Ear-Phone: A Context-Aware Noise Mapping using Smart Phones

TL;DR

Ear-Phone is a novel, cost-effective, context-aware mobile sensing system that creates up-to-date urban noise maps by leveraging crowdsourced data and advanced interpolation techniques.

Contribution

The paper introduces Ear-Phone, an end-to-end noise mapping system that uses smartphones for real-time data collection, context-aware sensing, and in-situ calibration to improve accuracy and update frequency.

Findings

High reconstruction accuracy of noise maps achieved.

Feasible real-time noise monitoring on mobile devices.

Effective context-aware sensing reduces data variability.

Abstract

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.