Spatio-temporal trajectory analytics are useful in diversified applications such as urban planning, infrastructure development, and vehicular networks. Trajectory similarity measure, which aims to evaluate the distance between two trajectories, is a fundamental functionality of trajectory analytics. In this paper, we propose a comprehensive survey that investigates all the most common and representative spatio-temporal trajectory measures. First, we provide an overview of spatio-temporal trajectory measures in terms of three hierarchical perspectives: Non-learning versus Learning, Free Space versus Road Network, and Standalone versus Distributed. Next, we present an evaluation benchmark by designing five real-world transformation scenarios. Based on this benchmark, extensive experiments are conducted to study the effectiveness, robustness, efficiency, and scalability of each measure, which offers guidelines for trajectory measure selection among multiple techniques and applications such as trajectory data mining, deep learning, and distributed processing. Specifically, i) Effectiveness: In terms of trajectory length, DFD and Seg-Frechet are length-sensitive, while OWD and Hausdorff always return same results when varying query trajectory length. In terms of trajectory shape, LCRS and LORS are able to effectively find similar trajectories for query trajectories with different shapes; ii) Robustness: Learning based measures are more robust compared with non-learning based ones. Among non-learning based measures, DFD, Hausdorff, OWD and Seg-Frechet are relatively non-sensitive to noises and different sampling rates; and iii) Efficiency& Scalability: Compared to non-learning based measures, learning based and distributed based measures are more efficient and scalable.

Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention. State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA. However, existing studies have not thoroughly exploited the advantages of temporal information in TKGs. Also, they perform EA by pre-aligning entity pairs, which can be labor-intensive and thus inefficient. In this paper, we present DualMatch that effectively fuses the relational and temporal information for EA. DualMatch transfers EA on TKGs into a weighted graph matching problem. More specifically, DualMatch is equipped with an unsupervised method, which achieves EA without necessitating the seed alignment. DualMatch has two steps: (i) encoding temporal and relational information into embeddings separately using a novel label-free encoder, Dual-Encoder; and (ii) fusing both information and transforming it into alignment using a novel graph-matching-based decoder, GM-Decoder. DualMatch is able to perform EA on TKGs with or without supervision, due to its capability of effectively capturing temporal information. Extensive experiments on three real-world TKG datasets offer the insight that DualMatch significantly outperforms the state-of-the-art methods.

As a typical cyber-physical system, multi-energy system is easier to suffer from the denial of service (DoS) attacks, which brings huge risks for the effective execution of distributed energy management algorithms. To address this issue, this paper studies the collaborative energy management problem for multi-energy system under DoS attacks, where the optimal operation of each energy hub and the optimal energy trading path among energy hubs are taken into account simultaneously. To enhance the system robustness, a distributed resilient double-gradient-descent based energy management strategy is proposed, which is designed by establishing multiple control protocols. The proposed method is initialization-free and can reasonably utilize the estimation to compensate the missing information during attack periods, which possesses strong robustness and adaptability against the DoS attacks. Furthermore, based on theoretical analysis, it is proved that the proposed method can still converge to the global optimal solutions although the connectivity of the communication network is broken during attack periods. Finally, several case studies are provided to verify the effectiveness and correctness of the proposed method.

Hypergraphs can model higher-order relationships among data objects that are found in applications such as social networks and bioinformatics. However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot learn effectively from features of unlabeled data. To such learning, we propose a contrastive hypergraph neural network, CHGNN, that exploits self-supervised contrastive learning techniques to learn from labeled and unlabeled data. First, CHGNN includes an adaptive hypergraph view generator that adopts an auto-augmentation strategy and learns a perturbed probability distribution of minimal sufficient views. Second, CHGNN encompasses an improved hypergraph encoder that considers hyperedge homogeneity to fuse information effectively. Third, CHGNN is equipped with a joint loss function that combines a similarity loss for the view generator, a node classification loss, and a hyperedge homogeneity loss to inject supervision signals. It also includes basic and cross-validation contrastive losses, associated with an enhanced contrastive loss training process. Experimental results on nine real datasets offer insight into the effectiveness of CHGNN, showing that it outperforms 19 competitors in terms of classification accuracy consistently.

Although the P2P power transactions using the multiagent deep deterministic policy gradient (MADDPG) algorithm has been extensively studied, there are still challenges in privacy protection and training incentives. Furthermore, the stability and efficiency of the strategy decreases when dealing with nonindependent identically distribution (Non-IID) data from heterogeneous prosumers. Therefore, this article proposes a blockchain-enabled asynchronous federated learning-MADDPG (BEAFL-MADDPG) framework designed to enhance the training efficiency of heterogeneous prosumers while safeguarding data privacy. The framework includes a novel P2P energy trading model that facilitates energy trading amidst incomplete information while ensuring privacy assurances. In addition, a BEAFL-MADDPG algorithm is proposed, which accelerates training processes and enables parallel computation among agents. This algorithm enhances the efficiency of algorithm and empowers the training of diverse prosumers. Furthermore, a blockchain-enabled training mechanism and prosumer incentive scheme are proposed that not only encourage prosumer engagement in training but also ensure traceable transactions without the need for trust among participants. These mechanisms promote transparency and integrity, fostering a collaborative and secure environment for energy trading. Simulation results demonstrate that the framework achieves peak load reduction through optimized P2P trading, maintains computation efficiency across discount rates, and ensures secure transactions via blockchain-based incentives. These practical benefits support scalable and sustainable community microgrid operations.

With the development of the technologies deployed on vehicles, there is a significant increase in the amount of data, which comes from various applications, such as battery management, VR, autopilot, etc. However, privacy is a critical obstacle to utilizing such information since many vehicle-based applications involve locations, conversations, driving behaviors, etc. Federated Learning (FL) is a promising technology perfect for filling the gap, as it keeps users' data in their devices, which gives rise to Federated Vehicular Networks (FVN). As a distributed paradigm, communication efficiency is an essential issue for researchers, since it impacts every aspect of FVN. This paper presents a communication-efficient federated vehicular network called FedAGL integrating Adaptive parameter control, Genetic Algorithm (GA), and Long Short-Term Memory (LSTM). This is a framework designed to accelerate the training process of FVN by minimizing communication overhead and latency. In this paper, we introduce a parameter-control algorithm to alleviate transmission burdens, thereby reducing communication overhead. This approach does not involve freezing the parameters of the entire layer but identifies certain parameters with minor updates. Further, we employ the GA to allocate communication bandwidth for each vehicle, considering the varying sizes of vehicles' active parameters. Thus, the maximum transmission time in each FL round decreases. In addition, due to the complexity of the genetic algorithm, the LSTM model is developed to accelerate the bandwidth allocation with quick response and sufficient precision, which uses data from the genetic algorithm as training labels. Abundant experiments indicate that the proposed framework achieves up to 10.44% and 22.62% reduction in communication overhead and latency, respectively, outperforming the benchmarks.