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.

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.

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.

Routing based on a connected dominating set (CDS) is a promising approach in wireless ad hoc networks. Wu and Li proposed a distributed approximation algorithm for calculating CDS in a given connected graph. However, this algorithm is difficult when there are obstacles in the network topology. Obstacle hybrid routing algorithm (OHRA), presented here, addresses these issues. OHRA consists of mobility model, CDS election and hybrid routing. In obstacle mobility model, we introduce STANDBY nodes as relaying nodes between two nodes that are invisible. This paper then proposes a distributed CDS election algorithm in the presence of obstacles. This algorithm extends Wu and Li's algorithm and utilizes STANDBY nodes to connect the existing dominating-nodes belonging to dominating set. In addition, OHRA based on a CDS uses hybrid routing scheme (flooding-based approach and position-based approach) to forward around any obstacles. Eventually, an example is given to show that the proposed approach can form a CDS and successfully construct routes.