  We present \emph{SmartLoc}, a localization system to estimate the location
and the traveling distance by leveraging the lower-power inertial sensors
embedded in smartphones as a supplementary to GPS. To minimize the negative
impact of sensor noises, \emph{SmartLoc} exploits the intermittent strong GPS
signals and uses the linear regression to build a prediction model which is
based on the trace estimated from inertial sensors and the one computed from
the GPS. Furthermore, we utilize landmarks (e.g., bridge, traffic lights)
detected automatically and special driving patterns (e.g., turning, uphill, and
downhill) from inertial sensory data to improve the localization accuracy when
the GPS signal is weak. Our evaluations of \emph{SmartLoc} in the city
demonstrates its technique viability and significant localization accuracy
improvement compared with GPS and other approaches: the error is approximately
20m for 90% of time while the known mean error of GPS is 42.22m.
