Latency-Sensitive Caching-Assisted Offloading in Digital Twin-Enabled IoV Using Particle Swarm Optimization

With the continuous growth of sixth-generation (6G) networks, the Digital Twin (DT) paradigm has emerged as a critical technology for controlling the fast-developing Internet of Vehicles (IoV). Vehicles with high mobility and complicated applications require low latency and efficient system throughput. In this study, we present a Particle Swarm Optimization (PSO) approach to solve the Latency-Aware Caching-Aided Offloading Problem (LCAOP) in a DT-enabled IoV system. The PSO algorithm optimizes job allocation and offloading techniques between cars, roadside units (RSUs), and the cloud to reduce total system latency and increase throughput. Extensive simulations demonstrate that the PSO-based technique decreases latency and improves throughput while beating other classic evolutionary algorithms.