Wi-Fi Sensing is becoming a prominent field with a wide range of potential applications. Using existing hardware on a wireless network such as access points, cell phones, and smart home devices, important information can be inferred about the current physical environment. Through the analysis of Channel State Information collected in the Neighborhood Discovery Protocol process, the wireless network can detect disturbances in Wi-Fi signals when the physical environment changes. This results in a system that can sense motion within the Wi-Fi network, allowing for movement detection without any wearable devices.
The goal of this thesis is to answer whether Wi-Fi Sensing can enable useful applications at the enterprise level. The main applications we will focus on are presence detection and in-zone movement detection. Our contributions include: 1. A scalable, statistical analysis system that generates a heatmap and detects movement in a 12 x 9 meter zone with 98 percent accuracy, as well as a 6 x 9 meter zone with 88 percent accuracy. 2. A broad dataset collected for evaluation in an enterprise setting. 3. An end-to-end CSI data visualization and analysis application.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3973 |
| Date | 01 December 2021 |
| Creators | Schnorr, Nicholas P |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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