Aug 2026· Internet of Things and Cloud Computing· 0 citations· 2 references
TL;DR
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.
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.
An Artificial Intelligence-Enabled UAV Communication Framework (AI-UCF) designed to optimize aerial communication performance is proposed and demonstrates substantial improvements in coverage probability, throughput, latency, and energy efficiency compared with traditional communication architectures.
Dr T Anvesh, Vengala Vishnuvardhan, Tumma Raghavendra· International Scientific Jou...· 0 citations
This paper investigates a solar-aware trajectory optimization for unmanned aerial vehicles (UAVs) deployed in Internet of Things (IoT) networks for environmental monitoring. We develop a comprehensive energy consumption model that integrates aerodynamic flight dynamics with solar energy harvesting under realistic conditions. Building on this model, we formulate a trajectory optimization problem that considers UAV flight path, velocity, and energy dynamics to maximize reliable data collection and transmission throughput. The problem is inherently non-convex due to probabilistic constraints, energy harvesting non-linearities, and mobility restrictions. To overcome these challenges, we propose a successive convex approximation framework that reformulates the problem into tractable convex subproblems, which are solved iteratively with guaranteed convergence. Simulation results across diverse IoT deployment scenarios demonstrate that the proposed design substantially enhances UAV endurance, energy efficiency, and data throughput compared to benchmark approaches. The results show that solar-powered UAV communications are effective for large-scale IoT networks and contribute to more sustainable, reliable airborne sensing systems.
Hanan Aljehani, Abdelhamid Salem, K. Hamdi· International Mediterranean...· 0 citations
The unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) network architecture has emerged as a key technology for supporting communications in emergency scenarios. The quality and freshness of the data collected by UAVs directly impact the effectiveness of emergency decision-making and the overall responsiveness of the system in time-critical situations. However, limited by the battery capacity of UAVs, a fundamental tradeoff exists between information freshness and energy consumption in UAV-assisted IoT networks, which constrains the effective coverage range of emergency operations. Accordingly, this paper explores the inherent trade-off between Age of Information (AoI) and energy consumption in UAV-assisted emergency IoT systems. To reflect the highly dynamic and uncertain channel conditions typical of post-disaster environments, this work further incorporates an imperfect channel state information (CSI) model. Then, a probabilistic constraint AoI-energy tradeoff problem is formulated to jointly optimizing the time allocation of data collection, UAV trajectory, and duration of time slots. To address the resulting non-convex non-linear problem, we first convert the probabilistic constraint problem into a non-probability one, then employ a block coordinate descent method to iteratively solve the highly coupled multi-variable problem. Finally, comprehensive simulation results validate the effectiveness of the proposed method.
Mingan Luan, Xin Zhang, Zheng Chang et al.· IEEE Transactions on Green C...· 0 citations
Driven by the vision of a thriving low-altitude economy and aiming to provide on-demand services for diverse entities, this paper investigates an integrated sensing and communication (ISAC)-enabled low-altitude wireless network (LAWN). Benefiting from flexible mobility and cost-effective cooperative deployment, multiple ISAC-enabled uncrewed aerial vehicles (UAVs) are emerging as an ISAC paradigm for on-demand deployment in LAWN. However, due to the complex inter-UAV interference and resource coupling in LAWN, it is difficult to properly coordinate different constrained resources, including spatial deployment, energy, and wireless channels, to simultaneously meet the sensing and communication requirements. To address these challenges, this paper formulates a sensing–communication optimization (SCO) problem in LAWN by jointly optimizing subcarrier allocation, transmit power allocation, and three-dimensional (3D) UAV deployments to maximize network utility while satisfying quality of service (QoS) requirements for multiple users and target sensing mutual information (MI) requirements. To enable efficient solutions, we propose a hierarchical optimization approach that vertically decouples the SCO problem into two subproblems: a top level employing a Gibbs Sampling–based multi-UAV 3D deployment algorithm for efficient exploration and deployment optimization, and a bottom level performing resource allocation via a dual-based joint power and subcarrier allocation algorithm. Simulation results demonstrate that the proposed approach achieves a favorable trade-off between communication and sensing and significantly enhances the overall performance and adaptability of the LAWN.
Cheng Ma, Zewei Jing, Qinghai Yang et al.· IEEE Transactions on Wireles...· 0 citations
: The emergence of Unmanned Aerial Vehicle (UAV)-enabled Wireless Energy Transfer (WET) and Simultaneous Wireless Information and Power Transfer (SWIPT) technology provide a promising solution to overcome the energy sustainability limitations of traditional harvesting-reliant sensor networks. However, in large-scale Battery-free SWIPT-enabled Sensor Networks (BSSN) characterized by sparse node distribution and heterogeneous energy consumption and harvesting rates, employing a single UAV for energy replenishment often suffers from insufficient operation continuity and low charging efficiency. To overcome these challenges, a Multi-UAV Collaborative Energy Charging for BSSN Based on Multi-Agent Deep Deterministic Policy Gradient (MCEC-MADDPG) is proposed in this paper. Specifically, we construct a collaborative one-to-one precision energy supply model where UAVs hover directly above specific nodes to achieve power transmission without complex beamforming requirements. To achieve collaborative scheduling among multiple UAVs in wide-area dynamic environments, the energy replenishment problem is first formulated as a Partially Observable Markov Decision Process (POMDP). Subsequently, the Centralized Training with Decentralized Execution (CTDE) architecture of the MADDPG algorithm is leveraged to solve this POMDP, which effectively tackles the non-stationarity challenge inherent in multi-agent environments. Simulation results demonstrate that MCEC-MADDPG exhibits superior performance in terms of convergence speed and stability. It enables the adaptive emergence of spatial-division collaborative strategies, significantly enhances the average residual energy of the network, and elevates the node survival rate to nearly 90%. Compared with Deep Deterministic Policy Gradient (DDPG), the traditional static Partition-Greedy method, the heuristic K-Means algorithm and the dynamic Two-Layer task allocation strategy, the proposed approach demonstrates substantial advantages.
Xiangyi Le, Deyu Lin, Yufei Zhao et al.· Computers, Materials & C...· 0 citations
Unmanned aerial vehicles (UAVs) have emerged as promising aerial platforms for next-generation wireless networks, offering three-dimensional mobility, rapid deployment, and high line-of-sight (LoS) link probability. This paper presents a structured overview of UAV-assisted wireless communications, covering key network architectures, air-to-ground channel characteristics, mobility-aware deployment and trajectory design, resource management, and multi-UAV cooperation. We further review recent integrations of UAVs with emerging technologies such as artificial intelligence (AI)-driven optimization, reconfigurable intelligent surface (RIS), integrated sensing and communication (ISAC), multiple-input multiple-output (MIMO), and semantic communication. Integration scenarios and recent research trends in beyond-5G and 6G networks are discussed, and open challenges along with future research directions are identified. This survey aims to provide a concise yet comprehensive reference for researchers and engineers working on UAV-assisted wireless network design.
Jueun Jeong, Sehyeon Kwon, Changhui Kim et al.· International Conference on...· 0 citations