Edge-Enabled Digital Twin of an IoT Drone: Real-Time Monitoring and Predictive Intelligence

Introduction/Objective: The integration of IoT, edge intelligence and Digital Twin (DT) technologies within Unmanned Aerial Vehicle (UAV) operations has the potential to revolutionize missions through real-time monitoring, predictive analysis and proactive decision making. In this paper, a patent edge-enabled DT framework is proposed for IoT-based UAVs and experimentally verified by the DJI Tello drone. Methods: Telemetry streams are received by a laptop acting as an edge device where DT mirrors drone states and predicts trajectory, as well as detects anomalies. As opposed to cloud-based DTs, the edge deployment makes a tradeoff between low-latency synchronization and commodity-- grade hardware operability. Results: The experimental results show that DT hosted on edge reduces the latency of telemetry by more than 75% comparing to a baseline running in the cloud, realizes trajectory prediction accuracy at MAE of 0.01 m. Anomaly detection experiments also demonstrate that the implemented methodology is able to detect motion spikes, slow drifts, packet loss, or drastic depletions with an F1-score of 0.89 and delays in the detection of lower than 0.1 seconds. Discussion: The integration of AI-enhanced modules is explored high level for trajectory forecasting using LSTM networks and autoencoders for anomaly detection. The DT matures into an intelligent Digital Twin (iDT). These AI–based extensions enhance predictive accuracy and detection sensitivity, making a proactive, pre-emptive AI-based security infrastructure. Conclusion: The results illustrate the feasibility and efficiency of patent edge-based UAV iDTs as predictive tools for early warning systems.