewhere. The “where to island” subproblem is formulated as a mixed integer linear programming (MILP) model that aims to boost system transient stability by minimizing power flow interruptions in disconnected lines. The model incorporates constraints on generators’ coherency, island connectivity, and node exclusivity. The subsequent layer determines the optimal generation/load actions for each island to prevent system collapse post-separation. Signals from the IoT cloud are relayed to the circuit breakers at the terminals of the optimal cut-set to establish stable isolated islands. Additionally, controllable loads and generation controllers receive signals from the cloud to execute load and/or generation adjustments. The proposed system’s performance is assessed on the IEEE 39-bus system through time-domain simulations on DIgSILENT PowerFactory connected to the ThingSpeak cloud platform. The simulation results demonstrate the effectiveness of the proposed ICI strategy in boosting power system stability. Full article

Vehicular communication is revolutionizing transportation by enhancing passenger experience and improving safety through seamless message exchanges with nearby vehicles and roadside units (RSUs). To accommodate the growing number of vehicles in dense urban traffic with limited channel availability, non-orthogonal multiple access (NOMA) is a promising solution due to its ability to improve spectral efficiency by sharing channels among multiple users. However, to completely leverage NOMA on mobile vehicular networks, a chain of operations and resources must be optimized, including vehicle user (VU) and RSU association, channel assignment, and optimal power control. In contrast, traditional orthogonal multiple access (OMA) allocates separate channels to users, simplifying management but falling short in high-density environments. Additionally, enabling NOMA-OMA switching can further enhance the system performance while significantly increasing the complexity of the optimization task. In this study, we propose an optimized framework to jointly utilize the power domain NOMA in a vehicular network, where dynamic NOMA-OMA switching is enabled, by integrating the optimization of vehicle-to-RSU association, channel assignment, NOMA-OMA switching, and transmit power allocation into a single solution. To handle the complexity of these operations, we also propose simplified formulations that make the solution practical for real-time applications. The proposed framework reduces total power consumption by up to $27\%$ compared to Util&LB/opt, improves fairness in user association by $18\%$, and operates efficiently with minimal computational overhead. These findings highlight the potential of the proposed framework to enhance communication performance in dynamic, densely populated urban environments. Full article

The conventional centralized Industrial Internet of Things (IIoT) framework is plagued by issues like subpar security performance and challenges related to storage expansion. This paper proposes a two-tier distributed secure storage framework based on blockchain for IIoT data. The authors first introduce the two-layer framework, which includes the edge network layer and the blockchain storage layer. The nodes in the edge network layer are classified into administrator nodes and ordinary nodes. It provides a lower latency network environment compared to cloud computing to preprocess raw industrial data. The blockchain storage layer provides storage space to keep data secure and traceable. Secondly, the authors propose a differentiated storage solution. Based on the timestamps of industrial data and the specific media access control (MAC) address, the Universally Unique Identifier (UUID) of the raw data is generated and uploaded to the blockchain for secure storage. Encrypt the corresponding raw data using the elliptic curve cryptography algorithm, and then upload it to InterPlanetary File System (IPFS) to expand the storage capacity of the blockchain. Deploy a smart contract on the blockchain to compare UUIDs for consistency in an automated, lightweight method to determine data integrity. Finally, we analyze the advantages brought by the integration of blockchain and IIoT. Additionally, the authors design comparative tests on different storage methods. The results prove that the security of this paper’s scheme is improved, and the storage performance is extended. Noteworthy enhancements include heightened throughput of data uploaded to the blockchain and minimized delay overhead. Full article

Telerobotics involves the operation of robots from a distance, often using advanced communication technologies combining wireless and wired technologies and a variety of protocols. This application domain is crucial because it allows humans to interact with and control robotic systems safely and from a distance, often performing activities in hazardous or inaccessible environments. Thus, by enabling remote operations, telerobotics not only enhances safety but also expands the possibilities for medical and industrial applications. In some use cases, telerobotics bridges the gap between human skill and robotic precision, making the completion of complex tasks requiring high accuracy possible without being physically present. With the growing availability of high-speed networks around the world, especially with the advent of 5G cellular technologies, applications of telerobotics can now span a gamut of scenarios ranging from remote control in the same room to robotic control across the globe. However, there are a variety of factors that can impact the control precision of the robotic platform and user experience of the teleoperator. One such critical factor is latency, especially across large geographical areas or complex network topologies. Consequently, military telerobotics and remote operations, for example, rely on dedicated communications infrastructure for such tasks. However, this creates a barrier to entry for many other applications and domains, as the cost of dedicated infrastructure would be prohibitive. In this paper, we examine the network latency of robotic control over shared network resources in a variety of network settings, such as a local network, access-controlled networks through Wi-Fi and cellular, and a remote transatlantic connection between Finland and the United States. The aim of this study is to quantify and evaluate the constituent latency components that comprise the control feedback loop of this telerobotics experience—of a camera feed for an operator to observe the telerobotic platform’s environment in one direction and the control communications from the operator to the robot in the reverse direction. The results show stable average round-trip latency of 6.6 ms for local network connection, 58.4 ms when connecting over Wi-Fi, 115.4 ms when connecting through cellular, and 240.7 ms when connecting from Finland to the United States over a VPN access-controlled network. These findings provide a better understanding of the capabilities and performance limitations of conducting telerobotics activities over commodity networks, and lay the foundation of our future work to use these insights for optimizing the overall user experience and the responsiveness of this control loop. Full article

The settlement of existing high-speed railway tunnels due to adjacent excavations is a complex phenomenon influenced by multiple factors, making accurate estimation challenging. To address this issue, a prediction model combining extreme gradient boosting (XGBoost) with Bayesian optimization (BO), namely BO-XGBoost, was developed. Its predictive performance was evaluated against conventional models, such as artificial neural networks (ANNs), support vector machines (SVMs), and vanilla XGBoost. The BO-XGBoost model showed superior results, with evaluation metrics of MAE = 0.331, RMSE = 0.595, and R² = 0.997. In addition, the BO-XGBoost model enhanced interpretability through an accessible analysis of feature importance, identifying volume loss as the most critical factor affecting settlement predictions. Using the prediction model and a particle swarm optimization (PSO) algorithm, a hybrid framework was established to adjust the operational parameters of a shield tunneling machine in the Changsha Metro Line 3 project. This framework facilitates the timely optimization of operational parameters and the implementation of protective measures to mitigate excessive settlement. With this framework’s assistance, the maximum settlements of the existing tunnel in all typical sections were strictly controlled within safety criteria. As a result, the corresponding environmental impact was minimized and resource management was optimized, ensuring construction safety, operational efficiency, and long-term sustainability. Full article

For people with diabetes, controlling blood glucose level (BGL) is a significant issue since the disease affects how the body metabolizes food, which makes careful insulin regulation necessary. Patients have to manually check their blood sugar levels, which can be laborious and inaccurate. Many variables affect BGL changes, making accurate prediction challenging. To anticipate BGL many steps ahead, we propose a novel hybrid deep learning model framework based on Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs), which can be integrated into the Internet of Things (IoT)-enabled diabetes management systems, improving prediction accuracy and timeliness by allowing real-time data processing on edge devices. While the GRU layer records temporal relationships and sequence information, the CNN layer analyzes the incoming data to extract significant features. Using a publicly accessible type 1 diabetes dataset, the hybrid model’s performance is compared to that of the standalone Long Short-Term Memory (LSTM), CNN, and GRU models. The findings show that the hybrid CNN-GRU model performs better than the single models, indicating its potential to significantly improve real-time BGL forecasting in IoT-based diabetes management systems. Full article

Reducing energy consumption in a MEC (Multi-Access Edge Computing) system is a critical goal, both for lowering operational expenses and promoting environmental sustainability. In this paper, we focus on the problem of managing the sleep state of MEC servers (MECSs) to decrease the overall energy consumption of a MEC system while providing users acceptable service delays. The proposed method achieves this objective through dynamic orchestration of MECS activation states based on systematic analysis of workload distribution patterns. To facilitate this optimization, we formulate the MECS sleep control mechanism as a constrained combinatorial optimization problem. To resolve the formulated problem, we take a deep-learning approach. We develop a task arrival rate predictor using a spatio-temporal graph convolution network (STGCN). We then integrate this predicted information with the queue length distribution to form the input state for our deep reinforcement learning (DRL) agent. To verify the effectiveness of our proposed framework, we conduct comprehensive simulation studies incorporating real-world operational datasets, with comparative evaluation against established metaheuristic optimization techniques. The results indicate that our method demonstrates robust performance in MECS state optimization, maintaining operational efficiency despite prediction uncertainties. Accordingly, the proposed approach yields substantial improvements in system performance metrics, including enhanced energy utilization efficiency, decreased service delay violation rate, and reduced computational latency in operational state determination. Full article

As security authentication issues continue to arise in future wireless communication networks, researchers are working hard to further improve authentication techniques. Recently, physical layer authentication (PLA) has received widespread attention for its lightweight nature compared to traditional encryption methods based on keys and blockchain. However, the existing PLA mechanisms based on a fixed decision threshold have low reliability in dynamic environments. Moreover, PLA solutions are typically based on binary authentication, and these binary-type schemes cannot provide different levels of access control. To address these challenges, this article introduces the concept of hierarchical security authentication, aiming to achieve multi-level secure authorization access. In order to further improve the accuracy of identity verification, we design an Attention-Enhanced Convolutional Network (AECN) model that integrates the attention mechanism. Specifically, by introducing a confidence score branch, the proposed AECN-based PLA scheme completes authentication without a threshold, thus avoiding the issues stemming from inappropriate threshold settings in conventional PLA schemes. The simulation results show that our proposed AECN framework outperforms existing algorithms at different levels of security authentication. Full article

As the proportion of new energy access increases year by year, the resulting energy imbalance and voltage/trend distribution complexity of the distribution network system in the spatio-temporal dimension become more and more prominent. The joint introduction of electromagnetic rotary power flow controller (RPFC) and energy router (ER) can improve the high proportion of new active distribution network (ADN) consumption and power supply reliability from both spatial and temporal dimensions. To this end, the paper proposes an ADN expansion planning method considering RPFC and ER access. A two-layer planning model for RPFC and ER based on spatio-temporal characteristics is established, with the upper model being the siting and capacity-setting layer, which takes the investment and construction cost of RPFC and ER as the optimization objective, and the lower model being the optimal operation layer, which takes the lowest operating cost of the distribution network as the objective. The planning model is solved by a hybrid optimization algorithm with improved particle swarm and second-order cone planning. The proposed planning model and solving algorithm are validated with the IEEE33 node example, and the results show that the joint access of RPFC and ER can effectively improve the spatial-temporal distribution of voltage in the distribution network and has the lowest equivalent annual value investment and operation cost. Full article

Intelligent Transportation Systems (ITSs) leverage Internet of Things (IoT) technology to facilitate smart interconnectivity among vehicles, infrastructure, and users, thereby optimizing traffic flow. This paper constructs an optimization model for the fresh food supply chain distribution route of fresh products, considering factors such as carbon emissions, time windows, and cooling costs. By calculating carbon emission costs through carbon taxes, the model aims to minimize distribution costs. With a graph attention network structure adopted to describe node locations, accessible paths, and data with collection windows for path planning, it integrates to solve for the optimal distribution routes, taking into account carbon emissions and cooling costs under varying temperatures. Extensive simulation experiments and comparative analyses demonstrate that the proposed time-window-constrained reinforcement learning model provides effective decision-making information for optimizing fresh product fresh food supply chain transportation and distribution, controlling logistics costs, and reducing carbon emissions. Full article

Named data networking (NDN) is a novel networking paradigm characterized by in-network caching, receiver-driven communication, and multi-source, multi-path data retrieval, which poses new challenges for congestion control. Existing work has largely focused on receiver-driven mechanisms. Due to delays in obtaining network control information (timeouts, NACKs) within NDN, consumers are unable to access the network congestion status from this information in a timely manner. To address the issues above, this paper combines the Q-learning algorithm with the NDN architecture, proposing Q-NDN. In Q-NDN, consumers can dynamically adjust the congestion window (cwnd) through the real-time monitoring of network status, leveraging the Q-learning algorithm, achieving automatic congestion control for the NDN architecture. Additionally, this paper introduces content popularity-based traffic scheduling for multi-user scenarioswhich adjusts the transmission rates of content with different popularity levels to maintain a dynamic balance in the network. The experimental results show that Q-NDN can converge quickly, make full use of bandwidth resources, and keep the packet loss rate to 0 in the basic network topology. In competing network topologies, Q-NDN can rapidly address conflict issues, efficiently utilize bandwidth resources, and maintain a relatively low packet loss rate. Full article

For both human health and the environment, air pollution is a serious concern. However, the available air quality monitoring networks have important limitations, such as the high implementation costs, limited portability, and considerable operational complexity. In this context, unmanned aerial systems (UASs) are emerging as a useful technological alternative due to their ability to cover large distances and access areas that are difficult or impossible for humans to reach. This article presents the development of an integrated platform that combines an unmanned aerial system (UAS) with specialized sensors to measure key parameters in relation to air quality, such as carbon monoxide (CO), ozone (O₃), and nitrogen dioxide (NO₂). In addition, a web application called PTECA is developed to visualize the data gathered by the wireless sensor array in real time. The platform incorporates a system that allows real-time tracking of the UAS route and measurement values during sample collection, employing the LoRa communication protocol. This solution represents a low-cost alternative that mitigates some of the limitations of traditional monitoring networks by offering greater portability and accessibility in terms of data collection. Preliminary tests successfully demonstrate the viability of the proposed system in a controlled airspace using geofencing. Full article