Localization is a fundamental operation for many wireless networks. While GPS is widely
used for location determination, it is unavailable in many environments either due to its
high cost or the lack of line of sight to the satellites (e.g., indoors, under the ground, or
in a downtown canyon). The limitations of GPS have motivated researchers to develop
many localization schemes to infer locations based on measured wireless signals. However,
most of these existing schemes focus on localization in static wireless networks. As many
wireless networks are mobile (e.g., mobile sensor networks, disaster recovery networks, and
vehicular networks), we focus on localization in mobile networks in this thesis. 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:
1. Low Rank based Localization (LRL), which exploits the low-rank structure in mobility.
2. Temporal Stability based Localization (TSL), which leverages the temporal stability.
3. 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. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2010-08-1794 |
| Date | 20 December 2010 |
| Creators | Rallapalli, Swati |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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