Jul 2026· International Journal of Emerging Technologies and Advanced Applications· 0 citations· 30 references
TL;DR
This paper reviews UAV swarm ad-hoc network communication technology for emergency scenarios, examines the technical characteristics and applicability boundaries of three network architectures, and surveys recent advances in routing and medium access, intelligent networking optimization, and transmission and security assurance.
Abstract
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.
In recent years, long-term communication systems for emergencies using UAVs have been developing rapidly in particular situations, such as disasters in remote regions. Previous machine learning approaches exhibit several limitations, including limited communication range, data loss in transmission systems, limited bandwidth availability, and abrupt communication failures, which collectively hinder overall system performance. To address this limitation, propose a hybrid, optimization-based UAV-assisted communication framework that integrates Federated Learning with swarm intelligence algorithm. The disaster-aware UAV deployment uses Federated Learning for decision-making to identify critical communication zones. A hybrid algorithm combining federated reinforcement learning with a graph attention-based UAV communication framework for consistent, low-latency data communication. The UAV network's lifecycle securities constant communication, energy efficient resource allocation and load balancing. In experiment analysis, 74.2% reduction in end-to-end latency (248 ms to 62 ms), 53.8% reduction in energy consumption, and a packet delivery ratio of up to 94% under varying network densities. The proposed system delivers reliable, scalable, and intelligent communication for emergency response in remote and disaster-affected areas.
C.Alakesan, Anthony Johnson A, M. M et al.· 2026 4th International Confe...· 0 citations
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
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
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.
Anita Murmu, Saurabh Kumar Srivastava, Nuthan Chingeetham et al.· IEEE Open Journal of the Com...· 0 citations
A comprehensive and structured review of methods for MACNs, with particular emphasis on AI-driven solutions and their relationship to classical and hybrid alternatives, and offers insights into the design of AI-driven MACNs that are efficient, scalable, and adaptive to evolving network and service demands.
Shafkat Khan Siam, Muhammad Yeasir Arafat, Muhammad Morshed Alam et al.· Artificial Intelligence Revi...· 0 citations
Timely and dependable information exchange is essential for large-scale unmanned aerial vehicle (UAV) swarms to coordinate under their fast motion, intermittent links, and limited energy on board. However, swarm deployments increasingly must contend with spectrum contention and jamming, as well as a lack of dependable infrastructure, which can reveal the shortcomings of traditional radio-frequency (RF) networking. This paper presents a synthesized overview of communication technologies and networking architectures for UAV swarm operations in FANETs. Representative studies were identified by a structured search and screening process across major venues of scholarly output, which are synthesized using a cross-layer lens including physical links, medium access, routing, information-centric networking, learning-enabled adaptation, and security. In this article, We compare RF/cellular with emerging high-capacity links including millimeter-wave and free-space optical communication, and then show how routing/indirection and content/function-centric paradigms (NDN/NFN) can mitigate fragility imposed by reliance on brittle end-to-end paths. Lastly, we analyse learning-based control (especially multi-agent reinforcement learning) for communication-aware mobility and resources management, as well as security approaches for contested settings. The resulting design perspective highlights recurring trade-offs among reliability, latency, throughput, energy, and integrity, and identifies practical research directions toward more robust and deployable swarm communication systems.
Azzam Almekhlafi, Y. Alqudsi· 2026 6th International Confe...· 0 citations
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