Decentralized federated learning (DFL) and efficient content delivery are critical in uncrewed aerial vehicle (UAV)-assisted internet of vehicles (IoV). However, the performance is hampered by dynamic topology, energy constraints, and fluctuating computational loads. In this paper, we present a unified optimization framework that reuses the DFL aggregation structure for cooperative edge caching in UAV-assisted IoV. In the first stage, we formalize the hierarchical DFL aggregation route as an NP-hard combinatorial problem and solve it with a graph neural network (GNN)-enhanced proximal policy optimization (PPO) algorithm for aggregation route (GPPO-R), which rapidly converges to near-optimal routing structures and achieves the trade-off balance between synchronization latency and energy consumption. In the second stage, we design a DFL-based multi-agent PPO caching strategy (DFL-MAPPO) that leverages the DFL aggregation structure and neighbor-state sharing. It consists of two phases: a long short-term memory (LSTM)-driven proactive caching phase, where the LSTM model is trained via DFL. And a real-time multi-agent reinforcement learning (MARL) phase for dynamic cache adjustment. Extensive simulations across varying number of UAVs, cache capacities, and user loads demonstrate that GPPO-R and DFL-MAPPO jointly reduce request latency and energy overhead compared to independent variants and classical schemes. The results validate the effectiveness and scalability of the proposed framework in dynamic UAV networks, providing a robust solution for integrated model aggregation and content caching in IoV.

Spatial transcriptomics (ST) comprehensively measure the gene expression profiles while preserving the spatial information. Accumulated computational frameworks have been proposed to identify spatial domains, one of the fundamental tasks of ST data analysis, to understand the tissue architecture. However, current methods often overlook pathway-level functional context and struggle with data sparsity. Therefore, we develop Path-MGCN, a multi-view graph convolutional network (GCN) with attention mechanism, which integrates pathway information. We first calculate spot-level pathway activity scores via gene set variation analysis from gene expression and construct distinct adjacency graphs representing spatial and functional proximity. A multi-view GCN learns spatial, pathway, and shared embeddings adaptively fused by attention and followed by a Zero-inflated negative binomial decoder to retain the original transcriptome information. Comprehensive evaluations across diverse datasets (human dorsolateral prefrontal cortex, breast cancer and mouse brain) at various resolution demonstrate Path-MGCN's superior accuracy and robustness, significantly outperforming state-of-the-art methods and maintaining high performance across different pathway databases (Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, Reactome). Crucially, Path-MGCN enhances biological interpretability, enabling the identification of Tertiary lymphoid structure-like regions and spatially resolved metabolic heterogeneity (hypoxia, glycolysis, AMP-activated protein kinase signaling) linked to tumor progression stages in human breast cancer. By effectively integrating functional context, Path-MGCN advances ST analysis, providing an accurate and interpretable framework to dissect tissue heterogeneity and enables detailed spatial mapping of molecular pathways that highlights potential targeted therapeutic strategies crucial for developing safe and effective synergistic anti-tumor therapies.

Taking into account the flexible deployment and Line-of-Sight (LoS) communication links of uncrewed aerial vehicles (UAVs), this article proposes a multi-UAV-assisted vehicular edge computing networks (VECNs) architecture to provide instantaneous computation support at multiple congestion road segments. Given that the computation resources of a single UAV are insufficient, and offloading tasks directly to the cloud computing center (CCC) in intelligent transportation systems (ITSs) introduces significant latency, multiple UAVs with precached service or content caching data are deployed optimally for the vehicle users. In order to address the tradeoff between system costs and service efficiency, we propose a novel cost-efficient layered optimization scheme, in which the number and deployment positions of UAVs are jointly optimized. According to the varying vehicular network environments and the dynamic requirements of vehicle users, we design a hierarchical reinforcement learning algorithm, combining double deep Q network (DDQN) and multiagent deep deterministic policy gradient (MADDPG), the former is used to optimize the number of UAVs, and the deployment of UAVs are optimized via the MADDPG. Simulation results demonstrate the effectiveness of the proposed scheme in lowering total task completed latency and increasing the system profits. The service efficiency in dealing with the vehicle users’ requirements also be improved.

Connected and automated vehicles (CAVs) have emerged as an efficient solution to improve the driving experience in the intelligent transportation systems (ITSs), in which the targeted vehicle (TV) can switch between the human-driven (HD) and autonomous-driven (AD) modes to act as server or terminal in vehicular edge computing networks (VECNs). However, due to the dynamic nature of traffic networks and the moving of vehicles, distribution of computational resources is imbalanced and variable, it is a challenge to design the cooperative resource management scheme for the whole journey of vehicle users. In this article, we propose a joint driving model selection and resource management scheme for TV in each road segment, to maximize the vehicle users’ satisfaction of the whole journey. For the complex formulated joint optimization problem, we design a three-stage hierarchical optimization (3SHO) framework, using deep Q-network (DQN) for driving mode optimization in the first stage and deep deterministic policy gradient (DDPG) for optimizing resource management under different selected driving modes. And a terminal-server matching mechanism is introduced to enable dynamic service quality improvement for TV. Specially, we design a new user satisfaction function with the quality of service, traffic revenue, and the gap between expected and actual revenues of users are considered. Experimental results showcase the robust convergence of the 3SHO algorithm, the adeptness to dynamic traffic networks, and the capacity to enhance user satisfaction significantly.

In non-terrestrial networks (NTN), optimal planning of the optimization problem of 3-dimensional (3D) trajectory is a key research topic. In this article, the optimization problem of the unmanned aerial vehicle (UAV) aided wireless sensor networks is addressed. To maximize the energy efficiency (EE) performance, we formulate the 3D trajectory optimization problem as a non-convex optimization and divide it into two sub-problems, the UAV's horizontal trajectory optimization problem with given altitude and the UAV's altitude optimization problem with given horizontal location. By combining with the discrete linear state-space approximation method, the energy-efficient algorithm with given transmit power of each sensor is proposed. Numerical results show that the proposed methods achieve significant improvements compared to the existing; EE schemes.

This article proposes distributed demand response (DR) approaches for a multienergy residential community, which is equipped with various energy conversion and storage devices to serve multiple residential loads (e.g., electricity, natural gas, and heating loads). In the proposed DR approaches, each of the energy devices and loads is an individual decision-maker and also a node in a randomly connected communication network. The DR approaches are tolerant to incomplete information which is caused by random inaction of nodes and links in the network. At first, in order to coordinate nodes' behaviors in distributed DR, different information transmission mechanisms among nodes are employed. Particularly, Steiner tree broadcast, in which nodes are networked according to their energy types, is proposed to lower the nodes' computational complexity and the network's communication overhead. Based on the information transmission mechanisms, the initial DR problem is transformed into network problems that are solvable in a random network. Then, based on the randomized alternating direction method of multipliers, distributed algorithms are designed to optimally solve the network problems in the presence of incomplete information. In simulation, real-world datasets of multiple energy loads and prices are used, and three proposed DR approaches are compared in terms of convergence performance and communication overhead.

By providing computation and storage resources at the edge of the wireless access networks, edge computing(EC) has been regarded as a provisioning solution to enable the efficient, reliable and cost-effective two-way energy and information flows in smart grids. In this paper, we propose the framework of EC-based smart grid networks, in which EC servers are deployed at the gateways between the remote cloud center and the terminal smart meters. The EC servers perform energy scheduling and renewable energy generation(RG) output forecasting, based on the collected power demand data of terminal devices and monitoring data of RG equipments. According to the inherent collaboration features of the monitoring data offloading, that the outputs of the same size/type RG equipments in a limited area are the same in a short time, we consider an efficient cooperative task offloading and resource allocation scheme. Not all of the monitoring data should be offloaded. Then, an optimal problem is formulated, the transmission powers and channels, computation resource allocation and task offloading fraction of devices are jointly optimized. Numerical results show that our proposed schemes reduce the system cost efficiently, while the latency constraints are ensured.

This paper studies a new model of energy storage (ES) sharing in a residential community in which some homes have physical ESs (PESs) but some do not. The non-PES homes can buy ES capacity from PES homes, creating virtual ESs (VESs). Based on the transaction results between PESs and VESs, an online algorithm is developed for real-time energy management of ES sharing among the homes. During online control, non-negative long-term utilities of homes and practical charging/discharging constraints of PESs and VESs are considered. The advantage of the proposed algorithm is that system state forecasting, such as home load, renewable generation, and grid price, is not required. The algorithm only needs current system states to make a control decision. Theoretic analysis shows that the worst-case system cost under the algorithm is upper bounded, guaranteeing the online solution is near-optimal. In the simulation, real-time data of grid price and home power use is employed, and the proposed algorithm is benchmarked against a greedy algorithm and a theoretic lower bound.

In green cities, one of the most promising energy system designs is the multi-energy system, which is capable of integrating different energy resources to supply stable energy for users. To schedule diverse energy efficiently, the energy trading among different energy entities is a big issue in multi-energy systems. This paper proposes auction mechanisms for energy trading in a smart multi-energy district, in which the district manager sells electricity, natural gas, and heating energy to users and meanwhile trades with outer energy networks. Two auction mechanisms are designed under the day-ahead and real-time markets, respectively. For each auction, energy allocation is optimized by solving a social welfare maximization problem, which is strictly subject to constraints of physical multi-energy system models. It is theoretically proven that both auctions are able to guarantee the properties of economic efficiency, truthfulness, and individual rationality. With these properties, users are incentivized to participate into the auctions with fairness. Finally, real data are adopted to evaluate the performance of the proposed mechanisms. The theoretic analysis of the properties is verified as well.

The introduction of wirelessly charged electric buses (WCEBs) into current public transportation system attracts many attentions in recent years. As the wireless charging technology enables energy transfer from power transmitters to electric vehicles (EVs) on road, it provides a promising solution to reduce the huge cost of battery with large size and long charging time, which are two critical impediments for EV applications. However, the system cost of WCEBs is huge. Under the dynamic electricity demands and the fluctuating electricity prices, the system operating electricity cost highly depends on the charging schedule. In this paper, according to the typical day-ahead electricity market, we explore an optimal charging scheduling scheme in a WCEB system to minimize the system operating electricity cost, while the characteristic of WCEBs is considered. The price of electricity fluctuates with the accumulated energy demands in both spatial and temporal domains. We first present a day-ahead reserved wholesale electricity determination algorithm, in which, the average speeds of WCEBs are presumed. Then, we propose an optimal charging scheduling algorithm, in which the WCEB charging schedules in slots are optimized sequentially. Both the reserved electricity and the predicted speeds in the slot are used. Simulation results demonstrate the efficiency of our proposed WCEB charging schedules.

Interference management through power control in wireless ad hoc networks has both the hidden terminal problem which induces collisions, and the exposed terminal problem which prohibits concurrent transmissions. Both problems are caused by the varied interference range induced by the adjusted transmission power. Through observing that the nodes adopt the power control mechanism induces collisions in one scenario and miss concurrent transmission opportunities in two scenarios, this paper presents IAPC (Interference-Aware Power Control), a novel protocol to improve the network throughput from these aspects. IAPC makes the interference range of each link covered by its CTS (Clear-To-Send) transmission through utilizing a signature detection process, so as to avoid interference. Meanwhile, it lets CTS convey the transmission power information of this link to make the neighboring node determine a limited transmission power, which tries to make the neighboring node outside the interference range of the ongoing link, thus can exploit concurrent transmissions. Simulation results based on ns-2 show that IAPC can outperform the other protocols significantly.

One of the major advantages of smart grid is to allow a large number of electric vehicles (EVs) to participate in energy dispatch as elastic energy storage devices via vehicle-to-grid (V2G) technology. As mechanism design for V2G energy trading can stimulate energy interaction between EVs and grids, it is really significant to V2G systems. This paper focuses on efficient mechanism design for energy trading in a two-layer V2G architecture, which includes a grid-aggregator layer and aggregator-EV layer. We propose two auction mechanisms for the two layers, respectively, and discuss three essential economic properties of the mechanisms, i.e., truthfulness, individual rationality, and efficiency. Then, based on these two mechanisms, we illustrate the detailed operation procedure of the two-layer V2G energy trading architecture. Performance evaluation shows that the proposed auction mechanisms greatly reduce social costs, i.e., enhance efficiency, while guaranteeing truthfulness and individual rationality.

A perfect bidirectional communication network is a common assumption in smart grids. However, it is unrealistic, especially in the neighborhood area network of microgrids. Due to the channel fading, large volumes of transmission data, and considerable communication cost, the imperfect communications affect the system performance directly. In this paper, we consider the uncertainty of imperfect communications in both supply and demand sides, which affects the microgrid system performance in terms of the packet loss ratio of the power demand data transmission and the forecasting accuracy ratio of the renewable energy generation. We analyze the impacts of imperfect communications on the demand response management (DRM) performance under the real-time pricing scheme. An optimization problem is formulated first to maximize the DRM performance of the microgrid system. As these impacts can be mitigated by using sufficient spectrum resources, we then propose a spectrum resource allocation scheme that considers different characteristics of transmission data and system communication cost to balance the tradeoff between the DRM performance and the incurred communication cost. We introduce a joint optimization problem that not only maximizes the DRM performance but also minimizes the communication cost. Simulation results reveal the impacts of imperfect communications on the DRM performance and power price, and the efficiency of the proposed optimization problems.

A reliable and energy-efficient communication infrastructure plays an important role in the success of data collection, transmission and control in smart grid (SG) networks. In order to satisfy the coverage and cost minimization requirements, in this paper, we introduce cognitive radio (CR) into a residential SG network in which the communication load and the available spectrum resource change with the power load in different time periods. To leverage this feature, we propose a spectrum access strategy selection scheme. Moreover, based on the proposed selection scheme, we propose an energy-efficient gateway on-off switching optimization scheme to minimize the energy consumption, while considering the sensing operation and transmission power control of SG nodes. Numerical results reveal that the proposed access strategy selection scheme can increase the average channel capacity of SG nodes considerably, and the proposed gateway on-off switching scheme can balance the tradeoff between the energy consumption and coverage requirement efficiently.

This paper represents the design, feasibility evaluation and performance validation of concurrency-based coordination mechanism (CCM), a novel cross-layer protocol that can coordinate among nodes effectively to avoid data packet interference in wireless local area networks (WLANs), achieving higher throughput compared to 802.11 standard and other state-of-the-art protocols. The design of CCM contains OpenCCM which is based on the architecture of software defined network to schedule the transmissions in both the uplink and downlink directions centrally to maximize transmission concurrency. It also contains an interference-resistant mechanism in the physical layer that can make the control message transmitted with the data packet simultaneously to eliminate the coordination overhead. Experiment results with USRP2 demonstrate the feasibility of the interference-resistant mechanism, and the simulations by ns-2 show that CCM can outperform other protocols significantly.

Spectrum access strategy plays a critical role in multichannel cognitive radio networks (CRNs). However, the CRNs cannot obtain the maximal throughput, when the existing access strategies, including overlay, underlay, and hybrid access strategies, are applied to multichannel CRNs. In this paper, we present a generalized access strategy in a multichannel CRN smart home environment, in which a secondary user (SU) system selects part of channels for sequential spectrum sensing, and accesses these channels based on the sensing results. Moreover, it accesses the remaining channels directly. We then formulate a two-phase optimization framework, which takes the sensing channel selection, sensing time allocation, and the power allocation into consideration, to maximize the gross average throughput of the multichannel CRN. In the sensing phase, a generalized access strategy algorithm (GAS) is first proposed, where we prove that only part of channels needs to be selected for spectrum sensing to achieve the maximum throughput. An optimal stopping rule is proposed to determine the optimal number of selected sensing channels. In addition, a completed hybrid access strategy algorithm is further investigated where the SU system senses all channels. An approximation algorithm is also presented to achieve suboptimal results with low computational complexity. In the transmission phase, the transmission powers of all channels are optimized via convex algorithms. Numerical experiments show that, compared with the existing schemes, the proposed schemes are able to achieve considerable throughput improvement.

When studying the real-time pricing in smart grids, there is a common assumption that a perfect bidirectional communication network is built between supply and demand ends. However, this assumption is unrealistic, especially in a microgrid system which includes renewable energy resources. In this paper, we consider the uncertainty of imperfect communications in both supply and demand ends, which affects the performance of the microgrid system in terms of the packet loss ratio of the power demand information transmission and the forecasting accuracy ratio of the renewable energy generation. We analyze the impacts of unreliable communications on the demand response management (DRM) performance of real-time pricing in the microgrid system. A joint optimization problem is therefore formulated in order to maximize the DRM performance of the microgrid system. Leveraging the dual decomposition, the proposed optimization problem is solved via two sub-problems. Simulation results reveal the impacts of imperfect communications on the DRM performance.

In cognitive radio networks, spectrum sensing and access scheme affects the system performance. In this paper, a new wideband mixed access scheme is proposed, in which the Secondary Users (SUs) sense the channels via wideband spectrum sensing, and access them with a mixed access strategy. In order to maximize the ergodic throughput of SUs, we find optimal sensing time and transmission power of each channel, while protecting the Primary Users (PUs) from interference. It is shown that the optimization problem can be formulated as a convex problem. Moreover, we present a QoS-aware low complexity scheme, in which the SUs select several specific channels to sense. An effective sensing channels selection criterion is proposed. Numerical results show that the proposed schemes can effectively improve the system performance.