PhD Proposal: Swati Rallapalli, GDC 6.816

Contact Name: 
Lydia Griffith
Dec 4, 2013 2:00pm - 4:00pm

PhD Proposal: Swati Rallapalli

Date: Dec. 4th, 2013
Time: 2 pm
Place: GDC 6.816
Supervisor: Prof. Lili Qiu

Title: Mobile Localization: Approach and Applications


Location information is critical to many wireless network
applications. Some of these applications demand location information in cases where
GPS is not suitable. While a lot of work has gone into developing alternative localization
schemes for such situations, most of them focus on static networks.
Recognizing that many wireless nodes are mobile, we develop novel
localization schemes for wireless mobile networks. We first focus on
single hop wireless networks and propose using measurements from a
trajectory as a fingerprint for localization. In particular, a mobile
device measures the magnetic field or channel state information (CSI)
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). The core
requirement of both the steps is an accurate and robust algorithm to
match two time-series that may contain significant noise and
perturbation due to differences in mobility, devices, and
environment. We address these issues by first performing multi-level
wavelet analysis of the measurements and applying our enhanced Dynamic
Time Warping (DTW) alignment to the wavelet coefficients. We
demonstrate that this approach is highly accurate and power efficient
using indoor and outdoor experiments.

Next we examine localization in multihop wireless networks. We analyze
real mobility traces and find that they exhibit temporal stability and
low-rank structure. Motivated by this observation, we develop three
novel localization schemes to accurately determine locations in mobile
networks: (i) Low Rank based Localization (LRL), which exploits the
low-rank structure in mobility, (ii) Temporal Stability based
Localization (TSL), which leverages the temporal stability, and (iii)
Temporal Stability and Low Rank based Localization (TSLRL), which
incorporates both the temporal stability and the low-rank
structure. These localization schemes are general and can leverage
either mere connectivity (i.e., range-free localization) or distance
estimation between neighbors (i.e., range-based localization).  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.

As on-going work, we are exploring physical analytics enabled by
localization. Analogous to online analytics, physical analytics has
the potential to provide deep insight into user interests, with
footsteps taking the place of a click-stream. We develop techniques to
track user movements indoor by establishing a consistent indoor
location coordinate system, identifying dwell points, which are likely to
be places of interest, and characterizing dwell points from real shoppers.