iMap: A Crowdsensing Based System for Outdoor Radio Signal Strength Map

With the ubiquity of mobile appliances, efficient wireless access has become an urgent need for mobile users. Efficient wireless access requires accurate radio signal maps. Unfortunately, most of solutions could not be applied to large-scale urban space. Crowdsensing schemes could leverage the smartphone to sense WiFi signals, but could not provide accurate map because of the intolerable noises in mobile devices. Even worse, device heterogeneity makes the noise taming more intractable. Thus how to build accurate radio signal map with heterogeneous devices, especially in mobile crowdsensing is a major challenge. In this work, we build an accurate WiFi signal in geographical domain with iMap, a crowdsensing system leveraging heterogeneous smart devices to tame device noise ambiently in large-scale urban space. First of all, we deployed our system in typical urban area, and collect the real trace data with heterogeneous smart phones. After that, we make a synthetic analysis over the collected data, and find that, (1) although the noise is intolerable, it could be fitted to a statistical model fairly well, (2) although the noise model varies across smartphone types, the relative value between smart phones could be modeled. Leveraging this fundamental observation, we construct and implement an accurate RSS map with model-driven approach for taming heterogeneous noise, and geo-tagged measurements could be tamed for accurate RSS map. Experimental evaluations further validate our design.