Autoencoders for Embedded Sensor Data Compression: A Case Study on Vehicular IoT Systems

The growing integration of sensors and embedded devices into distributed Internet of Things (IoT) systems has increased the demand for real-time data collection and processing solutions. However, the high volume and frequency of sensor data create challenges related to storage, transmission, and response latency, especially in resource-constrained environments. In this context, locally executed compression techniques, aligned with the Tiny Machine Learning (TinyML) paradigm, become differentiators for enabling embedded applications. Thus, this work proposes an autoencoder-based approach for efficiently compressing sensor data on edge devices. Three autoencoder variants (feed-forward, sparse, and contractive) are evaluated, combined with symmetric and asymmetric architectures, considering criteria such as compression ratio, information preservation, and embedded execution feasibility. For practical validation, a case study was conducted using vehicular data collected via the OBD-II interface, where the selected models were deployed on the OBDII Edge Freematics One+ device. The results show that the models could reduce data dimensionality with minimal information loss, maintain competitive performance on discriminative tasks, and exhibit inference times compatible with real-time applications. Autoencoders represent a viable neural compression solution for IoT environments, potentially applicable to various embedded sensing scenarios.