Renewable energy has become a viable alternative to fossil fuels owing to its environmental benefits. However, its inherent uncertainty pose significant challenges. Demand response mechanisms have been developed to address these issues, facilitating renewable energy integration through consumer-side flexible resources. However, these mechanisms often affect consumer satisfaction, necessitating precise measurement and control of these impacts. In this paper, we propose a two-stage electricity trading and load dispatch optimization model aimed at reducing carbon emission by promoting renewable energy accommodation, and the proposed optimization model takes into account multi-category energy consumer satisfaction. We begin by classifying consumers into distinct categories and designing tailored satisfaction functions that reflect their unique power consumption preferences. The electricity trading and load dispatch processes are formulated as a two-stage optimization problem, which is then transformed into Markov decision processes. A model-free framework applying two state-of-the-art deep reinforcement learning algorithms is proposed to solve the optimization problem without requiring complex environmental modeling and prior knowledge. Numerical results demonstrate that the proposed framework outperforms benchmark algorithms regarding both consumer satisfaction preservation and carbon emission reduction.

Accurate detection of surface defects on railway tracks is critical for safe railway operation. Most existing models rely solely on Red–Green–Blue (RGB) images, limiting their ability to capture structural information. Incorporating depth features provides richer spatial cues, significantly improving detection accuracy. However, current Red–Green–Blue and Depth (RGB-D) dual-stream models suffer from high computational complexity and hardware dependencies, making them impractical for real-world deployment. To address these limitations, we propose DAHNet, an asymmetric knowledge distillation model with a teacher–student architecture. DAHNet-T serves as the teacher network, taking RGB-D inputs and integrating a cross-modal attention feature enhancement (CAFE) module to capture contextual information, along with a depth feature interaction block (DFIB) for efficient cross-modal fusion. DAHNet-S is the student network, a lightweight single-stream RGB model employing depthwise separable convolutions to reduce computation. We introduce a multi-level distillation strategy with dynamic temperature scaling to balance coarse-grained and fine-grained knowledge transfer, while incorporating contrastive learning and structural loss to improve pixel-level accuracy. Extensive experiments on the NEU RSDDS-AUG dataset demonstrate that our distilled model DAHNet-KD outperforms state-of-the-art methods. Compared to DAHNet-T, the number of parameters is reduced from 87.72 MParams to 13.97 MParams, and the computational cost decreases from 19.79 GFLOPs to 5.41 GFLOPs. The proposed model achieves superior performance across various evaluation metrics and also generalizes well on other public datasets. Therefore, the model provides a lightweight and high-accuracy solution for deployment on mobile devices in real-world industrial scenarios.

The rapid development of the Internet of Vehicles (IoV) and sixth-generation (6G) technology has made traditional cloud computing architectures inadequate for vehicular networks. With ultra-low latency, high bandwidth, and massive connectivity, 6G networks provide essential support for task offloading in mobile edge computing. Task offloading transfers computational tasks from vehicles to edge base stations or cloud servers. However, traditional methods struggle to adapt to diverse user tasks and dynamic network environments. These limitations increase offloading delays. Digital twin (DT) offers real-time simulation to reflect system dynamics and address these challenges. In this paper, we propose a task offloading framework that combines DTs with deep reinforcement learning. We propose a multi-layer dynamic framework with an error-reward feedback mechanism to handle complexity, dynamic changes, and errors. This approach enables the conclusion of task offloading strategies that minimize latency. The framework includes a threshold-based warning mechanism to effectively manage edge base station loads. By monitoring load in real time, the system evaluates base station status and adjusts task allocation to maintain stability. We propose a multi-dimensional state encoder architecture, comprising environment encoders, base station state encoders, and task state encoders to effectively extract critical features. The proposed architecture enables the conclusion of task offloading strategies that minimize latency. Experimental results demonstrate that the proposed algorithm can accurately and quickly conclude the task offloading strategy to minimize energy consumption and latency.

The sixth-generation (6 G) networks aim to achieve ubiquitous intelligent connectivity while ensuring extremely low latency, reducing energy consumption, and enhancing privacy protection. Mobile edge computing (MEC) offers an effective solution to reduce latency and energy consumption by leveraging resources near end devices for task offloading. However, MEC faces significant challenges in meeting the requirements of 6 G networks, including limited computational resources, high mobility, and strict data privacy demands. Efficiently allocating edge resources while preserving privacy has become a critical issue for realizing the objectives of 6 G networks. In this paper, we propose a privacy-preserving edge computing power network (EdgeCPN) model that jointly leverages the computing resources of edge computing nodes and protects sensitive computing power information through differential privacy methods. In addition, we propose a task matching problem that aims to minimize the privacy-budget-weighted energy consumption while ensuring privacy protection and meeting task requirements. We propose a dynamic graph-based multiagent reinforcement learning (MADRL) algorithm to find the optimal strategy for task matching and computing resource allocation with privacy protection. The results show that our proposed task matching model with energy and privacy tradeoffs can minimize the energy consumption in the matching process while ensuring privacy, and the algorithm can find the optimal strategy for task matching efficiently.

To achieve carbon neutrality, reducing carbon emissions is crucial in dispatching problems in smart grid. Though renewable energy such as wind power has low carbon emissions, it suffers from random generation, which makes the thermal power necessary for a stable supply power system. To reduce carbon emissions, the thermal power plants are transformed into carbon capture power plants, which brings new challenges to economic dispatch algorithms. Besides, there are usually many constraints to keep the security operation of power systems, which incurs a large problem scale and high computational cost. Most existing methods either do not consider reducing carbon emissions, or suffer from high computational costs. In this paper, a framework for the carbon capture plants with wind power to reduce both running costs and carbon emissions is designed to support carbon neutrality. To reduce computational cost, initial-training and fine-tuning are used. A deep neural network is employed to describe the relationship between users' load and the constraints, which provides guides for finding the active constraints. Therefore, the problem scale can be significantly decreased, making the optimal dispatching strategy obtained quickly. The experimental results on real-world data show that the proposed framework can obtain the optimal strategy efficiently.

With the rapid development of information technology, the multisource heterogeneous data containing meaningful information have been significantly generated by various edge devices in Internet of Energy, which is one of essential foundations of many knowledge discovery tasks based on edge computing. For some complicated tasks, essential features are owned by different data sellers offering data by blockchains. With limited budgets, buying features are crucial steps in knowledge discovery tasks in electricity spot markets, especially for learning-based algorithms. However, there are lack of proper data pricing mechanisms tailored to dynamic learning processes. Besides, existing methods cannot efficiently employ edge computing servers to obtain optimal policies for selecting features according to dynamic pricing with limited budgets. To overcome such drawbacks, a data pricing mechanism is proposed in this article, which consists of static and dynamic pricing parts. Based on this mechanism, given limited budgets, a feature selection (FS) algorithm considering multiple new factors is proposed, which offers near-optimal solutions for FS at different scenarios. Numeric results show the effectiveness of the proposed algorithms.

Smart grid integrates the distributed energy resources such as renewable energy with massive information to facilitate the flow of energy in the industries. The renewable energy accommodation is one of the key issues to achieve the energy efficiency in smart grid, which is difficult to obtain dynamic optimal policies due to the intermittency of renewables. To capture statuses of renewable energy for decision-making, large amounts of information in heterogeneous forms are collected by massive end devices deployed in smart grid. Such information not only provides fruitful features for existing learning based algorithms but also incurs high computation complexity. Besides, such heterogeneous data may also contain missing values, which may result in wrong policies by existing algorithms. In this article, a novel cloud-edge-end orchestrated computing scheme is proposed to efficiently repair missing values and obtain optimal policies in two separate layers. In the first layer, deep learning based algorithms deployed can perceive the characteristics and repair the missing values. In the second layer, deep reinforcement learning based algorithms are employed to obtain optimal policies. Simulations on the real power grid dataset illustrate the effectiveness of proposed fault-tolerant renewable energy accommodation algorithm.

Internet of energy (IoE) is one of the most complex industrial systems, and its stable operation is very important. Situation awareness (SA) has been proposed to ensure the stable operation for IoE and making full use of the relationships between components has become the key point for designing an efficient SA model. In this article, graph digital twin (GDT) is proposed by combining digital twin technology with graph theory, to describe the logical relationships between physical entities more accurately in digital space, and then a novel SA model for IoE based on GDT is proposed. In order to make full use of the relationship between nodes, two classifiers based on graph convolution network are designed for fault location and stability prediction. The experimental results show that the proposed SA model can localize the multiple fault components with high accuracy, and can accurately predict the stability of the system.

Transient stability assessment (TSA) plays an important role to ensure the safe operation of the power system in Internet of Energy (IoE). Many time-domain simulation (TDS)-based and transient energy function (TEF)-based methods have been proposed to assess the transient stability of the power system. With the wide area measurement system (WAMS) and the phasor measure units (PMUs) applied to observe the real-time data, methods of TSA based on the machine learning and data-driven are continuously studied. These kinds of methods can only assess the transient stability of the power system when subjected to large disturbances. However, these kinds of methods cannot infer the type of event which leads to the collapse of the power system. In this article, the gated graph neural network (GGNN) is applied to assess the power system transient stability and infer the type of event leading the instability of the power system. First, conditional generative adversarial network (CGAN) is applied to generate unstable samples making the training data more balanced. With the balanced data graph-structured and used to train the GGNN-based TSA model, the GGNN-based TSA model achieves better performances. Finally, the real-time data is input into the trained TSA model and the transient stability of the power system is assessed. When the power system is considered unstable, the proposed TSA model can also infer the type of event leading the instability of the power system, classifying the unstable state to the corresponding event. Simulations performed on the New England 39-bus system verify the effectiveness of the proposed method.

With the development of the Internet of energy, more and more participants share data by different types of edge devices. However, such multi-source heterogenous data typically contain low-quality data, e.g., missing values, which may result in potential risks. Besides, resource-constrained devices incur large latency in edge computing networks. To alleviate such latency, distributed task offloading schemes are designed to share the computation burden between edge nodes and nearby servers. However, there are three main drawbacks of such schemes. First, low-quality data are not carefully evaluated by constraints under scenarios, which may result in slow convergence in distributed computation. Second, multi-source data including sensitive information are computed and shared among edge nodes without privacy protection. Third, distributed tasks on low-quality data may result in low-quality results even with an optimal offloading scheme. To address the problems above, a task offloading framework for edge computing based on consortium blockchain and distributed reinforcement learning is proposed in this paper, which can provide high-quality task offloading policies with data privacy protected. This framework consists of three key components: data quality evaluation (DQ) with multiple data quality dimensions, data repairing (DR) with a repairing algorithm based on a novel repairing consensus mechanism and distributed reinforcement learning for task arrangement (DELTA) with a distributed reinforcement learning algorithm based on a novel low-quality data distributing strategy. Numeric results are presented to illustrate the effectiveness and efficiency of the proposed task offloading framework for edge computing on low-quality data in the IoE.

Currently, the integration of distributed energy generators through virtual power plants in the Internet of Energy is a mainstream method. The complex structure of virtual power plants and the characteristics of distributed energy make it difficult to solve the economic dispatch problems of virtual power plants. In addition, the load of a virtual power plant is unstable and uncertain and thus requires a robust economic dispatch strategy. Because the selection of the set of uncertain conditions is conservative, the traditional robust economic dispatch strategies cannot effectively reduce the cost of virtual power plants. In addition, the traditional methods for solving robust strategies cannot directly solve nonlinear and nonconvex problems. In this article, we propose a scenario-based robust economic dispatch strategy for virtual power plants, aiming to reduce the operational costs of virtual power plants. First, to reduce the conservatism of the strategy, scenario-based data augmentation is adopted for data generation. Through a generative adversarial network, a large amount of scene data are generated to extend the set of uncertain conditions. The scene data cannot only reduce the conservatism but also can be used in the determination of robust strategies. Second, deep reinforcement learning is adopted for historical data training, directly solving nonlinear and nonconvex problems to obtain a robust economic dispatch strategy. As experiments show, with the accurate generation of scene data, the proposed economic dispatch strategy is robust and effectively reduces the cost of virtual power plants.

Nowadays, microgrids (MG) have attracted much attention, as a key technology of the Internet of Energy (IoE). A great deal of research have shown that the hierarchical microgrid is a more novel structure of IoE. Although the hierarchical microgrid model solves the problem of weak power scheduling capability across microgrids, it suffers from severe communications uncertainty, which can lead to communication delay and fluctuation. To obtain the accurate result of the renewable energy accommodation assessment capacity, a hierarchical microgrid model considering communication uncertainty is proposed in this article. The solution to solve the problem of the assessment renewable energy accommodation capacity for hierarchical MG is a hybrid control based on distribution deep reinforcement learning. The temporal difference (TD) generation adversarial network (TD-GAN) is proposed as a value-based method. Compared with the policy-based method, it can better solve the distributed problem in hybrid control with a generation adversarial network (GAN). Moreover, the challenge that the method cannot handle a continuous action space is solved by using a normalized advantage function (NAF). The method similar with the TD error method is employed to train the GAN network. Simulation results using real power grid data demonstrate the effectiveness and accuracy of the proposed method.

With the high penetration of large-scale distributed renewable energy generation, the power system is facing enormous challenges in terms of the inherent uncertainty of power generation of renewable energy resources. In this regard, virtual power plants (VPPs) can play a crucial role in integrating a large number of distributed generation units (DGs) more effectively to improve the stability of the power systems. Due to the uncertainty and nonlinear characteristics of DGs, reliable economic dispatch in VPPs requires timely and reliable communication between DGs, and between the generation side and the load side. The online economic dispatch optimizes the cost of VPPs. In this article, we propose a deep reinforcement learning (DRL) algorithm for the optimal online economic dispatch strategy in VPPs. By utilizing DRL, our proposed algorithm reduced the computational complexity while also incorporating large and continuous state space due to the stochastic characteristics of distributed power generation. We further design an edge computing framework to handle the stochastic and large-state space characteristics of VPPs. The DRL-based real-time economic dispatch algorithm is executed online. We utilize real meteorological and load data to analyze and validate the performance of our proposed algorithm. The experimental results show that our proposed DRL-based algorithm can successfully learn the characteristics of DGs and industrial user demands. It can learn to choose actions to minimize the cost of VPPs. Compared with the deterministic policy gradient algorithm and DDPG, our proposed method has lower time complexity.

Releasing social network data could seriously breach user privacy. User profile and friendship relations are inherently private. Unfortunately, sensitive information may be predicted out of released data through data mining techniques. Therefore, sanitizing network data prior to release is necessary. In this paper, we explore how to launch an inference attack exploiting social networks with a mixture of non-sensitive attributes and social relationships. We map this issue to a collective classification problem and propose a collective inference model. In our model, an attacker utilizes user profile and social relationships in a collective manner to predict sensitive information of related victims in a released social network dataset. To protect against such attacks, we propose a data sanitization method collectively manipulating user profile and friendship relations. Besides sanitizing friendship relations, the proposed method can take advantages of various data-manipulating methods. We show that we can easily reduce adversary's prediction accuracy on sensitive information, while resulting in less accuracy decrease on non-sensitive information towards three social network datasets. This is the first work to employ collective methods involving various data-manipulating methods and social relationships to protect against inference attacks in social networks.

Vehicular ad hoc networks (VANETs) which are deployed along roads make traffic systems safer and efficient. Existing theoretical results on capacity scaling laws provide insights and guidance for the design and deployment of VANETs. In this paper, we propose a novel fundamental framework RVWNM (Real Vehicular Wireless Network Model), which enables a more realistic capacity analysis in VANETs. We first introduce a Euclidean planar graph which can be constructed from any real map of urban area, and represents the practical geometry structure of the urban area. Then, an interference relationship graph is abstracted from the Euclidean planar graph which considers the transmission interference relations among the nodes in the network. Finally, we analyze theoretically the interference relationships in the interference relationship graph. As far as we know, we are the first to use a practical geometry structure to calculate the asymptotic capacity of VANETs. To verify the feasibility of RVWNM, we calculate the asymptotic capacity of urban area VANETs with the consideration of social- proximity based mobility of vehicles.

Abstract In sparse mobile wireless networks, normally, the mobile nodes are carried by people, and the moving activity of nodes always happens in a specific area, which corresponds to some specific community. Between the isolated communities, there is no stable communication link. Therefore, it is difficult to ensure the effective packet transmission among communities, which leads to the higher packet delivery delay and lower successful delivery ratio. Recently, an additional ferry node was introduced to forward packets between the isolated communities. However, most of the existing algorithms are working on how to control the trajectory of only one ferry work in the network. In this paper, we consider multiple ferries working in the network scenario and put our main focus on the optimal packet selection strategy, under the condition of mutual influence between the ferries and the buffer limitation. We introduce a non‐cooperative Bayesian game to achieve the optimal packet selection strategy. By maximizing the individual income of a ferry, we optimize the network performance on packet delivery delay and successful delivery ratio. Simulation results show that our proposed packet selection strategy improves the network performance on packet delivery delay and successful delivery ratio. Copyright © 2013 John Wiley & Sons, Ltd.

Capacity scaling laws of wireless networks have attracted a lot of attention. In this paper, we study the multicast capacity of bus-assistant VANETs (vehicular ad hoc networks) with two-hop relay scheme, which has not been addressed before. Assume that n ordinary vehicles and nbbuses are deployed in a grid-like road framework while the number of roads increase linearly with n. All the ordinary vehicles obey the restricted mobility model. Thus, the spatial stationary distribution decays as power law with the distance from the centre spot (home-point) of a restrict region of each vehicle. All the buses deployed in all roads as intermediate nodes. They are used to forward packets for ordinary vehicles. Each ordinary vehicle randomly chooses k - 1 vehicles from the other ordinary vehicles as receivers. The packets could be transmitted directly from source to destination or be transmitted to an intermediate vehicle or bus, then be forwarded to the destination. We found that the social-proximity urban bus-assisted VANET has three routing methods. For each routing method, we derive the matching asymptotic upper and lower bounds of multicast capacity of bus-assisted VANET.

In intermittently connected mobile Ad Hoc networks, how to guarantee the packet delivery ratio and reduce the transmission delay has become the new challenge for the researchers. Epidemic-theory based routing has shown the better performance in terms of improving packet delivery ratio and reducing the delay, when infinite node buffer and network bandwidth model is assumed. Typically, epidemic routing adopts the 2-hop or multi-hop forwarding mode to deliver a packet. However, these two modes have the intrinsic disadvantage on too much redundant copies or too long delivery delay. In this paper, we introduce a novel H+1 hop forwarding mode that is based on the epidemic theory. Firstly, we utilize the Susceptible-Infective-Recovered (SIR) model of epidemic theory to estimate the amount of relay nodes (epidemic equilibrium) and the delivery delay within the epidemic process. Secondly, we formulate the quantities of relay nodes into a single absorbing Markov Chain model, facilitating the estimation of the expected delay for the packet transmission. Simulation results show that our H+1 hop forwarding mode has the better performance on delay and packet delivery ratio.

In sparse mobile social networks, the moving activity of nodes always happen in a specific area, which corresponds to some specific community. How to guarantee the higher packet delivery ratio while reducing forwarding delay in such networks, is a challenging issue and has not been widely investigated yet. Recently, additional super-node was introduced to ferry packets between the isolated areas. However, most existing solutions assume the super-node is always moving according to the fixed trajectory. In this paper, we put some special mobile nodes in the networks, they are called postmen, and their responsibility is to carry packets for normal nodes which belong to specific communities. Our work focus on the optimization of the moving trajectory by considering the minimum transmission delay. We formulate the optimal issue into semi-Markov Decision Process model. The decision process includes two parts: Packets-choosing strategy and trajectory of packet-ferrying-determination strategy. By maximizing the individual reward of a postman, its optimal trajectory will be found. Furthermore, the proposed solution guarantees the packet delivery ratio and delay for isolated communities. Simulation results show that the proposed packet-ferrying solution outperforms two existing ferrying solutions in terms of packet delivery ratio.

In traditional delay tolerant networks (DTNs), there exists a potential assumption that the nodes are willing to help others for packet forwarding. However, in the real application scenarios, such as civilian DTNs, selfish behaviors always widely exist. Therefore, the assumption that nodes are cooperative is not realistic in all applications. Currently, most of the existing incentive mechanism focuses on individual selfish behaviors. Few research work is proposed on social selfish behavior in DTNs. In this paper, we stimulate the nodes to cooperate with others by using a virtual bank mechanism. This incentive mechanism can effectively avoid individual selfish behaviors. Meanwhile, we observe that under this individual selfish incentive mechanism, the social distribution is unfair. That means the poverty nodes would appear in the networks, and become the internal threats for the social DTNs. To avoid this, we introduce the Gini coefficient to measure the inequality of the social distribution. Furthermore, by using the taxation strategy, we avoid the internal threats caused by social selfishness. To demonstrate the selfish behavior, we introduce the forwarding protocol which is based on social relations of nodes. We verify the proposed methods using simulation evaluations.

In this paper, we introduce a novel routing algorithm which is based on ant colony system. The objective of this novel algorithm is to solve the problem of energy and congestion control on wireless sensor network routing process. This novel algorithm is able to achieve better load balance and prolong the network lifetime. In this novel algorithm we combine the pheromone released by multi-ant colonies and residual energy as the algorithm control factor. Furthermore, we also introduce the competition mechanism among multiant colonies to avoid the simplex convergence in our algorithm. In this way, the novel algorithm controls the network traffic congestion effectively and balances the energy consumption for sensor networks. Simulation results in this paper demonstrate that this novel algorithm has better performance on load balance comparing with fundamental ant colony algorithm.