Author(s)

Yang Fan

Affiliation(s)

Department of Software Engineering, Fudan University, China.

Corresponding Author

Yang Fan

ABSTRACT

This paper presents a comprehensive exploration of the integration of adaptive sharding and Artificial Intelligence to significantly enhance the performance of Unmanned Aerial Vehicle networks. As UAV technology evolves from predominantly military applications to a diverse range of civilian uses—including agriculture, logistics, surveillance, and disaster response—maximizing operational efficiency becomes increasingly critical. The study identifies several key challenges that hinder the effectiveness of UAV networks, such as limited battery life, constraints on communication bandwidth, and processing power limitations. These constraints directly impact the operational range, data transmission efficiency, and real-time decision-making capabilities of UAVs.

To address these challenges, we propose an innovative framework that employs adaptive sharding, which dynamically allocates tasks among multiple UAVs based on their real-time capabilities and environmental conditions. This approach not only optimizes resource management but also enhances the network's overall resilience. Furthermore, we leverage advanced AI techniques, particularly deep reinforcement learning, to improve decision-making processes and enhance the adaptability of UAVs in task allocation. By analyzing historical data and real-time conditions, the UAV network can proactively adjust its operations, thereby mitigating potential challenges and improving overall performance.

The research evaluates various performance metrics, including latency, throughput, energy efficiency, and reliability, to assess the effectiveness of the adaptive sharding framework. Empirical results demonstrate that the combination of adaptive sharding and AI significantly improves UAV network performance, leading to reduced operational costs and enhanced mission success rates. The findings underscore the transformative potential of integrating adaptive systems and intelligent algorithms in UAV networks, paving the way for more resilient and efficient aerial operations that can meet the growing demands of modern applications across various sectors.

KEYWORDS

Unmanned Aerial Vehicles; Adaptive sharding; Artificial intelligence

CITE THIS PAPER

Yang Fan. Improving UAV network performance with adaptive sharding and AI. World Journal of Mathematics and Physics. 2024, 2(2): 66-73. DOI: https://doi.org/10.61784/wjmp3010.

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