Optimising for Dense Deployments in Commercial Ambient Human Sensing with WiFi CSI

WiFi Channel State Information (CSI) is widely-used in research for human sensing applications, yet its actual deployment in commercial real-time applications remains sparse with few examples. Existing demonstrations in research literature predominantly rely on specialised deployments of a single sensing apparatus, which cannot efficiently be used in large-scale deployments. Additionally packet loss is common which leads to an over-reliance on interpolation for missing points. Addressing these gaps, this paper presents a low-cost, and scalable solution for CSI-based human sensing, tailored for high performance and consistent operation in residential environments.Our approach leverages ESP32 hardware which is renowned for its high availability and low-cost compared to popular CSI collection solutions. We define a methodology for remotely collecting CSI data from multiple sensors concurrently over WiFi, by employing a single beacon for traffic generation while CSI data is gathered over a separate channel. We further optimize this process using DEFLATE compression on CSI payloads to minimize airtime contention during transmission. This proposed system has been evaluated through a series of experiments designed to assess its viability, scalability, and environmental adaptation capability. Notably, we demonstrate the system’s capability to support 30 sensors sampling CSI data at over 90Hz simultaneously, with additional projected capacity. This validation has been conducted across two distinct residential environments, affirming the adaptability and effectiveness of our approach for high-performance CSI sensing in real-world scenarios.