Joint Optimization of Delay and Energy Consumption in Urban IoV: A Resource Allocation and Cooperative Caching Strategy

As in-vehicle services grow, the increasing size of cached content prolongs wait times for users. For electric vehicles, balancing efficient communication with reduced energy consumption remains a challenge. In this paper, a vehicle clustering cooperative caching model for urban Internet of Vehicles (IoV) systems is proposed. This model decreases system energy consumption and task delay by leveraging buses as regular mobile Roadside Units (RSUs) and pre-caching nodes. It includes a kinetic energy recovery scheme for vehicles and an Energy Harvesting (EH) mechanism for RSUs, both intended to further reduce energy consumption. Terahertz (THz) technology is harnessed for Vehicle-to-Vehicle (V2V) communication to accelerate caching tasks and reduce tasks delay. To address these challenges, we propose the Deep Deterministic Policy Gradient (DDPG)-based Power Splitting (DPS) algorithm to address the needs of information transmission and energy recharging of buses while in motion. The proposed (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1+1$ </tex-math></inline-formula>)-Evolutionary Strategy (ES)-based joint Task Decomposition and Bandwidth Allocation (TDBA) algorithm, which transmits the decomposed task file segments in parallel on different paths, reduces the additional delay and energy consumption caused by frequent task switching. Furthermore, the proposed Time-Location Preference User Rated Recommendation (TLPURR) algorithm recommends appropriate content based on the vehicle user’s time and location preferences, reducing the delay and energy consumption of obtaining content from remote cloud resources. Simulation results demonstrate that our proposed algorithms have a significant improvement in the delay and energy consumption metrics compared to other algorithms.