Smart City Gnosys
Smart city article details
| Title | Self-Supervised Learning With Variational Autoencoders For Anomaly Detection In Autonomous Drone Fleets |
|---|---|
| ID_Doc | 48199 |
| Authors | Kirubhakaran M.; Sundarrajan M.; Gowtham T.; Agila D.; Akshya J.; Sundarrajan M.; Choudhry M.D. |
| Year | 2025 |
| Published | Proceedings - 4th International Conference on Smart Technologies, Communication and Robotics 2025, STCR 2025 |
| DOI | http://dx.doi.org/10.1109/STCR62650.2025.11019136 |
| Abstract | However, Unmanned Aerial Vehicles (UAVs) are increasingly being employed for different applications such as surveillance, logistics, disaster response, and smart city management, and thereby, the requirement for robust detection of anomalies and path optimization techniques has arisen. Existing traditional UAV monitoring systems typically depend on anomaly detection based on a rule or an unsupervised clustering method, i.e., DBSCAN and One-Class SVM, balancing high false positives and slow response time, correction, and suboptimal trajectory. To tackle these issues, we develop an AI-powered UAV anomaly detection and adaptive navigation framework where Isolation forests and MDPs are used to predict UAV trajectory, and DQNs are used for adaptive navigation. Experimental results in a simulated smart city arena with a multi-UAV fleet show that our proposed framework achieves 94% accuracy, 92% precision, and 91% recall, which are much superior to the existing approaches, such as One Class SVM (87% accuracy) and DBSCAN (80% accuracy). Additionally, the system compensates for false positives by 30%, improves energy efficiency by 15%, and reduces UAV collision risks by 20%, with high compatibility with real-time UAV fleet management. In the future, the scalability and the adaptability of the UAV-based smart city operation will be furthered through federated learning-based decentralised UAV coordination, reinforcement learning for autonomous swarm intelligence, and hybrid optimisation techniques for improving energy efficiency. © 2025 IEEE. |
| Author Keywords | Adaptive Flight Path Optimization; Autonomous Drone Fleet Management; Deep Reinforcement Learning; UAV Anomaly Detection; UAV Monitoring; UAV Navigation |
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