AI-Driven Energy-Efficient Routing and UAV Trajectory Optimization for UAV-Assisted Internet of Things Sensor Networks in 6G Environments

Abstract

The rapid expansion of the Internet of Things (IoT) and the emergence of sixth-generation (6G) wireless networks have created unprecedented opportunities for large-scale intelligent sensing, real-time data collection, and ubiquitous connectivity. However, the deployment of massive IoT sensor networks faces significant challenges, including limited energy resources, dynamic network topologies, communication reliability issues, routing inefficiencies, and coverage constraints, particularly in remote, disaster-stricken, and infrastructure-deficient environments where conventional terrestrial communication systems often fail to provide reliable services. Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution for enhancing network coverage, improving data collection efficiency, and supporting communication services in IoT ecosystems; nevertheless, their integration introduces additional challenges related to energy consumption, trajectory planning, routing optimization, and resource allocation. To address these issues, this paper proposes an AI-Driven Energy-Efficient Routing and UAV Trajectory Optimization Framework for UAV-assisted IoT sensor networks operating in 6G environments. The proposed framework integrates intelligent routing, adaptive energy management, and dynamic UAV trajectory optimization within a unified cross-layer architecture and develops a comprehensive mathematical model to characterize the relationships among energy consumption, communication delay, packet delivery performance, routing decisions, and UAV mobility. Furthermore, a Deep Reinforcement Learning (DRL)-based optimization algorithm is introduced to enable autonomous decision-making and adaptive network control under dynamic environmental conditions. The proposed approach continuously monitors key network parameters, including residual sensor energy, link quality, transmission distance, traffic load, UAV battery status, and data collection requirements, and dynamically determines optimal routing paths and UAV flight trajectories to minimize overall energy consumption while maximizing network lifetime, packet delivery ratio, and data collection efficiency. In addition, the framework leverages the ultra-reliable low-latency communication capabilities envisioned for future 6G infrastructures to facilitate intelligent coordination between UAV platforms and IoT sensor nodes. Performance evaluation under various network densities, mobility scenarios, and communication conditions demonstrates that the proposed framework significantly reduces energy consumption, improves routing efficiency, extends network lifetime and enhances packet delivery performance, and decreases communication overhead and data collection latency compared with conventional approaches. The results confirm that the integration of artificial intelligence, energy-aware routing, and UAV trajectory optimization provides an effective and scalable solution for next-generation UAV-assisted IoT systems and establishes a robust foundation for intelligent 6G-enabled wireless sensor networks.