Deep Learning-Based Multiclass Anomaly Detection in the IoT_GPS_Tracker Dataset: Unveiling Patterns for Enhanced Tracking Accuracy

This paper delves into the utilization of deep learning (DL) techniques for the task of multiclass anomaly detection within the "IoT_GPS_Tracker" dataset, resulting in an impressive overall accuracy of 94.72%. With a primary focus on discerning anomalies across diverse scenarios, our model employs a range of DL architectures to capture nuanced spatial-temporal relationships inherent in the data. Addressing challenges posed by the dynamic nature of IoT-generated information, the study not only attains high accuracy in anomaly detection but also contributes to a deeper comprehension of spatial dependencies. The implications extend to bolstering the accuracy of locationbased services, thereby facilitating improved anomaly recognition and decision-making. The investigation encompasses an in-depth analysis of various DL architectures, an exploration of spatial-temporal representations, and an assessment of real-world implications. This work offers valuable insights to the evolving realm of internet of things (IoT) analytics, laying the groundwork for more intelligent and adaptive multiclass anomaly detection systems within the IoT_GPS_Tracker dataset. The achieved overall accuracy of 94.72% underscores the effectiveness of our DL approach in elevating anomaly detection within the context of multiclass classification.