Noisemap: Discussing Scalability in Participatory Sensing

Authors:
Christian Meurisch;Karsten Planz;Daniel Schäfer;Immanuel Schweizer
Affiliations:
Technische Universität Darmstadt, Germany;Technische Universität Darmstadt, Germany;Technische Universität Darmstadt, Germany;Technische Universität Darmstadt, Germany
Venue:
Proceedings of First International Workshop on Sensing and Big Data Mining
Year:
2013

Citing 6
Cited 0

Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype

Proceedings of the 4th workshop on Embedded networked sensors
Participatory noise pollution monitoring using mobile phones

Information Polity - Government 2.0: Making Connections between citizens, data and government
Joint bluetooth/wifi scanning framework for characterizing and leveraging people movement in university campus

Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
Exploring micro-incentive strategies for participant compensation in high-burden studies

Proceedings of the 13th international conference on Ubiquitous computing
A survey on privacy in mobile participatory sensing applications

Journal of Systems and Software
Noisemap: multi-tier incentive mechanisms for participative urban sensing

Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones

Quantified Score

Hi-index	0.00

Visualization

Abstract

Environmental pollutants are an ever increasing problem in dense urban environments. To assess the effect of these pollutants, an unprecedented density of data is needed for large areas (cities, states, countries). In the past, participatory sensing has been proposed as a mean to acquire large sets of data. Since the smartphone is ubiquitous, scalability seems to be no problem anymore. In reality this far from the truth. Measuring their environment, people need to invest their time. For Android and iOS the application needs to compete with more than 700,000 other applications. Measuring large amounts of data is only possible, if we can attract large amounts of casual users. Since 2011, we have been working with and on Noisemap. Noisemap is one of many applications that uses the microphone to measure sound pressure. It then uploads the captured data to our backend, where the data is processed and visualized. Noisemap is officially available since February 2012, has been downloaded over 2,500 times, and has more than 1,000 registered users, which have collected over 500,000 unique data points in 39 countries and 58 cities. We want to share the current state of Noisemap as a multi-platform tool on Android and iOS, as well as our experience in scaling the application.