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| Title | Enhancing Capabilities And Security Features Of Drone Networks Through Machine Learning: A Comprehensive Overview |
|---|---|
| ID_Doc | 23748 |
| Authors | Gomes D.; Hasan M.; Philip S.R. |
| Year | 2025 |
| Published | Advances in Science, Technology and Innovation, Part F372 |
| DOI | http://dx.doi.org/10.1007/978-3-031-80961-3_5 |
| Abstract | The integration of machine learning algorithms into drone networks offers significant opportunities to enhance the processing power and security of Unmanned Aerial Vehicles (UAVs). This chapter provides a comprehensive overview of how leveraging IoT, and collaborative learning techniques can enable drones to operate autonomously, adapt to changing environments, and perform complex tasks with greater efficiency. By analyzing large datasets, drones can achieve improved situational awareness, predictive maintenance, and optimized mission planning through adaptive reinforcement learning and deep reinforcement learning. Techniques like federated learning, collaborative swarm intelligence, and ensemble learning contribute to the accuracy and effectiveness of drone operations, making them valuable in applications such as traffic management, disaster response, smart city infrastructure, and agricultural monitoring. The chapter also addresses critical issues such as privacy concerns, data security, regulatory compliance, and transparency in data usage. Additionally, it explores future directions, including the development of advanced machine learning algorithms, edge computing, interoperability, ethical frameworks, multi-domain integration, resilience, and human-drone interaction. By continuing to explore these areas, researchers and industry professionals can drive innovation and fully harness the potential of machine learning to advance drone networks for both civilian and military uses. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. |
| Author Keywords | Autonomous systems; Drone networks; Features; IoT (Internet of Things); Machine learning; Privacy concerns; Regulatory compliance; Security |
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