Sensing data (SD) plays an important role in safe-related applications for Internet of Vehicles. Proactively caching required sensing data (SD) is a pivotal strategy for alleviating network congestion and improving data accessibility. Despite merits, existing studies predominantly address SD caching within a single time slot, which may not be scalable to scenarios involving multi-slots. Furthermore, the oversight of service capacity at caching nodes could lead to significant queuing delays in SD reception. To tackle these limitations, we jointly consider the problem of anchoring caching placement and requests allocation for SD. A value model incorporating both temporal and spacial characteristics is first proposed to estimate the significance of different caching decisions. Subsequently, a stochastic integer nonlinear programming model is provided to optimize the long-term system performance, which is converted into a series of online optimization problem by leveraging the Lyapunov method and linearized via introducing auxiliary variables. To expedite the solution, we provide a binary quantum particle swarm optimization based algorithm with quadratic time complexity. Numerical investigations demonstrate the superiority of proposed algorithms compared with other schemes in terms of energy consumption, response latency, and cache-hit ratio.

翻译：感知数据在车联网安全相关应用中发挥着重要作用。主动缓存所需感知数据是缓解网络拥塞、提高数据可访问性的关键策略。尽管现有研究具有诸多优点，但主要关注单一时隙内的感知数据缓存，难以扩展至多时隙场景。此外，对缓存节点服务容量的忽视可能导致感知数据接收产生显著排队延迟。为应对这些局限，本文联合研究感知数据的锚点缓存部署与请求分配问题。首先提出融合时空特征的价值模型以评估不同缓存决策的重要性；随后建立随机整数非线性规划模型以优化长期系统性能，该模型通过李雅普诺夫方法转化为系列在线优化问题，并借助辅助变量实现线性化。为加速求解，提出具有二次时间复杂度的二进制量子粒子群优化算法。数值实验表明，所提算法在能耗、响应延迟和缓存命中率方面均优于其他对比方案。