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Smart city article details
| Title | Learning-Based Uav Swarm Video Analytics Orchestration In Disaster Response Management |
|---|---|
| ID_Doc | 34952 |
| Authors | Gao T.; Goins D.; Ballotti C.; Liu J.; Qu C. |
| Year | 2025 |
| Published | SN Computer Science, 6, 5 |
| DOI | http://dx.doi.org/10.1007/s42979-025-04063-5 |
| Abstract | Unmanned Aerial Vehicles (UAV) are increasingly used in sectors such as smart cities, precision agriculture, disaster response, and last-mile logistics, with Multi-Access Edge Computing (MEC) playing a key role in enhancing their capabilities. In disaster response management, UAV assist in locating survivors, tracking objects, mapping post-disaster areas, and delivering critical supplies to inaccessible regions. However, unstable network conditions in disaster environments pose significant challenges to maintaining reliable video transmission and real-time decision-making. In this paper, we propose a comprehensive orchestration framework that integrates both offline and online strategies to optimize UAV video transmission, multi-UAV networking, and network management. The offline strategy combines policy-based orchestration with batch reinforcement learning (RL) to prepare UAV for deployment by optimizing network settings and video properties. The online strategy leverages reinforcement learning to enable real-time trajectory prediction and adaptive multi-UAV networking, ensuring efficient communication and decision-making during missions. Our experimental results, conducted across various Disaster Response Scenarios (DRS), demonstrate that the DQN-based approach significantly improves network throughput and round-trip time (RTT) compared to traditional methods, e.g. heuristic-based and rule-based, achieving approximately 87% of the Oracle baseline. The proposed framework enhances both the efficiency and adaptability of UAV operations, providing a robust solution for disaster response management. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025. |
| Author Keywords | Disaster response management; Reinforcement learning; UAV video analytics; Unmanned aerial systems |
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