Open WiFi access points (APs) are demonstrating
that they can provide opportunistic data services to moving
vehicles. We present CrowdWiFi, a novel system to look
up roadside WiFi APs located outdoors or inside buildings.
CrowdWiFi consists of two components: online compressive
sensing (CS) and offline crowdsourcing. Online CS presents an
efficient framework for the coarse-grained estimation of nearby
APs along the driving route, where received signal strength (RSS)
values are recorded at runtime, and the number and location of
the APs are recovered immediately based on limited RSS readings
and adaptive CS operations. Offline crowdsourcing assigns the
online CS tasks to crowd-vehicles and aggregates answers on a
bipartite graphical model. Crowd-server also iteratively infers
the reliability of each crowd-vehicle from the aggregated sensing
results, and then refines the estimation of the APs using weighted
centroid processing. Extensive simulation results and real testbed
experiments confirm that CrowdWiFi can successfully reduce
the computation cost and energy consumption of roadside WiFi
lookup, while maintaining satisfactory localization accuracy.