Digital Twin (DT) and mobile edge computing are two promising solutions for achieving latency-sensitive and computing-intensive applications in Industrial Internet of Things (IIoT). However, existing collaborative task offloading schemes with DT empowerment are faced with challenges, such as the complex collaborative relationship between tasks and multiple Edge Servers (ESs), and spatio-temporal heterogeneity of ES resources. This paper investigates the issues of parallel collaborative offloading and resource allocation under the assistance of DT and Overlapping Coalition Formation (OCF) game. One novel scheme, abbreviated as OCF-based PCORA, is proposed. With comprehensive information within the digital space, the OCF-based PCORA scheme dynamically models collaborative relationships between tasks and ESs. Subsequently, each task is offloaded to its optimal ES coalition for efficient collaborative processing. The offloading request is formulated as a non-convex problem. To make the non-convex problem solvable in polynomial time, the original problem is decomposed into three subproblems: ED association, bandwidth allocation, and collaborative task processing. The first subproblem is transformed through slack variable relaxation and solved using the interior-point method. The second subproblem, being naturally convex, is addressed via convex optimization method. For the third subproblem is modeled as an OCF game with transferable utility. On top of this, a bilevel iterative optimization algorithm is proposed to form overlapping task coalitions in a distributed manner. Numerical results demonstrate that the proposed scheme reduces the average task completion latency by 16.01%–44.97% and decreases the task offloading failure rate by 28.99%–75.41%, compared to state-of-the-art baselines.

Accurate mobile network traffic prediction is crucial for transit infrastructure service optimization in industrial informatization. Traditional linear models fail to capture complex non-linear dynamics, while existing deep learning methods struggle with rapid temporal changes, signal fluctuations, and diverse network conditions, limiting real-time applicability. To address these challenges, this paper proposes a hybrid model integrating Convolutional Neural Networks (CNNs) and Transformers, tailored for High-Speed Railway (HSR) environments. The proposed hybrid model is evaluated using both public datasets and a real-world HSR dataset collected through empirical field measurements, it not only achieves state-of-the-art (SOTA) predictive accuracy, reducing root mean square error by 4.7% over strong baselines in the challenging HSR environment, but also delivers this performance with superior computational efficiency, achieving over 3.6 times lower inference latency than leading SOTA models. This establishes an optimal performance-to-cost ratio, demonstrating its practical value for real-time HSR systems.

A metaverse can bring many benefits (i.e., self-sustaining and proactive analytics (e.g., analysis before user requests)) to wireless applications; however, its deployment is very challenging due to simultaneous quality of service (QoS) and quality of physical experience (QoE) constraints. Furthermore, the computing and communication resources of end-nodes are limited. For instance, immersive experience devices (e.g., augmented reality (AR) headsets) in a metaverse have limited computing power and therefore, might not be able to perform rendering tasks. Consequently, this paper proposes a novel task offloading framework for metaverse-empowered wireless systems. Our formulated problem aims at minimizing the cost of task offloading in the metaverse while considering both QoS and QoE constraints by optimizing the task offloading, resource allocation, and transmit power allocation variables. For QoS, we consider latency and reliability, whereas for QoE, we consider both immersive experience and packet error rate. To optimize the formulated problem, we use a decomposition-based scheme that further uses modified block-successive upper-bound minimization (BSUM), convex optimization, and multi-agent reinforcement learning (MARL) for transmit power allocation, resource allocation, and task offloading, respectively. Our solution of using convex optimization-assisted MARL for joint resource allocation and task offloading significantly improves the performance of learning in terms of reward and attaining fast QoS as well as QoE. Furthermore, BSUM significantly improves transmit power allocation when used in conjunction with a convex optimizer and MARL. Other than that, we also use a dueling (i.e., it is a reinforcement learning architecture combining the dueling network structure with the double deep Q-learning network method for more stable and efficient Q-value learning) concept to further improve the performance of MARL. Our analyses show that convex optimization, BSUM, and dueling help in significantly improving the performance of MARL. Compared to traditional MARL, our proposal results in significant improvement in terms of reward and cost, as illustrated by the results.

The rapid advancement of metaverse applications in wireless environments necessitates efficient resource management to enhance Quality of Experience (QoE). This paper presents a novel framework for optimizing wireless resource allocation within the metaverse to optimize QoE using convex optimization and matching theory. We formulate a QoE optimization problem considering packet error rate (PER) and immersive experience. Our problem also enables us to trade off between immersive experience and PER while computing QoE. The formulated problem is a mixed-integer non-linear programming (MINLP) problem, which is addressed through decomposition, convex optimization, matching theory, and block successive upper-bound minimization (BSUM). Specifically, for a solution, our proposed model integrates matching theory, BSUM, and convex optimization to optimize the association, transmit power allocation, and resource allocation. Finally, numerical results are provided.

Federated learning (FL) offers many benefits, such as better privacy preservation and less communication overhead for scenarios with frequent data generation. In FL, local models are trained on end-devices and then migrated to the network edge or cloud for global aggregation. This aggregated model is shared back with end-devices to further improve their local models. This iterative process continues until convergence is achieved. Although FL has many merits, it has many challenges. The prominent one is computing resource constraints. End-devices typically have fewer computing resources and are unable to learn well the local models. Therefore, split FL (SFL) was introduced to address this problem. However, enabling SFL is also challenging due to wireless resource constraints and uncertainties. We formulate a joint end-devices computing resources optimization, task-offloading, and resource allocation problem for SFL at the network edge. Our problem formulation has a mixed-integer non-linear programming problem nature and hard to solve due to the presence of both binary and continuous variables. We propose a double deep Q-network (DDDQN) and optimization-based solution. Finally, we validate the proposed method using extensive simulation results.

Energy consumption forecasting in microgrids is critical to ensure efficiency, reliability, and sustainability. It can be beneficial for optimizing grids, reducing costs, demand-supply balancing, and enhancing sustainability. The forecast scope can range from hours or days to months and years, based on the goal of the entity doing the prediction. In the case of microgrids, the focus tends to be in the short to medium range, hours to days to achieve efficient energy distribution, cost minimization, and renewable energy utilization. In this dynamic and localized setup, the energy consumption forecast is an integral part of the process. Therefore, in this paper, we explore different ways we can generate reliable forecasts of energy consumption by a group of residential houses from the Pecan Street dataset. Leveraging on the advancement of LLMs and transformers, we propose an LLM-based model and we benchmark our findings against other models. Our results highlight the advantage and performance that can be achieved with our transformer based which demonstrates a superior predictive accuracy over other architectures.

The Low Earth Orbit (LEO) constellation has great potential for global coverage and high-capacity transmission. However, poor reliability and high energy consumption can severely affect the routing transfer performance. In this paper, a multi-path adaptive energy-efficient routing scheduling scheme for LEO constellations is designed. A static weight multi-path (SWM) routing scheme is proposed initially, extending single-path routing to multi-path routing to simulate route scheduling in large-scale constellations accurately. To further address the limitations of static weight allocation in adapting to dynamic network changes, a particle swarm optimization-based dynamic weight multi-path (PSODWM) routing scheme is proposed, considering transmission delay, energy consumption, capacity, and packet loss rate, thereby achieving an optimal multi-path load distribution without additional resource consumption. Finally, leveraging the neural population dynamics optimization algorithm (NPDOA) and digital twin technology, the multi-path adaptive energy-efficient (MPAEE) scheme is developed, providing real-time feedback for path selection and optimizing system performance. Simulation results demonstrate that the MPAEE scheme significantly reduces propagation delay, packet loss, hop count, and interruption probability while improving satellite energy efficiency and extending satellite lifetime.

The rapid expansion of vehicular networks has intensified congestion and privacy risks. Although Federated Learning (FL) addresses both challenges through decentralized model training while preserving data locality, existing FL-based client selection strategies often fall short in highly heterogeneous vehicular environments. Specifically, the inherent heterogeneity in vehicular networks—characterized by diverse data distributions and system resources—complicates current methods. The imbalanced nature of local data further exacerbates model divergence, resulting in degraded overall system performance. To solve the above problems, in the process of vehicle selection, this paper comprehensively considers three kinds of heterogeneity and proposes an FL model with adaptive proximal term. The weight coefficient of this model is dynamically adjusted based on the difference between the local and global model parameters. Based on this, a contribution score-based vehicle selection strategy (CSVS), considering the dynamics of vehicles, is proposed to alleviate the problem of traditional model weight divergence and minimize system cost. Experimental results on two classic datasets demonstrate that the proposed strategy significantly outperforms baseline methods in reducing system cost and improving model training performance, particularly in highly heterogeneous vehicular environments.

Cloud-enabled edge computing scenarios can intelligently cache and update the content on a periodic basis, thereby enhancing users' overall perception of quality, which is called quality of experience (QoE). To enhance the QoE, we aim to the multi-objective optimization, which maximizes the cache hit ratio while simultaneously minimizing traffic load and time latency. To address this issue, we focus on employing an innovative algorithm named HT-PAD, which provides a complete solution for prediction and decision-making for proactive caching. First, to improve the prediction accuracy of the cached content, we use the encoding layer in hyperdimensional computing to extract the information features. Second, HD-Transformer, as the prediction part of HT-PAD, is proposed to make predictions based on user preferences, historical information, and popular information. HD-Transformer uses DNN to predict user preferences and process time series data by combining hyperdimensional computation with Transformer. Third, to avoid error in the prediction content, we employ PER-MADDPG as the decision-making part of HT-PAD, which consists of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Prioritized Experience Replay (PER). We use MADDPG to enhance the content decision-making and utilized PER to select appropriate training samples for PER-MADDPG. Finally, our experiments have shown that our proposed approach achieves the strong performance in terms of the edge hit ratio, the latency, and the traffic load, thus improving the QoE

In this paper, a diagram that depicts a mild parallel hybrid electric vehicle is used to simulate the energy usage and efficiency. MATLAB and Simulink were used for the simulation. All aspects of this vehicle are governed by the law of conservation of energy. The system uses a drive schedule of a driver within an urban setting. Many aspects were taken into account such as the vehicle speed, battery state of charge, and tractive power needed to propel the vehicle. This simulation helps us understand how a hybrid electric vehicle operates and may give some insight on how to improve the design of such vehicles. Related work can also be used to further improve the bigger picture. Wireless charging and automated control of large scale charging can improve the transfer of energy from sources to homes and charge stations.

The smart grid is a new and improved power grid that seems to have all the solutions to our energy problems. The technology for the smart grid is still in the research and development phase but it is rapidly growing. There are many opportunities to be taken advantage of when talking about the smart grid. The possibility for the seamless integration of renewable energy resources and ushering in a new era of consumer choice is evident. However, there are still many challenges to be overcome in the pursuit of the smart grid. Energy storage is one of the biggest hurdles engineers face today along with the communication needed for the smart grid. The most ideal and efficient way to set up the smart grid is still a mystery with standards not fully in place. This paper will help uncover some of these issues and review available solutions, projects, and technologies to address them.