Improved AFSA-Based Energy-Aware Content Caching Strategy for UAV-Assisted VEC

UAV-assisted VEC can provide content caching services for vehicles by flying close to the vehicles for vehicle's QoS. However, in real-world scenarios with traffic congestion, due to the battery capacity and cache space limitations of UAVs, low content response speed and high response latency may occur. Based on this, we proposed a dynamic energy consumption-based content caching strategy in UAV-assisted VEC. We use the PSO algorithm to solve the problem and obtain the optimal UAV deployment location. For content caching, we construct a content caching model by considering UAV deployment, vehicle user preference, UAV cache capacity, and UAV energy consumption with the goal of minimizing content request latency. In addition, we propose an IAFSA-based content caching strategy. We reduce the solution space of the fish swarm algorithm, decrease the number of caching decisions, and improve the convergence performance of AFSA by employing dynamic horizons and step sizes. Experimental results show that the proposed IAFSA effectively reduces the average content request latency of the vehicle, improves the cache hit rate, and reduces the number of content return trips. Particularly, the proposed strategy reduces the average content request latency by more than 9.84% compared to the baseline algorithm.