Cross-chain relay architectures face critical security gaps: centralized trust dependencies, consensus vulnerabilities, and potential quantum threats. We propose the Quantum Cross-Chain (QCC) model—a post-quantum secure framework integrating quantum cryptography at the foundational layer. QCC establishes a global identity registry for heterogeneous chains and introduces a Two-Way Identity Authentication (TIA) protocol using GHZ entanglement, enabling information-theoretically secure mutual verification in a single execution. To fortify transaction integrity, we design a Quantum Ring Signature (QRS) scheme with novel key-loss security, ensuring that compromised keys cannot forge valid signatures. Unlike conventional systems that rely on smart contract autonomy, QCC delegates security to quantum one-way functions and distributed auditing, synchronizing consensus transmission with cryptographic validation. Formal verification proves composite security bounded by negl(n)+negl(q), while simulations demonstrate stable throughput (78.5 TPS) and predictable latency (130ms) under variable network conditions. QCC achieves post-quantum resilience, decentralized auditability, and linear scalability, providing a practical blueprint for next-generation cross-chain infrastructure. © The Author(s) 2026.

Time series data are widely encountered across scientific and industrial domains, but multivariate time series classification (MTSC) remains challenging because of the need to jointly model multidimensional feature interactions and complex temporal dependencies. To address these difficulties, MSCFormer, a hybrid neural architecture that integrates multiscale convolutional feature extraction with dual multihead self-attention for performing MTSC tasks, is proposed in this paper. The model introduces a multiscale feature module (MSFM) to capture fine-grained and coarse-grained local patterns and to perform channel fusion, yielding richer and more discriminative feature representations. In addition, MSCFormer adopts a dual multihead attention mechanism, where F-MHA enhances the featurewise dependency modeling process, and T-MHA focuses on long-range temporal relationships. MSCFormer is evaluated on 27 UEA benchmark datasets, achieving an average accuracy of 80.0%, an average ranking of 2.06, and a +4.1% improvement over the best baseline. Comprehensive ablation experiments and visualization analyses further verify the individual and complementary contributions of the MSFM, F-MHA, and T-MHA to the performance of MSCFormer. These results demonstrate that MSCFormer provides an effective and robust framework for multivariate time series classification tasks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.

Federated learning (FL) is a promising paradigm that can enable collaborative model training between vehicles while protecting data privacy, thereby significantly improving the performance of intelligent transportation systems (ITSs). In vehicular networks, due to mobility, resource constraints, and the concurrent execution of multiple training tasks, how to allocate limited resources effectively to achieve optimal model training of multiple tasks is an extremely challenging issue. In this paper, we propose a mobility-aware multi-task decentralized federated learning (MMFL) framework for vehicular networks. By this framework, we address task scheduling, subcarrier allocation, and leader selection, as a joint optimization problem, termed TSLP. For the case with a single FL task, we derive the convergence bound of model training. For general cases, we first model TSLP as a resource allocation game, and prove the existence of a Nash equilibrium (NE). Then, based on this proof, we reformulate the game as a decentralized partially observable Markov decision process (DEC-POMDP), and develop an algorithm based on heterogeneous-agent proximal policy optimization (HAPPO) to solve DEC-POMDP. Finally, numerical results are used to demonstrate the effectiveness of the proposed algorithm.

In the Internet of Autonomous Vehicles (IoAV), task offloading is crucial for managing tasks that require extensive computing power to guarantee vehicle safety under different weather scenarios. However, extreme weather events can lead to infrastructure damage and network disruptions, significantly increasing the computational demands of autonomous vehicles. These vehicles require additional computing resources to navigate complex road conditions and risks, all while facing a high degree of uncertainty, such as fluctuations in vehicle resource utilization and task workloads. To address these challenges, a new and lightweight task offloading decision framework, named 2FDP-BRL, has been first proposed in this paper. This framework not only considers the fast response time required for autonomous driving, but also considers the resource shortage and offloading uncertainty caused by extreme weather. Therefore, we introduce the dynamic pricing idea and the Interval Type-2 Fuzzy Inference System (IT2FIS) utilizing broad reinforcement learning to deal with various dynamic uncertainties in the IoAV under extreme weather. For the authenticity of experimental results, we utilize the VISSIM platform to collect experimental data and conduct simulations. Moreover, to accurately simulate extreme weather scenarios, we also account for the variability of infrastructure and road elements, including reduced transmission rates and decreased efficiency in executing tasks. Furthermore, to enhance the realism of the simulation, we incorporate historical weather data from NOAA for Shenyang in 2024 to model dynamic uncertainties under extreme weather conditions and conduct comparative experimental analyses focusing on task completion rates. Finally, the proposed framework was implemented on both a local setup and the Huawei Atlas 200I DK A2 device, illustrating its efficacy design.

Mobile edge computing (MEC) meets the requirements of various delay-sensitive applications by providing high-speed computing services to a large number of user vehicles simultaneously. Nevertheless, the inherent open feature of wireless channels and the constraints of limited spectrum resources present significant challenges to achieving both secure offloading and high offloading rate simultaneously. Millimeter wave (mmWave) can provide user vehicles with abundant spectrum resources, but its short wavelength causes high path loss. In this paper, we utilize hybrid beamforming and non-orthogonal multiple access (NOMA) technologies to improve the offloading rate of user vehicles and to interfere with eavesdroppers, thus improving the security of the offloading process in mmWave vehicular edge computing (VEC) networks. We first use the K-means algorithm to cluster user vehicles. Then, we minimize the system delay by jointly optimizing the analog beamforming matrix, the user vehicle transmit power and the allocation ratio of the MEC server computation resource while ensuring the security of the offloading process. The above optimization problem is formulated as a Markov decision process (MDP) and a twin Delayed Deep Deterministic Policy Gradient (TD3)-Dueling Double Deep Q Network (D3QN) based multi-agent secure computation offloading scheme is proposed to solve the MDP problem. Simulation results demonstrate that the TD3-D3QN based multi-agent scheme is able to adapt to highly dynamic VEC networks when guaranteeing the security of the offloading process and low system delay.

The diverse applications of internet of things (IoT) have significantly increased the demand for efficient and reliable wireless networks, making power consumption a critical concern. Reconfigurable intelligent surface (RIS) have been proposed as a solution to mitigate power consumption in wireless communication systems by dynamically adjusting the signal propagation direction between transmitters and receivers. Due to the operational status of IoT devices and the complex association relationships between RISs and devices, a dynamic and highly variable communication environment is typically resulted, which renders power consumption optimization more challenging, as compared to conventional methods that do not incorporate RISs. This paper addresses the optimization of power consumption and IoT device coverage rate in an indoor communication scenario to improve system performance. We design an Adaptive Hybrid Optimization Strategy based on the association between RISs and devices to maximize the device coverage rate. Additionally, we optimize the phase shifts of multiple RISs to minimize system power consumption using the relaxation transformative method while satisfying the coverage rate constraint. Extensive simulation results demonstrate that, in an indoor environment with several obstacles, the proposed algorithm achieves a higher device-centric coverage rate compared to a solution without RIS and exhibits lower power consumption compared to strategies that rely more on base stations.

Vehicular named data networking (VNDN), which integrates the principles of named data networks with vehicular ad hoc networks, represents a promising paradigm for future intelligent transportation systems. Nevertheless, VNDN faces significant hurdles, including broadcast storms from excessive interest packet flooding and reverse-path disruptions due to high vehicular mobility. To address these challenges, we introduce an adaptive forwarding with path optimization method. First, a dynamic caching algorithm is designed to optimize roadside unit storage efficiency and maximize cache hit rates. Second, a gated recurrent unit-based adaptive data forwarding mechanism is introduced to dynamically select optimal forwarders and preserve reverse paths via decentralized heartbeat detection and interface remapping, improving link reliability. Simulation outcomes demonstrate that the proposed approach significantly lowers data retrieval delays while curbing overall communication overhead.

With the expanding application range of drones, ensuring the data and user privacy security during the data sharing process in the Low-Altitude Networking (LAN) has become particularly crucial. Especially in the collaborative missions of drones, key escrow, cross-domain data loss, and privacy leakage become the key factors affecting mission completion. This paper proposes a cross-domain privacy preserving (CDPP) model and a certificateless designated verifier proxy signature scheme (CL-DVPS) for the LAN. By using blockchain technology, the CDPP model facilitates the creation of a cross-domain data sharing platform among diverse drones or drone swarms, which helps to achieve distributed data sharing and storage in the blockchain-based LAN (BLAN). Meanwhile, in the CL-DVPS scheme, the certificateless mechanism solves the problem of key escrow, the designated verifier mechanism ensures the non-transferability of the signature, and the construction basis of lattice theory can achieve anti-quantum security. These three designs can well protect the drone data and user privacy across the data sharing processes in the BLAN. Then, the correctness of the CL-DVPS scheme is demonstrated, and it is proven that the CL-DVPS scheme fulfills the properties of unforgeability, anonymity, and non-transferability under the random oracle model. The efficiency analysis involves the comparison of key and signature sizes, and the critical steps' time overheads. The results show that the CL-DVPS scheme has certain storage advantages and performance efficiency over similar literature. This work is capable of ensuring the data sharing privacy of the BLAN and mitigating the conflict between blockchain transparency and sensitive data privacy in cross-domain sharing. It also achieves the cross-domain collaboration and data utilization of diverse drone data.

Cold chain logistics systems (CCLSs) require the secure, efficient, and fresh management of cold products. Blockchain technology facilitates cross - institutional data - sharing for CCLSs, yet heterogeneous blockchains across different institutions give rise to ’data islands’ and privacy issues. Facing these problems,we propose a cross-chain privacy-preserving (CCPP) framework based on a notary mechanism. This CCPP model aggregates heterogeneous institutional chains through a notary network, enabling inter-blockchain operability while eliminating the data islands. Meanwhile, to ensure transaction security and address the quantum-vulnerability of traditional signature schemes, we design an identity-based ring signature (ID-RS) scheme. The ID - RS combines the identity mechanism (for traceability) with the ring mechanism (to guarantee the signer’s unconditional anonymity), and combats quantum threats by relying on the lattice assumption (a foundation for post-quantum security). Under the random oracle model, we prove the correctness, unforgeability, and anonymity of the ID-RS scheme. Additionally, experimental results under 80-bit security settings show that our ID-RS scheme outperforms other signature schemes in signature size and verification latency. These findings validate that the CCPP framework and the ID-RS scheme together offer an efficient and practical foundation for privacy-preserving, quantum-resistant data sharing among heterogeneous blockchain-based CCLSs. © 2014 IEEE.

Named data networks (NDNs), as one of the standards for information center networks (ICNs), have attracted substantial attention in recent years due to their efficient forwarding mechanisms. However, like other communication networks, the NDNs also face certain cybersecurity-related problems. The collusive Interest flooding attacks (CIFAs) exploit flaws in the internal forwarding mechanism of an NDN and send collusive Interest packets in the form of pulses with the assistance of a collusive Producer. As a result, the CIFAs are less detectable than the Interest flooding attacks (IFAs) and more difficult to distinguish from normal traffic. Many research solutions targeting traditional IFAs have been proposed, achieving good detection and defense results. However, due to different attack principles, CIFAs cannot be detected using the same scheme as the IFAs. To address this limitation, this study proposes a CIFA detection scheme that employs a radial basis function (RBF) neural network-based algorithm to recognize the extracted network traffic features. The proposed detection scheme can accurately distinguish between normal, malicious, and traffic fluctuation states, enabling rapid detection of CIFAs. The simulation results show that the proposed method performs considerably better than the existing detection methods in terms of attack detection accuracy and false positives.