Feature Extraction in Wireless Personal and Local Area Networks

Context awareness is currently being investigated for applications in different areas, including Mobile Computing. Many mobile devices are already shipped with support for Bluetooth and Wireless LAN, making these technologies commonly available. It is thus possible to exploit the wireless interfaces as sensors for deriving information about the device/user context. However, extracting features from typical Bluetooth or Wireless LAN properties is difficult because not only numerical, but also non-numerical features like the list of MAC addresses in range are important for context awareness. In this paper, we introduce a method to automatically classify these highly heterogeneous features with supervised or un-supervised classification methods. By defining two operators, a distance metric and an adaption operator, any feature can be used as input for the classifier and can thus contribute to context detection.