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Abstract:
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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 . |