Name
Abstract | ||
|---|---|---|
ABSTRACTWireless sensing and the Internet of Things support real-time monitoring and data-driven control of the built environment, enabling more sustainable and responsive infrastructure. As buildings and physical structures tend to be large and complex, instrumenting them to support a wide range of applications often requires numerous sensors distributed over a large area. One impediment to this type of large-scale sensing is simply tracking where exactly devices are over time, as the physical infrastructure is updated and interacted with over time. Having low-cost but accurate localization for devices (instead of users) would enable scalable IoT network management, but current localization approaches do not provide a suitable tradeoff in terms of cost, energy, and accuracy for low power devices in unknown environments. We propose UbiTrack, a low-cost indoor positioning system that enables accurate tracking for single antenna commodity WiFi devices, without the need for a complex antenna array. UbiTrack leverages two-way channel state information (CSI) across all WiFi channels to measure the distance between nodes, and uses a new probabilistic localization algorithm based on Bayesian estimation to locate each device. We demonstrate the system on commodity $4.00 ESP32 WiFi chips and realize 1-meter level position accuracy in an indoor environment. This approach provides localization for everyday IoT devices, enabling more scalable deployments and new IoT applications. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3486611.3486646 | SENSYS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenpeng Wang | 1 | 1 | 1.42 |
| Zetian Liu | 2 | 1 | 0.72 |
| Jiechao Gao | 3 | 17 | 3.89 |
| Nurani Saoda | 4 | 2 | 1.72 |
| Bradford Campbell | 5 | 3 | 4.21 |