Underwater Optical Wireless Sensor Networks (UOWSNs) have emerged as a promising solution for high-speed underwater communication. However, these networks face a critical challenge of mutual interference among optical nodes, which occurs when the directional optical beams intersect or coverage areas overlap due to node mobility in dynamic underwater environments. Existing interference management approaches demonstrate limited effectiveness due to their reliance on simplified channel models and inability to handle rapid topology changes, resulting in significant network performance degradation. This paper presents a novel framework that systematically addresses interference management in UOWSNs through two key innovations. First, we propose a Sparse Bayesian Learning-based Interference Detection (SBL-ID) algorithm that enables real-time identification and characterization of interference patterns under complex underwater channel conditions. Second, we develop an Adaptive Interference Alignment and Delay Compensation (AIADC) algorithm that projects interference signals into a reduced-dimensional subspace, thereby enhancing the signal-to-interference ratio and facilitating accurate detection of desired signals amid interference. Our framework transforms the NP-hard interference management problem into tractable optimizations, achieving near-optimal solutions with polynomial time complexity. Extensive simulations demonstrate that our approach reduces BER by 95% and improves network throughput by 67% compared to state-of-the-art techniques. Testbed experiments conducted in both pool and lake further validate our framework's effectiveness, maintaining consistent performance improvements under diverse underwater conditions.

The growing number of mobile devices used by terrestrial users has significantly amplified the traffic load on cellular networks. Especially in urban environments, the high traffic demand brought about by dense user populations has bottlenecked network resources. The Space-Air-Ground-Integrated Network (SAGIN) provides a new solution to cope with this demand, enhancing data transmission efficiency through a multi-layered network structure. However, the heterogeneous and dynamic nature of SAGIN also poses significant management and resource allocation challenges. In this paper, we propose a cloud-edge-SAGIN framework for multi-UAV assisted traffic offloading based on Hierarchical Federated Learning (HFL), aiming to improve the traffic offloading ratio while optimizing the offloading resource allocation. HFL is used instead of traditional Federated Learning (FL) to solve problems such as irrational resource allocation due to heterogeneity in SAGIN. Specifically, the framework applies a hierarchical federated average algorithm and sets a reward function at the ground level, aiming to obtain better model parameters, improve model accuracy at aggregation, enhance UAV traffic offloading ratio, and optimize its scheduling and resource allocation. In addition, an improved Reinforcement Learning (RL) algorithm TD3-A4C is designed in this paper to assist UAVs in realizing intelligent decision-making, reducing communication latency, and further improving resource utilization efficiency. Simulation results demonstrate that the proposed framework and algorithms display superior performance across all dimensions and offer robust support for the comprehensive investigation of intelligent traffic offloading networks.

Secure and efficient service authentication is vital for trustable interactions between users and service nodes in decentralized metaverse environments. However, conventional PKI-based authentication methods face limitations such as high query latency, privacy leakage risks, and centralized trust dependencies, making them unsuitable for large-scale, real-time metaverse services. To address these challenges, we propose ESAChain, a novel blockchain-based authentication framework that e nsures lightweight, decentralized, and privacy-preserving identity verification. Specifically, we propose a Mutually Exclusive Cuckoo Filter (MECF) integrated with a Filter Hash Chain (FHC), which provides lightweight data structures and efficient querying capabilities for certificate status. Furthermore, we design a trust-decay-based Delegated Proof-of-Stake (TD-DPoS) consensus mechanism to maintain the integrity and reliability of certificate status data by dynamically adjusting node trust values and decaying votes to prevent single-node dominance. We also incorporate a blind-signature-based authentication mechanism to enhance privacy-preserving identity authentication by preventing tracking of certificate verification requests. Extensive simulation experiments and security analyses demonstrate that ESAChain significantly reduces query latency and data transmission overhead, enhances consensus robustness, and provides an efficient, trustworthy, and privacy-preserving authentication solution for secure metaverse services.

Drones are a viable solution for searching and rescuing people in distress at sea. However, traditional methods of unmanned aerial vehicle (UAV) search and rescue have slow emergency response speed, limited task environment perception and decision-making ability, high search and rescue task cost, and low efficiency. Therefore, this letter proposes a distributed drone dynamic ocean search and rescue framework that integrates deep reinforcement learning (DRL) and predicts the drift probability range based on ocean data. The existence probability of each area is calculated through a mixture of Gaussian distributions, and the possibility changes dynamically with ocean currents. The predicted probability will be input into the learning network in a matrix with the same dimension as the environment and trained using DDQN. In addition, we introduce a distance penalty in the reward function and establish a dynamic greedy strategy to accelerate learning and improve training accuracy and stability. This framework can dynamically identify the location of potential victims and guide drone groups to coordinate search and rescue operations. In particular, to adapt to the decision-making quality and synergy of DRL under distributed drone swarms, we introduce a distributed priority experience replay mechanism to share the best experiences of drones in different scenarios. By comparing various methods, this study shows excellent comprehensive performance and effectively improves search and rescue efficiency.

With the increasingly complex urban transportation system and the continuous growth of vehicle ownership, it is impossible to effectively guarantee the priority of police cars, ambulances, and other emergency vehicles, threatening the timeliness of tasks and public safety. Given the limitations of traditional signal control strategies in ensuring the priority of emergency vehicles, this paper proposes a traffic signal control strategy based on the three-layer architecture of fog computing (DNLight), which utilizes deep reinforcement learning methods to enable continuous strategy adjustments. In this architecture, the bottom fog node is responsible for collecting real-time traffic status and vehicle interaction information; the middle layer fog node agent uses the deep reinforcement learning algorithm, introduces dynamic noise for hybrid exploration, combines Dueling network diversion architecture to improve stability, and introduces the penalty coefficient C to ensure fairness between emergency vehicles and social vehicles. In terms of the reward function, this paper modifies the calculation of reward value to be dynamic, allowing it to adjust the weight of emergency vehicles and social vehicles according to traffic conditions, thereby improving flexibility. The top layer cloud service layer is mainly responsible for receiving the data backed up at the bottom layer and conducting training. The simulation experiment demonstrates that the DNLight strategy enhances the traffic efficiency of emergency vehicles by more than 40% under various traffic scenarios and traffic flow intensities, and reduces the negative impact on social vehicle traffic by up to 50%, providing strong support for urban traffic optimization and emergency management.

Video deraining in complex scene is a hot but challenging research topic. This paper proposes a novel video deraining network named STformer, which is integrating with spatial transformers and multiscale feature extraction. Specifically, the STformer architecture mainly comprises three primary components: a Local Feature Dynamic Extraction Network (LFDE) for preprocessing, a hierarchical encoder-decoder backbone with Spatial Transformer Blocks (STB) for feature extraction, and a Residual Mixture of Experts Feature Compensator (ResMEFC) for enhancing model performance and robustness. Especially, the proposed STB incorporates Channel-Wise Sparse Attention (CWSA) and Spatial Transformer Feedforward Network (STFN), and could focus on pertinent features for video deraining while minimizing noise interference. Extensive experiments on various benchmarks, including synthetic datasets like Rain200L/H and real-world datasets like SPA-Data and NTURain, demonstrate STformer’s superior performance to state-of-the-arts, particularly in terms of PSNR and SSIM.

RF sensing has attracted a tremendous amount of attention and achieved promising progress in applications such as human gesture recognition and vital sign monitoring. This paper delves into sensing the moisture level of fabrics---an important metric for smart clothing, wound care, and textile manufacturing. We present TMSense, an innovative contact-free fabric moisture measurement system that leverages UWB signals for sensing. We introduce a set of signal processing methods to tackle the challenge of weak fabric reflections that can be easily overwhelmed by noise interference. Additionally, we adopt a model-driven approach to get rid of reliance on extensive datasets. By exploiting the changes in the dielectric properties induced by moisture in textile fabrics, we establish a theoretical model that bridges the characteristics of the RF signal with the moisture content. Based on this model, we successfully eliminate interfering factors such as target-device distance and target attributes through delicate signal processing and parameter calibration. Comprehensive experiments conducted under various conditions, including different materials, sample forms, and parameter settings, demonstrate an impressively low median error of 1.4% on textile moisture measurements, outperforming commodity moisture sensors on the market.

With the exponential growth in mobile data volume, cellular networks are under severe capacity pressure. To address this issue, Unmanned Aerial Vehicles (UAVs) are being used as mobile Base Stations (BSs) for traffic offloading. However, coordinating and scheduling traffic across multiple UAVs and BSs remains a challenge in complex environments. This paper proposes a solution that optimizes UAV deployment locations and user resource allocation, the goal is to maximize traffic offloading and minimize UAV energy consumption simultaneously. We introduce a hierarchical intelligent traffic offloading network optimization framework based on Deep Federated Learning (DFL). Through federated learning, the UAV swarm is organized hierarchically. Additionally, we developed the CPRAFT algorithm, which uses capacity values as criterion to select the Leader UAV (L-UAV). The L-UAV then becomes the top-level central server for model aggregation in the federated learning environment. Furthermore, we formalize the traffic offloading problem as a Markov Decision Process (MDP). Based on MDP, this paper proposes FL-SNTD3 algorithm to optimize dynamic decision-making, which adapts to the ever-changing network environment and fluctuating traffic demands. Simulation experiments demonstrate that the proposed framework and algorithm exhibit outstanding performance in various aspects, providing robust support for future research in intelligent traffic offloading networks.

With the vigorous development of 5G communication technology, massive Internet of Things (IoT) devices generate data incrementally. Different data owners control different private domains of the IoT through edge devices and hope to achieve credible data sharing. Most of the existing solutions are based on blockchain to realize cross-domain IoT data sharing. However, incremental IoT data sharing has dual requirements for the consensus mechanism to be efficient and Byzantine fault tolerant. The independent use of the existing consensus mechanism cannot meet the above requirements simultaneously. Therefore, we propose an efficient and fault-tolerant blockchain consensus transform (BCT) mechanism for IoT. In addition, we design two consensus algorithms, namely, detectable RAFT (DRAFT) and double-layer parallel BFT (DPBFT), to improve the efficiency and fault tolerance of the data-sharing process. Extensive experiments have been conducted to show the efficiency and tolerance of our BCT mechanism.

Although combining the Internet of Things (IoT) and industrial scenarios has brought about a technological revolution, it has also caused equipment security issues. Due to the characteristics of Industrial Internet of Things (IIoT) devices with a wide distribution, complex application scenarios, considerable differences in node performance, and device heterogeneity, spoofing attacks and third-party attacks are common. Identity authentication for IIoT devices can solve this dilemma. However, most existing authentication technologies involve a tradeoff between traditional centralized certificate issuance and sacrificing device storage resources, resulting in lower efficiency of IIoT device authentication, and the process is complicated. Therefore, ensuring the security and trustworthiness of device identities in the IIoT is imminent. In this article, we propose an IIoT device authentication scheme based on an editable blockchain, that can solve the problem of the device’s low energy while satisfying the usage needs of large-scale scenarios. In particular, we created a suite of secure, efficient, and innovative technical solutions for this protocol. First, to solve the problem of the authentication difficulty between industrial devices, we propose a lightweight identity authentication protocol called BLMA. Moreover, we propose the validate-practical Byzantine fault tolerance algorithm and introduce the online and offline signature algorithm to reduce communication overhead and resource consumption between devices. Finally, considering the top security and dynamics of the IIoT environment, we use the chameleon hash function to build a hash chain of authentication results. Extensive simulation and experimental results demonstrate the reliability of our protocol.

Due to the vigorous development of wireless communication technology, massive sensors have been gradually connected to the Internet of Things (IoT) and generate a massive quantity of valuable IoT data from large-scale wireless sensor networks (WSNs) controlled by different owners. Massive IoT data need to be collected and circulated among multiple data owners and data users. However, existing data collection frameworks may cause heavy computational overhead or rely on trusted third parties, since sensors have constrained resources. Consequently, massive IoT data are transformed among different parties, causing severe trust and security issues. In this article, we propose a blockchain-assisted massive IoT data collection (MIDC) intelligent framework to support the security, trust and efficiency of massive data collection for large-scale heterogeneous WSNs. In particular, we propose a series of novel technologies for the framework: 1) we design a large-scale heterogeneous WSNs collaborative identity verification protocol to ensure reliable data sources; 2) we build a hierarchical massive data aggregation scheme to collect massive IoT data efficiently and securely; and 3) we depict a blockchain-based massive IoT data management method to construct trust among different parties. Extensive simulation and prototype experimental results prove the effectiveness of our framework.

There is an urgent need to solve the problems of secure storage, reliable sharing, access control and privacy protection in medical industry. In this paper, we propose EHRChain, a blockchain-based EHR system using attribute-based and homomorphic cryptosystem to solve the above problems. First, we design a medical record storage scheme to realize secure high capacity medical data storage and reliable sharing based on blockchain technology and IPFS. Second, we propose an improved cryptographic primitive called SHDPCPC-CP-ABE. Our SHDPCPC-CP-ABE realizes the functions of semi-policy hiding and dynamic permission changing based on partial ciphertext simultaneously. Furthermore, our program achieves the neutrality of the subject of judicial identification in medical disputes and fine-grained access control of medical data. Third, our system applies an additive homomorphic cryptosystem, Paillier cryptosystem with optimized parameters on patients’ privacy protection during the process of the medical insurance claim. After analysis and experiment, we have proved that the SHDPCPC-CP-ABE is indistinguishable under chosen plaintext attack and takes one third of the time of CP-ABE when changing access policy. Our system has higher performance than other EHR systems based on blockchain.

In this approach, we present an efficient topology and geometry optimization of triply periodic minimal surfaces (TPMS) based porous shell structures, which can be represented, analyzed, optimized and stored directly using functions. The proposed framework is directly executed on functions instead of remeshing (tetrahedral/hexahedral), and this framework substantially improves the controllability and efficiency. Specifically, a valid TPMS-based porous shell structure is first constructed by function expressions. The porous shell permits continuous and smooth changes of geometry (shell thickness) and topology (porous period). The porous structures also inherit several of the advantageous properties of TPMS, such as smoothness, full connectivity (no closed hollows), and high controllability. Then, the problem of filling an object's interior region with porous shell can be formulated into a constraint optimization problem with two control parameter functions. Finally, an efficient topology and geometry optimization scheme is presented to obtain optimized scale-varying porous shell structures. In contrast to traditional heuristic methods for TPMS, our work directly optimize both the topology and geometry of TPMS-based structures. Various experiments have shown that our proposed porous structures have obvious advantages in terms of efficiency and effectiveness.

Datastream analysis aims at extracting discriminative information for classification from continuously incoming samples. It is extremely challenging to detect novel data while incrementally updating the model efficiently and stably, especially for high-dimensional and/or large-scale data streams. This paper proposes an efficient framework for novelty detection and incremental learning for unlabeled chunk data streams. First, an accurate factorization-free kernel discriminative analysis (FKDA-X) is put forward through solving a linear system in the kernel space. FKDA-X produces a Reproducing Kernel Hilbert Space (RKHS), in which unlabeled chunk data can be detected and classified by multiple known-classes in a single decision model with a deterministic classification boundary. Moreover, based on FKDA-X, two optimal methods FKDA-CX and FKDA-C are proposed. FKDA-CX uses the micro-cluster centers of original data as the input to achieve excellent performance in novelty detection. FKDA-C and incremental FKDA-C (IFKDA-C) using the class centers of original data as their input have extremely fast speed in online learning. Theoretical analysis and experimental validation on under-sampled and large-scale real-world datasets demonstrate that the proposed algorithms make it possible to learn unlabeled chunk data streams with significantly lower computational costs and comparable accuracies than the state-of-the-art approaches.