DEEP REINFORCEMENT LEARNING FOR DYNAMIC SHARDING IN UAV NETWORKS

This study investigates the application of Deep Reinforcement Learning (DRL) for dynamic sharding in Unmanned Aerial Vehicle (UAV) networks, addressing the limitations of traditional static resource management techniques. As UAV networks expand their roles across diverse sectors, including agriculture, logistics, surveillance, and disaster management, the need for efficient and adaptive resource allocation becomes increasingly critical. UAVs operate under constraints such as limited battery life, communication bandwidth, and processing power, making optimal resource management essential for mission success. Traditional static sharding methods often fail to adapt to rapidly changing operational conditions, such as fluctuations in environmental factors or mission requirements, leading to inefficiencies, increased latency, and potential mission failures. This research proposes a DRL-based framework that dynamically allocates tasks and resources among UAVs based on real-time performance metrics and environmental conditions. By employing a learning-based approach, the DRL framework is capable of continuously improving its decision-making processes through experience, allowing it to respond effectively to the complexities inherent in UAV operations. The findings indicate that the DRL-based dynamic sharding solution significantly enhances operational efficiency, reduces latency, and improves overall resource utilization in UAV networks. The results demonstrate that the DRL approach not only optimizes task allocation but also ensures a balanced distribution of workload among UAVs, ultimately leading to increased reliability and responsiveness of the network. This work contributes to the development of more resilient and adaptive UAV systems, addressing the challenges posed by static resource management methods. Furthermore, it lays the groundwork for future advancements in UAV network management, highlighting the potential of machine learning techniques to revolutionize resource allocation strategies in dynamic and complex environments. The implications of this research extend beyond UAV networks, offering insights into the broader applications of DRL in distributed systems and real-time decision-making scenarios.