Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning

Accurately locating users in a wireless environment is an important task for many pervasive computing and AI applications, such as activity recognition. In a WiFi environment, a mobile device can be localized using signals received from various transmitters, such as access points (APs). Most localization approaches build a map between the signal space and the physical location space in a offline phase, and then using the received-signal-strength (RSS) map to estimate the location in an online phase. However, the map can be outdated when the signal-strength values change with time due to environmental dynamics. It is infeasible or expensive to repeat data calibration for reconstructing the RSS map. In such a case, it is important to adapt the model learnt in one time period to another time period without too much recalibration. In this paper, we present a location-estimation approach based on Manifold co-Regularization, which is a machine learning technique for building a mapping func...

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: Sinno Jialin Pan, James T. Kwok, Qiang Yang, Jeffrey Junfeng Pan
• Keywords: AAAI 2007, Intelligent Agents, Physical Location Space, Time Periods, WiFi Environment