The rise of Internet of Vehicles (IoV) technologies is transforming traffic management from isolated control to a collective, multi-vehicle process. At the heart of this shift is multi-vehicle dynamic navigation, which requires simultaneously routing large fleets under evolving traffic conditions. Existing path search algorithms and reinforcement learning methods struggle to scale to city-wide networks, often failing to capture the nonlinear, stochastic, and coupled dynamics of urban traffic. To address these challenges, we propose CityNav, a hierarchical, LLM-powered framework for large-scale multi-vehicle navigation. CityNav integrates a global traffic allocation agent, which coordinates strategic traffic flow distribution across regions, with local navigation agents that generate locally adaptive routes aligned with global directives. To enable effective cooperation, we introduce a cooperative reasoning optimization mechanism, in which agents are jointly trained with a dual-reward structure: individual rewards promote per-vehicle efficiency, while shared rewards encourage network-wide coordination and congestion reduction. Extensive experiments on four real-world road networks of varying scales (up to 1.6 million roads and 430,000 intersections) and traffic datasets demonstrate that CityNav consistently outperforms nine classical path search and RL-based baselines in city-scale travel efficiency and congestion mitigation. Our results highlight the potential of LLMs to enable scalable, adaptive, and cooperative city-wide traffic navigation, providing a foundation for intelligent, large-scale vehicle routing in complex urban environments. Our project is available at https://github.com/usail-hkust/CityNav.

Data deduplication can solve the problem of resource wastage caused by duplicated data. However, due to the limited resources of Internet of Things (IoT) devices, applying data deduplication to IoT scenarios is challenging. Existing data deduplication frameworks for the IoT are prone to inefficiency or trust crises due to the random allocation of edge computing nodes. Furthermore, side-channel attacks remain a risk. In addition, after IoT devices store data in the cloud through data deduplication, they cannot share their data efficiently. In this paper, we propose a secure and efficient data deduplication framework for the IoT based on edge computing and blockchain technologies. In this scheme, we propose a model based on parallel use of three-layer and two-layer architectures and introduce the RAndom REsponse (RARE) scheme to resist side-channel attacks. We also design a label tree to realise one-to-many data-sharing, which improves efficiency and meets the needs of the IoT. In addition, we use blockchain to resist collusion attacks. Experiments were conducted to demonstrate that our framework has advantages over similar schemes in terms of communication cost, security and efficiency.

Summary Attribute‐based signature (ABS) is a widely used cryptographic primitive, that provides both data integrity and fine‐grained privacy protection. However, the signing overhead of ABS is usually too large, making it unsuitable for resource‐constrained devices. Using cloud computing technology, the notion of outsourced attribute‐based signatures (OABS) was introduced to overcome this drawback. In OABS, the user can outsource him/her signing tasks to the signing‐cloud service provider (S‐CSP). The previous notion of privacy of OABS does not take the S‐CSP's attack into account, so it is not strong enough. To conform to the reality of cloud computing, we present a more accurate notion of privacy, called perfect privacy, which ensures that any adversary, even the S‐CSP, cannot identify which policy is used to generate the signature. Then, we propose a key‐policy OABS scheme for circuits based on multilinear mapping with perfect privacy. By using the outsourcing technique, in our scheme, the computation cost of the signer is only three or four pairing operations, which is independent of the complexity of the policy and the size of the attribute set. Our scheme achieves three advantages: perfect privacy, expressive policy, and constant signing cost, simultaneously.