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

2025-05-06

AI Summary

High-dimensional Wi-Fi channel state information (CSI) data in resource-constrained IoT—particularly digital forensics scenarios—imposes severe bottlenecks in storage, transmission, and on-device processing. Method: This work systematically investigates the impact of lossy compression on activity recognition accuracy, comparatively evaluating interpretable lightweight methods (e.g., PCA) against deep autoencoders under forensic constraints. It identifies critical mechanisms enabling robust recognition even at extreme compression ratios (up to 16,000:1) and introduces a “compression–sensing co-design” paradigm. Contribution/Results: PCA achieves >95% data reduction with <2% accuracy degradation; deep models sustain fine-grained activity recognition with only a 1.3% accuracy drop at 16,000:1 compression. The framework significantly enhances edge deployment feasibility and long-term forensic data storage efficiency while preserving analytical fidelity.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Data Compression
• Computer Vision: Video Understanding & Activity Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

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.

Problem

• Reducing CSI data storage in resource-constrained IoT environments
• Evaluating lossy compression impact on Wi-Fi sensing accuracy
• Balancing compression efficiency and computational resource demands

Innovation

• Uses lossy compression for CSI data reduction
• Compares PCA and deep learning compression techniques
• Optimizes storage for IoT forensic applications