Service function chain (SFC) based on network function virtualization (NFV) technology can handle network traffic flexibly and efficiently. The virtual network function (VNF), as the core function unit of SFC, can experience software aging, which reduces the availability and reliability of SFC and even leads to service interruption, after it runs continuously for a long time. Software rejuvenation technique can effectively combat software aging. However, its effectiveness in improving the availability and reliability of SFC needs to be evaluated. Compared with existing models, this paper proposes a semi-Markov model to capture the behaviors of each VNF in a SFC from the occurrence of software aging to recovery by software rejuvenation technique under the condition that the failure times and recovery times follow general distribution, while considering trigger intervals of software rejuvenation technique. We then derive the calculation formulas of the steady-state availability, transient availability, and reliability, which are applied to evaluate the effectiveness of software rejuvenation technique. Finally, we conduct sensitivity analysis and numerical experiments to analyze the effects of system parameters, the number of VNFs and trigger interval of software rejuvenation technique on availability and reliability of SFC, and the effects of time-varying parameters on transient availability of SFC.

The rapid growth of the low-altitude economy has driven the widespread adoption of unmanned aerial vehicles (UAVs). This growing deployment presents new challenges for UAV trajectory planning in complex urban environments. However, existing studies often overlook key factors, such as urban airspace constraints and economic efficiency, which are essential in low-altitude economy contexts. Deep reinforcement learning (DRL) is regarded as a promising solution to these issues, while its practical adoption remains limited by low learning efficiency. To overcome this limitation, we propose a novel UAV trajectory planning algorithm that integrates DRL with the large language model (LLM) reasoning to enable safe, compliant, and economically viable trajectory planning. Specifically, we model the trajectory planning task as a partially observable Markov decision process, explicitly incorporating obstacle avoidance, regulation awareness, and energy constraints. We design a hybrid optimization algorithm based on the soft actor-critic algorithm and LLM reasoning to enable adaptive decision-making in uncertain and dynamic environments. Experimental results demonstrate that our algorithm achieves the best overall performance, with the highest data collection rate (99.50%), almost zero collision avoidance rate and regulation violation rate, a successful landing rate of nearly 100%, and the lowest energy consumption rate (76.95%). These results validate the effectiveness of our algorithm in addressing UAV trajectory planning key challenges under constraints of the low-altitude economy networking.

Establishing and sustaining Metaverse service necessitates an unprecedented scale of resources. Some researchers consider the deployment of Metaverse service in a cloud-edge resource architecture, which can satisfy the escalating demand for Metaverse service resources while ensuring both high bandwidth and low latency. However, how to ensure the security and trustworthiness of resource nodes (RNs) in this architecture and thus ensure the reliability and security of Metaverse service is still a challenge. In this paper, we first propose a novel mechanism, named Reliable and Secure Metaverse Service (RSMS), to ensure Metaverse service reliability and security without sacrificing performance. RSMS consists of two protocols: (1) One is a blockchain-based lightweight mutual authentication protocol, which can assure the trustworthiness of heterogeneous Metaverse service RNs dynamically joining a Metaverse service resource pool and then guarantee the security of Metaverse service. (2) The other is a group authentication protocol used to form and maintain a stable and secure Metaverse service group composed of RNs, which ensures the reliability and enhances the security of Metaverse service. The reliability and security of Metaverse service under RSMS are thoroughly discussed, and also informal and formal security analysis are conducted. Additionally, we study the impact of RSMS on Metaverse service throughput, demonstrating its lightweight feature.

Advanced wireless communication and artificial intelligence technologies are significantly facilitating the deployment of driverless taxis (DTs). To ensure high-quality of DT services, data sharing among DTs is essential during service provision. However, at least three key challenges are then raised in terms of security and privacy: 1) guaranteeing data authenticity with traceability; 2) enabling flexible data authorization and preserving privacy amid complex data ownership; and 3) protecting DT privacy under frequent data sharing. Most existing works address only one aspect, such as data authenticity, privacy preservation, or single-entity authorization. To address these challenges, we propose a novel Multi-authorized, Privacy-protected, and Traceable Data Sharing (MPTDS) scheme for DTs. Firstly, MPTDS upholds data privacy preservation by designing the data sharing credential for each DT and the data access credential for each service provider. Data sharing credential also ensures anonymity and unlinkability of DTs. Secondly, MPTDS ensures data integrity and origin authentication by designing the credential-based authentication technique for DT, which also enables the traceability. Additionally, MPTDS facilitates the data multi-authorization by designing the data sharing credential multi-party issuance technique without sacrificing the non-frameabiltiy. We conduct security analyses and performance evaluations to validate the security properties and practicality of MPTDS.

Connected autonomous vehicles (CAVs) represent a transformative advancement in transportation, offering enhanced safety, improved traffic efficiency, and reduced environmental impact through intelligent driving. As CAVs operate without human intervention, they heavily rely on secure vehicle-to-vehicle (V2V) communication for cooperative perception and coordinated decision-making. These real-time inter-vehicle exchanges underpin safe coordination, dynamic decision-making, and collision avoidance. To ensure trust in such communication, robust and efficient authentication mechanisms are essential. However, existing schemes often fall short in terms of security resilience and operational practicality. In this article, we propose a novel certificateless signature scheme with conditional privacy-preserving authentication (CLSS-CPPA) tailored to CAV environments. The proposed scheme addresses three fundamental limitations in existing schemes: signature forgery vulnerabilities, single-authority dependency, and lack of dynamic revocation capability. Our approach employs distributed key generation to prevent signature forgery attacks, uses prefix tree structures for efficient dynamic key revocation, and implements dual-agency pseudonym management with mutual authority constraints to prevent single-entity power abuse. Lightweight cryptographic operations are also adopted to suit resource-constrained vehicular systems. Formal security analysis and extensive evaluations demonstrate that CLSS-CPPA enhances privacy preserving and reduces signing and verifying costs by 20%–90% compared to state-of-the-art schemes, making it a promising solution for real-world CAV deployments.

Emergency supplies allocation is a critical task in post-disaster response, as ineffective or delayed decisions can directly lead to increased human suffering and loss of life. In practice, emergency managers must make rapid allocation decisions over multiple periods under incomplete information and highly unpredictable demand, making robust and adaptive decision support essential. However, existing allocation approaches face several challenges: (1) Those traditional approaches rely heavily on predefined uncertainty sets or probabilistic models, and are inherently static, making them unsuitable for multi-period, dynamically allocation problems; and (2) while reinforcement learning (RL) technique is inherently suitable for dynamic decision-making, most existing RL-base approaches assume fixed demand, making them unable to cope with the non-stationary demand patterns seen in real disasters. To address these challenges, we first establish a multi-period and multi-objective emergency supplies allocation problem with demand uncertainty and then formulate it as a two-player zero-sum Markov game (TZMG). Demand uncertainty is modeled through an adversary rather than predefined uncertainty sets. We then propose RESA, a novel RL framework that uses adversarial training to learn robust allocation policies. In addition, RESA introduces a combinatorial action representation and reward clipping methods to handle high-dimensional allocations and nonlinear objectives. Building on RESA, we develop RESA_PPO by employing proximal policy optimization as its policy optimizer. Experiment results with realistic post-disaster data show that RESA_PPO achieves near-optimal performance, with an average gap of only 3.7% in terms of the objective value of the formulated problem, from the theoretical optimum derived by exact solvers. Moreover, RESA_PPO outperforms all baseline methods, including heuristic and standard RL methods, by at least 5.25% on average.