Poster: detecting client mobility in WLANs using PHY layer information

The continuously increasing number of smartphones and tablets allow the users to access Wireless LANs (WLANs) while undergoing different types of mobility, posing new challenges to wireless protocols. Current history-based WLAN protocols do not work well in mobile settings where wireless conditions change rapidly. Thus, today's WLANs need to be able to determine the type of the client's mobility and employ appropriate strategies in order to sustain high performance. While previous work tried to detect mobility using hints from sensors available in mobile devices, in this work, we demonstrate how different mobility modes can be distinguished by using physical layer information - Channel State Information (CSI) and Time-of-Flight (ToF) - available at commodity APs, with no modifications on the client side. Our testbed experiments show that our mobility classification algorithm achieves more than 92% accuracy in a variety of scenarios. In addition, we demonstrate how fine-grained mobility determination can be exploited to greatly improve performance of client roaming and MIMO beamforming.