The Raspberry Pi is a low-cost, credit-card-sized single-board computer that has been widely adopted in Wi-Fi/CSI sensing research as a compact and affordable platform for deploying wireless sensing systems in real-world environments. Its significance to the field lies in its ability to run Wi-Fi interface drivers capable of extracting Channel State Information (CSI), enabling researchers to build practical, deployable sensing nodes without relying on expensive dedicated hardware. Common variants used in sensing research include the Raspberry Pi 3 and Raspberry Pi 4, which differ in processing power and wireless chipset support, with compatibility depending on the specific CSI extraction tool or firmware patch being employed.
Source Papers
- A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels ↗ — A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Cha
- A Survey on Green Wireless Sensing: Energy-Efficient Sensing via WiFi CSI and Lightweight Learning ↗ — A Survey on Green Wireless Sensing: Energy-Efficient Sensing
- A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects ↗ — A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techni
- Crowd Counting Through Walls Using WiFi ↗ — Crowd Counting Through Walls Using WiFi
- Deep Learning-Enhanced Human Sensing with Channel State Information: A Survey ↗ — Deep Learning-Enhanced Human Sensing with Channel State Info
- Estimating indoor crowd density and movement behavior using WiFi sensing ↗ — Estimating indoor crowd density and movement behavior using
- Passive WiFi Radar for Human Sensing Using a Stand-Alone Access Point ↗ — Passive WiFi Radar for Human Sensing Using a Stand-Alone Acc
- SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing ↗ — SenseFi: A library and benchmark on deep-learning-empowered