  Detection and classification of vehicles are very significant components in
an Intelligent-Transportation System. Existing solutions not only use
heavy-weight and costly equipment, but also largely depend on constant cloud
(Internet) connectivity, as well as adequate uninterrupted power-supply. Such
dependencies make these solutions fundamentally impractical considering the
possible adversities of outdoor environment as well as requirement of
correlated wide-area operation. For practical use, apart from being technically
sound and accurate, a solution has to be lightweight, cost-effective,
easy-to-install, flexible as well as supporting efficient time-correlated
coverage over large area. In this work we propose an IoT-assisted strategy to
fulfil all these goals together. We adopt a top-down approach where we first
introduce a lightweight framework for time-correlated low-cost wide-area
measurement and then reuse the concept for developing the individual
measurement units. Our extensive outdoor measurement studies and trace-based
simulation on the empirical data show about 98% accuracy in vehicle detection
and upto 93% of accuracy in classification of the vehicles over moderately busy
urban roads.
