2026· IEEE Open Journal of the Communications Society· Vol 7, pp. 8772-8785· 0 citations· 24 references
TL;DR
This study proposes an intelligent Q-learning-enhanced Evolutionary Game Theory (QEGT) routing mechanism for USNs that leverages game-theoretic incentives and Q-learning to adaptively select strategies.
Abstract
Unmanned Aerial Vehicle (UAV) swarm networks play a vital role as real-time communication relays. It provides communication services in applications such as disaster management, intelligent transport, and environmental monitoring. The inherent characteristics of Unmanned Swarm Networks (USNs), such as flexible deployment and high operational adaptability, make them valuable in environments with limited infrastructure. However, the highly dynamic and decentralized nature of USNs presents major challenges in designing efficient and reliable routing protocols. Traditional approaches often struggle with frequent topology changes and high mobility of USNs. To address these challenges, this study proposes an intelligent Q-learning-enhanced Evolutionary Game Theory (QEGT) routing mechanism for USNs. The proposed scheme leverages game-theoretic incentives and Q-learning to adaptively select strategies. The optimal next-hop UAV is selected based on residual energy, queue length, and proximity to the destination. Extensive simulations in NS-3 demonstrate the effectiveness of the proposed QEGT scheme. Simulation results show that the proposed QEGT scheme outperforms the state-of-the-art approaches in terms of network survival time, the number of successfully delivered packets, average hop count, and average delay.
Results show that topology‐aware cooperative learning can provide a scalable and practical solution for reliable UAV communication in future intelligent aerial and 6G‐enabled networks, particularly in dense deployments.
V. Nam, A. Chehri, Weiwei Jiang et al.· Expert systems· 0 citations
Major disasters such as earthquakes, floods, and wildfires can rapidly destroy terrestrial communication infrastructure, producing an extreme operating environment in which power, road, and network outages compound one another. Owing to their rapid deployability, flexible networking, and three-dimensional mobility, unmanned aerial vehicle (UAV) swarms are being studied as a flexible component of emergency communication systems. This paper reviews UAV swarm ad-hoc network communication technology for emergency scenarios. It examines the technical characteristics and applicability boundaries of three network architectures---flat, hierarchical clustering, and space-air-ground integrated---and surveys recent advances in routing and medium access, intelligent networking optimization, and transmission and security assurance. Particular attention is given to the reported performance and applicability of emerging approaches, including reinforcement-learning-based adaptive routing, decentralized federated learning, digital twins, and semantic communication, under highly dynamic and resource-constrained conditions. Drawing on studies of emergency routing, post-disaster data collection, semantic forwarding, and multi-layer coverage, the paper assesses current validation methods and outlines research directions in energy use, scalability, security, resilience, and standardization. Its contribution is a cross-layer comparison that relates architecture choices to protocol requirements, implementation costs, and validation maturity.
Yihang Ren, Huatao Zhu, Jie Zhang· International Journal of Eme...· 0 citations
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.
Mojtaba Nasehi· Internet of Things and Cloud...· 0 citations
Flying Ad Hoc Networks (FANETs) have become an important research area because of their ability to provide communication between unmanned aerial vehicles (UAVs) in a wide range of applications such as military operations, agriculture, environmental monitoring, and smart transportation. However, the limited battery capacity of UAVs and the high dynamicity of FANETs make energy-efficient routing a great challenge. An efficient routing protocol should not only be energy efficient but also provide reliable communication, improve packet delivery, reduce delay, and increase network lifetime. Recently, many energy-efficient routing protocols have been proposed by researchers based on different approaches such as clustering, swarm intelligence, reinforcement learning, fuzzy logic, and hybrid techniques. Each approach has its own advantages and limitations depending on the network environment and application requirements. This paper offers an in-depth review of energy-efficient routing protocols that have been developed for FANETs. The reviewed protocols are analyzed according to their parameters used in the study, the simulator used, advantages, and limitations. We also provide comparisons that point out the strengths and weaknesses of existing approaches. In addition, this review highlights the main research challenges, such as high mobility, dynamic topology, routing overhead, scalability, and security, and discusses future research directions to design more intelligent and energy-aware routing protocols. The present review results and conclusions offer researchers a better understanding of current trends, and they also point out potential future research directions in energy-efficient FANET routing.
Ragvinder Kaur, Amit Sharma· Journal of Intelligent Decis...· 0 citations
The Enhanced RDAP (e-RDAP), a multi-criteria association policy that combines RSSI, Packet Delivery Ratio (PDR), and communication delay with an adaptive deployment strategy is introduced, indicating that QoS-aware multi-criteria association provides additional gains beyond load-aware association alone.
Lucas Baptista de Moraes, N. Fernandes, Fernanda G. O. Passos et al.· Annals of Telecommunications· 0 citations
The adoption of a new communication paradigm is getting attention in the research world, where Flying Ad Hoc Networks (FANETs) have been deemed a viable approach for supporting coordinated operations of multiple Unmanned Aerial Vehicles (UAVs) in situations characterized by dynamic environments and the absence of infrastructure. Taking into consideration these drawbacks, in this paper, a novel and up-to-date AI-Based Mobility and Topology Management Framework for Flying Ad Hoc Networks via Hybrid Bio-Inspired Optimization is proposed. The proposed systems combine a Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) inspired model, introducing a novel hybrid model, with Artificial Intelligence techniques to provide a dynamic framework for optimizing UAV mobility patterns, topology formation, and communication paths within the proposed framework. Predictive mobility analysis using AI to make networks more adaptable and minimize topology changes. In addition, the hybrid optimization method will optimize the routing efficiency, reduce the communication overhead, and increase the packet delivery efficiency between nodes in the highly dynamic FANET environment. Results of experimental analysis prove that the proposed scheme has a better PDR of 96.4%, lower EED or end-to-end delay of 31%, and better topology stability that performs better than the traditional mobility management approaches with respect to reducing energy consumption.
Anshu Vashisth, Gagandeep Kaur, Ruhi Saxena et al.· 2026 7th International Confe...· 0 citations