PhD Final Oral Defense: Swati Rallapalli, GDC 6.816

Contact Name: 
Lydia Griffith
Date: 
Aug 22, 2014 11:00am - 1:00pm

PhD Final Oral Defense:  Swati Rallapalli

Title: Mobile Localization: Approach and Applications
Committee: Lili Qiu (Supervisor), Donald Fussell, Simon Lam, Venkat Padmanabhan, Yin Zhang
Date: Aug. 22nd, 2014
Time: 11 am
Place: GDC 6.816

Abstract:

Localization is critical to a number of wireless network applications. Many of these applications demand location information in situations where GPS is not suitable. This dissertation (i) develops novel localization schemes for wireless networks by explicitly incorporating mobility information and (ii) applies localization to physical analytics i.e., tracking shoppers' behavior within retail stores by leveraging inertial sensors, Wi-Fi and vision enabled by smart glasses.
More specifically, we first focus on multi-hop mobile networks, analyze real mobility traces and observe that they exhibit temporal stability and low-rank structure. Motivated by these observations, we develop novel localization algorithms to effectively capture and also adapt to different degrees of these properties. Using extensive simulations and testbed experiments, we show that our new schemes significantly outperform state-of-the-art localization schemes under a wide range of scenarios and are robust to measurement errors.

Second, since mobile nodes that need to be localized may not always communicate with each other, i.e., no multi-hop information is available, we focus on single wireless nodes and propose using measurements from a trajectory as a fingerprint for localization. In particular, a mobile device measures the magnetic field or Wi-Fi signal strength along a trajectory while moving. We show that these measurements have the potential to uniquely identify the trajectory. We then develop a novel approach to identify which trajectory it is by matching its measurements with those in the training traces (trajectory matching) and localizing points on the trajectory (localization). Thus, by dividing the problem into two simpler tasks, we improve accuracy. We demonstrate that this approach is highly accurate and power efficient using indoor and outdoor experiments.

Finally, localization is a critical step in enabling a lot of applications --- an important one is physical analytics. Physical analytics has the potential to provide deep-insight into shoppers' interests and activities and therefore better advertisements, recommendations and a better shopping experience. To enable physical analytics, we build ThirdEye system which first achieves zero-effort localization by leveraging emergent devices like the Google-Glass to build AutoLayout that fuses video, Wi-Fi, and inertial sensor data, to simultaneously localize the shoppers while also constructing and updating the product layout in a virtual coordinate space. ThirdEye then leverages such inferences made from a small population of smart-glasses-enabled users to aid in tracking the physical browsing by the many smartphone-only users. Further, ThirdEye comprises of a range of schemes that use a combination of vision and inertial sensing to study mobile users' behavior while shopping, namely: walking, dwelling, gazing and reaching-out. We show the effectiveness of ThirdEye through an evaluation in two large retail stores in the United States.PhD Final Oral Defense:  Swati Rallapalli