The convergence of unmanned aerial vehicles (UAVs) and the Internet of Things (IoT) is expected to enhance sensing coverage, connectivity, and resilience in distributed smart grids, especially in remote or infrastructure-sparse regions. In this UAV-assisted, IoT-enabled paradigm, UAVs act as aerial relays that collect, aggregate, and forward sensing data between ground devices and control centers. However, privacy-preserving data aggregation (PPDA) in such settings still faces key-escrow vulnerabilities, certificate management overhead, incomplete privacy protection, and high computational and energy costs, particularly for signature verification at UAV relays and decryption at control centers. To address these challenges, we propose LCE-PPDA, a lightweight, certificateless, and escrow-free PPDA scheme tailored for UAV-assisted, IoT-enabled smart grids. LCE-PPDA eliminates key escrow through joint key generation, adopts a hierarchical timing structure with macro-interval rekeying and micro-interval reporting, and supports ciphertext-level in-network aggregation with both individual and batch authentication at UAV relays. To ensure privacy with accountability, it integrates certificateless signatures, dynamic pseudonyms, and session-bound key masking, achieving end-to-end confidentiality, conditional anonymity, unlinkability, and accountable traceability. Formal analysis shows that LCE-PPDA achieves correctness and EUF-CMA security in the random-oracle model under the ECDLP assumption against both Type-I and Type-II adversaries. Performance evaluation further demonstrates that LCE-PPDA reduces computational, communication, and energy overheads compared with representative schemes, providing a scalable and lightweight foundation for secure, privacy-preserving data aggregation in UAV-assisted, IoT-enabled smart grids.

Timely and secure emergency message delivery is critical to resilient smart-grid operation and rapid disturbance response. However, existing schemes remain inadequate, leaving smart grids vulnerable to security and privacy threats and causing verification bottlenecks, particularly when nonlinear emergency measurements cannot be homomorphically aggregated, which prevents bandwidth-efficient in-network aggregation and scalable batch verification. We propose EF-CPPA, an escrow-free, conditional privacy-preserving authentication scheme for real-time emergency messaging in smart grids. EF-CPPA enables smart meters to deliver authenticated emergency messages to the CC via power gateways verifiable as legitimate relays, while ensuring the confidentiality, integrity, and unlinkability of embedded nonlinear measurements. EF-CPPA further provides conditional anonymity with accountable tracing, as well as origin authentication, intra-domain verification, and scalable batch verification under bursty multi-meter messaging. An ECDLP-based escrow-free key-generation mechanism reduces reliance on the CC and enables efficient node joining and revocation. Security analysis shows that EF-CPPA achieves existential unforgeability under chosen-message attacks (EUF-CMA) and satisfies the stated security and privacy requirements. Performance evaluation demonstrates low computational, communication, energy, and node-management overhead, making EF-CPPA suitable for security-critical, time-sensitive smart-grid emergency messaging.

Satellite-Terrestrial Integrated Network (STIN) serves as essential infrastructure for providing seamless global coverage and wireless remote subscription services. However, the inherent heterogeneity, satellite exposure, and the openness of satellite-terrestrial links pose significant security challenges for authentication, such as privacy breaches, eavesdropping, replay attacks, and identity impersonation, as well as scalability issues like dynamic node joining and revocation. Existing authentication protocols suffer from deficiencies in unlinkability, scalability, and resistance to multiple attacks, and often rely on overly optimistic assumptions regarding satellite trustworthiness. Moreover, performance bottlenecks in handling numerous user access authentication requests within short timeframes remain unresolved. To address these challenges, we propose the N3PA-STIN protocol, a novel three-party authentication protocol for multi-user access that ensures mutual trust among users, satellites, and ground stations. The protocol minimizes computational overhead through an efficient batch verification mechanism, and enhances privacy and unlinkability by employing temporary identifiers derived from one-time pseudonyms. Furthermore, the protocol ensures conditional anonymity, enabling accountability while preserving user privacy, and achieves conditional verifiability by restricting the verification of authentication messages exclusively to registered nodes. A domain key update mechanism based on the Chinese Remainder Theorem (CRT) supports dynamic node management, effectively addressing the scalability challenges in heterogeneous networks. Security and performance analyses demonstrate that the N3PA-STIN protocol meets the security requirements and minimizes both computational and communication overhead, making it a practical and effective solution for STIN.

Contrastive learning (CL) has emerged as a powerful approach for self-supervised learning. However, it suffers from sampling bias, which hinders its performance. While the mainstream solutions, hard negative mining (HNM) and supervised CL (SCL), have been proposed to mitigate this critical issue, they do not effectively address graph CL (GCL). To address it, we propose graph positive sampling (GPS) and three contrastive objectives. The former is a novel learning paradigm designed to leverage the inherent properties of graphs for improved GCL models, which utilizes four complementary similarity measurements, including node centrality, topological distance, neighborhood overlapping, and semantic distance, to select positive counterparts for each node. Notably, GPS operates without relying on true labels and enables preprocessing applications. The latter aims to fuse positive samples and enhance representative selection in the semantic space. We release three node-level models with GPS and conduct extensive experiments on public datasets. The results demonstrate the superiority of GPS over state-of-the-art (SOTA) baselines and debiasing methods. In addition, the GPS has also been proven to be versatile, adaptive, and flexible.

Channel state information (CSI) feedback enhancement based on artificial intelligence (AI)/machine learning (ML) has been widely studied in the recent years, and has been selected as one of three physical layer AI/ML use cases in 3GPP Release 18 [1]. Despite their success of achieving a better trade-off between feedback accuracy and feedback payload size, most of the current solutions lack the flexibility to adjust to feedback payload size. For different feedback payload sizes, separate ML models are trained and utilized, resulting in a linear increase of the number of ML models with respect to the number of feedback payload sizes. It can be problematic for real-world deployment, since one ML model usually has hundreds of thousands or even millions of parameters. In this paper, we build an ML model where the majority of model parameters are shared, while the normalization layer is switched according to feedback payload size. The feasibility and effectiveness of switchable normalization layer is verified through numerical simulations, where an autoencoder (AE) is leveraged for CSI feedback and both convolutional neural network (CNN) and transformer based AE are considered. Results show that switchable normalization layer can adapt to various quantization bit-widths as well as various encoder output widths, achieving similar or sometimes even better performance than having separate AEs. Furthermore, by re-using most of the model parameters, the increase in model size is negligible.

Energy storage is an important part of the new power system, responsible for ensuring stable power output and balancing loads. At the same time, it is also a national critical infrastructure. As the country gradually strengthens its control over data security risks of critical infrastructure, data security issues in the energy storage industry have become a focus of attention. In this context, this paper collects and organizes and analyzes relevant literature on data security in the energy storage industry. First, the energy storage system and energy storage technology are summarized and analyzed, and the data and data characteristics of the energy storage system are explained. Secondly, the current data security risks of the energy storage system are sorted out, including BMS network security risks, user privacy leakage risks, information interaction risks of distributed energy storage systems, smart meter data leakage risks, and trust risks between devices. Based on the above data security risks, the existing energy storage data security governance methods are sorted and classified. Finally, it is summarized that the current research in the field of data security in the energy storage industry is not sufficient. This paper comprehensively analyzes the literature on data security in the energy storage industry in recent years, which will provide support for the research on data security governance in the energy storage industry.

The use of smart contracts enhances the capabilities of blockchain-based botnets, allowing for greater information capacity, richer application scenarios, and the deployment of program functions directly on the blockchain. However, smart blockchains offer a better solution for the intelligence of IoT systems, but they also come with some security risks. Botnet is a highly insecure community because it is used to do hazardous things like Distributed Denial of Service (DDoS). It is extremely essential to detect botnets with some useful tools, such as artificial intelligence (AI) algorithms, because these algorithms can assist us to monitor the network automatically. We need to pay the utmost attention to some feature engineering work, as recognition rates of AI models are considerably improved with suitable features. In this article, we propose domain embedding (DE) models to generate low-dimensional features for domains with unsupervised learning algorithms. We also explore some key parameters of the DE model to obtain decent effects on domain features. A modified version of the$k$-means algorithm called extended$k$-means, is used to cluster these domains in certain hubs and botnets that can be found for smart blockchain-based IoT systems. In the experiments, some domain correlation scores can be computed during the DE model, and similar domains have higher correlation scores.

From a process perspective, a flood includes several phases with distinguishable features. Fine-grained multisource data for different flood phases can be used to inform decision-making as flooding progresses. Therefore, the aim of this study was to develop an integrated framework based on human perceptions to progressively profile floods, including flood process segmentation rules (FPSR), flood severity index (FSI) and flood process perception ontology (FPPO). FPSR identifies flood phases based on specific signals in multisource data and provides spatiotemporal process information to FPPO consistent with flood perception. FSI follows FPSR to evaluate flooding throughout its evolution process. The comparison between FPSR and the flood monitoring index (IF) demonstrates that FPSR can detect flood events and segment the flooding process into latency, onset, development and recovery phases. The correlations between the standardized antecedent precipitation index (SAPI) and FSI show that FSI can assess flood severity with both natural and social effects in every flooding phase (R2 = 0.726 and 0.673 for the 2016 and 2020 floods, respectively). An experiment finds that flood events in Wuhan, China, usually begin in mid-to-late June and are the most severe in July, when more caution is needed for flood prevention and mitigation.

Broadcast encryption scheme enables a sender distribute the confidential content to a certain set of intended recipients. It has been applied in cloud computing, TV broadcasts, and many other scenarios. Inner product broadcast encryption takes merits of both broadcast encryption and inner product encryption. However, it is crucial to reduce the computation cost and to take the recipient’s privacy into consideration in the inner product broadcast encryption scheme. In order to address these problems, we focus on constructing a secure and practical inner product broadcast encryption scheme in this paper. First, we build an anonymous certificate-based inner product broadcast encryption scheme. Especially, we give the concrete construction and security analysis. Second, compared with the existing inner product broadcast encryption schemes, the proposed scheme has an advantage of anonymity. Security proofs show that the proposed scheme achieves confidentiality and anonymity against adaptive chosen-ciphertext attacks. Finally, we implement the proposed anonymous inner product broadcast encryption scheme and evaluate its performance. Test results show that the proposed scheme supports faster decryption operations and has higher efficiency.

We are primarily concerned with the problem of aggregating multiple attributes with uncertainty to form an overall decision function. We introduce a new type of approach for aggregation called an induced ordered weighted evidential reasoning (IOWER) approach, which is inspired by an induced ordered weighted averaging operator and the evidential reasoning (ER) approach. In the IOWER approach, we use a belief decision matrix combined with an induced ordered weighting vector for problem modeling and the Dempster–Shafer theory of evidence for attribute aggregation. It is proved that the original ER algorithm is a special case of the IOWER algorithm. Then we examine the properties of the IOWER approach. One key point in the IOWER approach is to reorder the arguments in the form of distributed assessment structure. A kind “expected utility” order-inducing variable is proposed in the IOWER approach, which can make the alternative's advantages prominent. Finally, we present an illustrative example in which the result obtained with the new aggregation approach can be seen.