Value-based Proactive Caching for Sensing Data in Vehicular Networks: An Operator's Perspective

TL;DR

The paper addresses proactive caching of time-sensitive sensing data in vehicular networks under a long-term energy budget. It introduces a region-centric, value-based SD caching model that combines temporal freshness, spatial validity, and SD popularity to form a long-run objective $V(t)$, then solves the stochastic optimization with Lyapunov drift-plus-penalty, yielding a per-slot problem $\mathbf{P2}$. Two solution approaches are developed: OCDA, which linearizes and solves an MILP for high-quality decisions, and BQPSO-DA, a fast heuristic based on Binary Quantum Particle Swarm Optimization; both determine caching placements $\mathbf{X}$ and request allocations $\mathbf{Y}$ online. Numerical investigations show that OCDA delivers superior cache-hit ratios, lower energy consumption, and stricter latency satisfaction across congestion levels, with BQPSO-DA offering a competitive and faster alternative. The work provides practical insights for operators managing SD services in IoVs and points to AI-driven enhancements to further reduce computation and improve predictive accuracy.

Abstract

Access to sensing data (SD) is crucial for vehicular networks to ensure safe and efficient transportation services. Given the vast volume of data involved, proactive caching required SD is a pivotal strategy for alleviating network congestion and improving data accessibility. Despite merits, existing studies predominantly address SD caching within a single slot. Therefore, these approaches lack scalability for scenarios involving multi-slots and are not well-suited for network operators who manage resources within a long-term cost budget. Moreover, the oversight of service capacity at caching nodes may result in substantial queuing delays for SD reception. To tackle these limitations, we jointly consider the problem of anchoring SD caching and allocating from an operator's perspective. A value model incorporating both temporal and spacial characteristics is given to estimate the significance of various caching decisions. Subsequently, a stochastic programming model is proposed 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.

Figures (10)

• Figure 1: Proactive caching in a traffic status monitoring scenario.
• Figure 2: Impact of $\mathcal{V}$
• Figure 3: Fitness Loss $F(\mathbf{X},\mathbf{Y})$
• Figure 4: Time Average Energy
• Figure 5: Maximum Delay in Regions

Theorems & Definitions (8)

• Theorem 1
• proof
• Theorem 2
• proof
• Theorem 3
• proof
• Theorem 4
• proof