- Spring 2019-present
This project is a part of the Sensory Network Infrastructure for Scientific Laboratory Environments (SENSELET) project, which is an NSF-funded project.
Indoor localization based on Wi-Fi fingerprints has been an active research topic for years. However, existing approaches do not con- sider the instability of access points (APs) which may be unreliable in practice, particularly the ones deployed by individual users. This instability impacts the localization accuracy severely, due to the un- reliable or even wrong Wi-Fi fingerprints. Ideally, the localization should be done using only the well-deployed APs (e.g., deployed by facility teams). However, in many places the number of these APs is too few to achieve a good localization accuracy. To solve this problem, we leverage emerging smart APs equipped with multi- mode antennas, and build a new indoor localization system to reduce the number of necessary APs. The key idea is controlling the modes of AP antennas to generate more fingerprints with fewer APs. A clustering based localization strategy is designed to enable a mobile terminal to figure out the RSSI (Received Signal Strength Indicator) for different antenna modes without requiring any synchronization. We have implemented a prototype system using smart APs and commercial smartphones. The current experimental results demonstrate that our system can reduce the number of necessary APs by 50%, and achieve the same or even better localization accuracy.
- Tuo Yu, Wenyu Ren, Klara Nahrstedt, “MMLOC: Multi-Mode Indoor Localization System Based on Smart Access Points”, 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), November 12–14, 2019.
This project is supported by a collaborative award from the National Science Foundation (NSF award numbers OAC-1827126 and OAC-1659293).