With the increasing complexity of logistics operations, traditional static vehicle routing models are no longer sufficient. In practice, customer demands often arise dynamically, and multi-depot systems are commonly used to improve efficiency. This paper first introduces a vehicle routing problem with the goal of minimizing operating costs in a multi-depot environment with dynamic demand. New customers appear in the delivery process at any time and are periodically optimized according to time slices. Then, we propose a scheduling system TS-DPU based on an improved ant colony algorithm TS-ACO to solve this problem. The classical ant colony algorithm uses spatial distance to select nodes, while TS-ACO considers the impact of both temporal and spatial distance on node selection. Meanwhile, we adopt Cordeau’s Multi-Depot Vehicle Routing Problem with Time Windows (MDVRPTW) dataset to evaluate the performance of our system. According to the experimental results, TS-ACO, which considers spatial and temporal distance, is more effective than the classical ACO, which only considers spatial distance.

Periodic pattern mining plays an important role in revealing recurring behavioral regularities from temporal sequence data. Most existing approaches, however, are developed for single-sequence settings and rarely account for quantitative information or sequence-level constraints when patterns recur across multiple sequences. This limits their usefulness in practical scenarios, where a pattern is expected to be not only periodic but also quantitatively significant in a sufficiently large portion of sequences. In this work, we formulate the problem of mining High-Quantitative Periodic Frequent Patterns (HQPFPS) from multi-sequence databases and propose an efficient algorithm, termed MHQPFPS. The proposed method evaluates pattern significance through a quantitative ratio within each sequence and exploits a sequence-level upper bound to effectively prune unpromising candidates during pattern growth. To support efficient evaluation, a compact list-based structure is introduced to maintain support, periodicity, and quantitative statistics, thereby avoiding repeated scans of the database. These components are combined within a depth-first exploration framework to systematically generate valid patterns while discarding those that fail to satisfy the required periodic or quantitative constraints. Experimental results on both real-world and synthetic datasets show that MHQPFPS is able to extract meaningful high-quantitative periodic patterns across multiple sequences. Moreover, the results indicate that the proposed pruning strategies substantially reduce computational cost in terms of runtime and memory consumption under a wide range of parameter settings.

With the rapid development and widespread adoption of Internet of Things (IoT) technology, the innovative concept of the Internet of Vehicles (IoV) has emerged, ushering in a new era of intelligent transportation. Since vehicles are mobile entities, they move across different domains and need to communicate with the Roadside Unit (RSU) in various regions. However, open environments are highly susceptible to becoming targets for attackers, posing significant risks of malicious attacks. Therefore, it is crucial to design a secure authentication protocol to ensure the security of communication between vehicles and RSUs, particularly in scenarios where vehicles cross domains. In this paper, we propose a provably secure cross-domain authentication and key agreement protocol for IoV. Our protocol comprises two authentication phases: intra-domain authentication and cross-domain authentication. To ensure the security of our protocol, we conducted rigorous analyses based on the ROR (Real-or-Random) model and Scyther. Finally, we show in-depth comparisons of our protocol with existing ones from both security and performance perspectives, fully demonstrating its security and efficiency.