Di Wu
ABSTRACT
In this paper, we present CrowdWiFi, a novel vehicular middleware to identify and localize roadside WiFi APs that are located outside or inside buildings. Our work is motivated by the recent surge in availability of open WiFi access points (APs) that are enabling opportunistic data services to moving vehicles. Two key elements of CrowdWiFi that provide vehicles with opportunistic WiFi access include (a) an online compressive sensing component and (b) an offline crowdsourcing module. Online compressive sensing (CS) techniques are primarily used to for the coarse-grained estimation of nearby APs along the driving route; here, the received signal strength (RSS) values are recorded at runtime, and the number and locations of APs are recovered immediately based on limited RSS readings. The offline crowdsourcing mechanism assigns the online CS tasks to crowd-vehicles and aggregates answers using a bipartite graphical model. This offline crowdsourcing executes at a crowd-server that iteratively infers the reliability of each crowd-vehicle from the aggregated sensing results and refines the estimation of APs using weighted centroid processing. Extensive simulation results and real testbed experiments confirm that CrowdWiFi can successfully reduce the number of measurements needed for AP recovery, while maintaining satisfactory counting and localization accuracy. In addition, the impact of CrowdWiFi middleware on WiFi handoff and data transmission applications is examined.
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Index Terms
- CrowdWiFi: efficient crowdsensing of roadside WiFi networks
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