Value-based Proactive Caching for Sensing Data in Internet of Vehicles

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.