  A noise map facilitates the monitoring of environmental noise pollution in
urban areas. However, state-of-the-art techniques for rendering noise maps in
urban areas are expensive and rarely updated, as they rely on population and
traffic models rather than on real data. Smart phone based urban sensing can be
leveraged to create an open and inexpensive platform for rendering up-to- date
noise maps. In this paper, we present the design, implementation and
performance evaluation of an end-to-end, context-aware, noise mapping system
called Ear-Phone. Ear-Phone investigates the use of different interpolation and
regularization methods to address the fundamental problem of recovering the
noise map from incomplete and random samples obtained by crowdsourcing data
collection. Ear-Phone, implemented on Nokia N95, N97 and HP iPAQ, HTC One
mobile devices, also addresses the challenge of collecting accurate noise
pollution readings at a mobile device. A major challenge of using smart phones
as sensors is that even at the same location, the sensor reading may vary
depending on the phone orientation and user context (for example, whether the
user is carrying the phone in a bag or holding it in her palm). To address this
problem, Ear-Phone leverages context-aware sensing. We develop classifiers to
accurately determine the phone sensing context. Upon context discovery,
Ear-Phone automatically decides whether to sense or not. Ear-phone also
implements in-situ calibration which performs simple calibration that can be
carried out without any technical skills whatsoever required on the user's
part. Extensive simulations and outdoor experiments demonstrate that Ear-Phone
is a feasible platform to assess noise pollution, incurring reasonable system
resource consumption at mobile devices and providing high reconstruction
accuracy of the noise map.
