Efficient Data Management Mechanism for Evaluating Uncrewed Aerial Vehicles in Internet of Things

Abstract

Uncrewed aerial vehicles (UAVs) are playing an increasingly critical role in Internet of Things (IoT), and with AI advancements, they are evolving into intelligent, multifunctional platforms supporting crowdsensing applications in modern IoT ecosystems. Given the dynamic nature of UAV-based IoT services, it is crucial to evaluate whether UAVs meet essential performance characteristics like flexibility, robustness, and stability. Flight data of UAVs is fundamental for assessing these characteristics, and effective data management is key to enabling timely, accurate performance evaluations. In this article, we propose a hierarchical tree model (HTM) specifically designed to accommodate the characteristics of UAVs-generated data. This model is supported by a hierarchical tree-based storage structure that optimizes the organization and retrieval of time-series data. To enable characteristic evaluation, we use tags to store characteristic information. To enable characteristic evaluation, we use tags to store characteristic information and apply encoding/decoding algorithms for flexible operation. We extend the SQL syntax tree of IoTDB with new syntax and semantics, enhancing the IoTDB parser and optimizer for tag integration and data operation. Experimental results show that our framework enables more efficient real-time evaluation of UAVs performance and better scalability for large deployments. Our findings highlight the potential of this time-series data management approach to support the real-time evaluation of UAVs characteristics, facilitating more informed decision-making and resource allocation in IoT-driven environments.