Efficient Wi-Fi Sensing for IoT Forensics with Lossy Compression of CSI Data

TL;DR

This paper explores lossy compression techniques for Wi-Fi CSI data to enhance IoT forensic applications, balancing data reduction with sensing accuracy, and demonstrating the potential of simple and deep learning-based methods.

Contribution

It introduces and evaluates lossy compression methods, including PCA and deep learning, for Wi-Fi CSI data to improve efficiency in resource-constrained IoT forensic scenarios.

Findings

PCA-based techniques significantly reduce CSI data volume while maintaining classification accuracy.

Deep learning models achieve high compression ratios with minimal sensing performance loss.

Lossy compression enables more efficient storage and processing in IoT forensic applications.

Abstract

Wi-Fi sensing is an emerging technology that uses channel state information (CSI) from ambient Wi-Fi signals to monitor human activity without the need for dedicated sensors. Wi-Fi sensing does not only represent a pivotal technology in intelligent Internet of Things (IoT) systems, but it can also provide valuable insights in forensic investigations. However, the high dimensionality of CSI data presents major challenges for storage, transmission, and processing in resource-constrained IoT environments. In this paper, we investigate the impact of lossy compression on the accuracy of Wi-Fi sensing, evaluating both traditional techniques and a deep learning-based approach. Our results reveal that simple, interpretable techniques based on principal component analysis can significantly reduce the CSI data volume while preserving classification performance, making them highly suitable for lightweight IoT forensic scenarios. On the other hand, deep learning models exhibit higher potential in complex applications like activity recognition (achieving compression ratios up to 16000:1 with minimal impact on sensing performance) but require careful tuning and greater computational resources. By considering two different sensing applications, this work demonstrates the feasibility of integrating lossy compression schemes into Wi-Fi sensing pipelines to make intelligent IoT systems more efficient and improve the storage requirements in forensic applications.