Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 559-569· 0 citations· 25 references
Abstract
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.
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.
Yihang Ren, Hua-Tao Zhu, Jie Zhang· International Journal of Eme...· 0 citations
This study explores how reliable intelligent drone swarms are utilized as a single efficient bi-functional agent for precision agriculture and disaster relief support. Even though autonomous Unmanned Aerial Vehicles (UAVs) change individual sectors, traditional systems with one agent face limits in operational scale, battery duration as well as fault tolerance. This paper proposes a resilient co-design architecture based on decentralized swarm intelligence, bio-inspired optimization algorithms alongside dynamic ad-hoc networking capable of seamlessly transitioning from agricultural monitoring to search-and-rescue missions in minutes. We propose a hybrid coordination scheme, integrating Modified Particle Swarm Optimization (MPSO) and decentralized consensus protocols, allowing for near real-time avoidance of obstacles, task allocation and on-the-fly area coverage based on limited communication mapping in unstructured environments. HIL simulations and field validations over diff operational topologies show that the suggested system can decrease overall mission time by 32% and increase coverage efficiency by 24% compared to conventional centralized routing algorithms. Also, the swarm showed substantial self-healing properties even after massive nodes loss 30% active nodes) still providing 88% of operational throughput. In the end, this renewable energy and nutrient-cycling infrastructure provides an economically competitive, high-performance solution for rural resilience and resource management that reconciles the dual rescue technologies of contemporary commercial food production with advanced emergency crisis technologies.
Jennifer A. Doudna· International Journal of Mod...· 0 citations
Reliable data collection is essential for disaster-oriented Internet of Things (IoT) systems, where damaged terrestrial communication infrastructure often leaves sensed data buffered at disconnected end devices. In Unmanned Aerial Vehicle (UAV)-Internet of Things device (IoTD) collaborative data collection, random UAV faults and limited energy and buffer resources further complicate mission execution, making fault-tolerant scheduling crucial for robust data recovery. To address these issues, a unified framework is developed by integrating dynamic UAV reliability modeling, Maximum Distance Separable (MDS)-coded fault-tolerant backup, and collaborative scheduling optimization. Within this framework, a data fault-tolerance mechanism, termed MFTB, and a bilevel collaborative scheduling algorithm, termed LP-DCFS, are proposed. Simulation results indicate that, in the evaluated scenarios, the proposed methods achieve better overall performance than the considered baselines. In a representative high-load, high-failure scenario, MFTB reduces data loss by 4.8% and 37.5% compared with Buffer-Limited Retransmission (BLR) and Replication, respectively, while LP-DCFS increases the amount of recovered data by 33.9%, 32.3%, and 53.1% compared with ACEPSO, ADE-DMRM, and DQN, respectively. Under the modeled independent random crash and non-return faults and the evaluated simulation settings, these results suggest that coordinating failure-risk characterization, data-protection mechanisms, and task-scheduling strategies can improve the robustness and data-recovery capability of disaster-oriented UAV-assisted data collection.
Hailu Xin, Weidong Bao, Hui Yan et al.· Drones· 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
This paper analyzes the factors affecting communication interactions between UAVs and proposes a bidding-based grouping method to eliminate ineffective communication interactions, and introduces a network simplification algorithm based on reducing the number of triangular network topologies to optimize the communication network structure.
Wei-Xing Xia, Peng Chen, Fei-Fei Song et al.· Drones· 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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
