DroneSSL: Self-Supervised Multimodal Anomaly Detection in Internet of Drone Things

In this study, we introduce a pioneering framework, DroneSSL, that integrates the concept of spatial crowdsourcing with TinyML to enhance anomaly detection in the Internet of Drone Things (IoDT). This innovative approach leverages drones and unmanned ground vehicles (UGVs) for expansive data collection in environments that are typically inaccessible or hazardous, such as during Australian bushfire incidents. By employing lightweight machine learning models alongside advanced communication technologies, DroneSSL transcends traditional spatial-temporal data analysis methods. It efficiently processes multimodal data from diverse Points-of-Interest (PoIs), significantly improving the quality and speed of data collection and analysis. The framework's integration of a temporal feature extraction module with a Graph Neural Network (GNN) and its adaptable, scalable GNN architecture tailor DroneSSL for real-time operations in resource-constrained IoDT environments. Achieving an 89.6% F1 score, DroneSSL marks a substantial 4.9% improvement over existing approaches, highlighting its effectiveness in critical applications such as environmental surveillance and emergency response. This advancement not only showcases the potential of combining TinyML with spatial crowdsourcing for IoDT but also sets a new standard for efficient, scalable anomaly detection, paving the way for future innovations in IoT edge devices and environmental monitoring systems.