Optimizing Energy Efficiency and Latency in UAV-Assisted 6G Networks through Intelligent Routing Algorithms - A Systematic Review

Abstract

The advent of 6G networks is transforming wireless communication, with Unmanned Aerial Vehicles (UAVs) playing a key role in expanding connectivity to remote and disaster-affected areas. While offering scalability and flexibility, UAV-assisted networks face challenges such as high energy consumption and latency. Efficient routing is essential to maximize UAV operational time and ensure low-latency data transmission. This review examines recent advancements in intelligent routing algorithms for UAV-assisted 6G networks, focusing on energy efficiency and latency reduction. Approaches using artificial intelligence, machine learning, and optimization techniques including deep reinforcement learning, swarm intelligence, and adaptive routing are explored. These strategies enable real-time path adjustments based on energy levels, environmental factors, and network conditions. Integration with edge computing further enhances responsiveness. The paper identifies current strengths and limitations, highlighting key research gaps in scalability, computational complexity, and real-time feasibility. Promising directions include renewable energy integration, energy-aware protocols, and hybrid optimization models. This review offers valuable insights for advancing UAV-based 6G communication systems.