Ear-phone: an end-to-end participatory urban noise mapping system

By Rajib Kumar Rana, Chun Tung Chou, Salil S. Kanhere, Nirupama Bulusu, Wen Hu. International Symposium on Information Processing in Sensor Networks, 2010

775 citations recorded by the source corpus.

Subfields: Ocean Engineering, Speech and Hearing, Transportation

View Paper: https://www.semanticscholar.org/paper/99dc3568a9eed97868098ddadd085e501dd91cbe

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In Ocean Engineering

In Speech and Hearing

Salil S. Kanhere

In Transportation

Citations in the corpus

Internet of Things (IoT): A vision, architectural elements, and future directions (2012, methodology): “levels using battery powered nodes using fixed infrastructure and participatory sensing [50] as a key component for health and quality of life services for its inhabitants.”
Participatory Sensing: Crowdsourcing Data from Mobile Smartphones in Urban Spaces (2011, methodology): “In EarPhone [9], the noise levels are used to monitor noise pollution, which can e.g. affect human hearing and behavior, and build representative pollution maps accessible to either specialists to understand e.g. the relationships between noise exposition and behavioral problems for the general publ”
Mobile Crowd Sensing and Computing (2015, background): “EarPhone [Rana et al. 2010] was also a participatory noise mapping system that also used mobile phones to determine environmental noise levels (particularly roadside ambient noise).”
TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing (2014, background): “Although a number of mobile crowdsourcing applications [5] [6] [7] [8] have been proposed, most of them have assumed that smartphones voluntarily contribute to the mobile crowdsourcing system, which is not true in the real world.”
Crowdsourcing with Smartphones (2012, background): “The latter class includes applications for crowdsourced traffic monitoring (e.g., Waze 3 ) and road traffic delay estimation (VTrack [2]); constructing finegrained noise maps by letting users upload data captured by their smartphone microphone (Ear-Phone [3], NoiseTube [4]); identifying holes in str”
Distributed Social Welfare Maximization in Urban Vehicular Participatory Sensing Systems (2018, background): “Other applications are indoor localization [5], [6], environmental monitoring [7], [8], and crowd counting [9].”
A survey on privacy in mobile participatory sensing applications (2011, neutral): “The cameras on mobile phones can be used as video and image sensors.”
How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint (2014, background): “…collect data, enabling numerous mobile crowdsourced sensing (MCS) applications, such as Sensorly [2] for constructing cellular/WiFi network coverage maps, SignalGuru [3], Nericell [4] and VTrack [5] for providing trafﬁc information, Ear-Phone [6] and Noise-Tube [7] for making noise maps.”
Diagnosing New York city's noises with ubiquitous data (2014, background): “[16] proposed to recover a noise map from incomplete and random samples based on compressive sensing.”
Mobile Phone Sensing Systems: A Survey (2013, background): “By affixing a sensory device to a mobile phone, mobile sensing provides the opportunity to track dynamic information about environmental impacts and develop maps and understand patterns of human movement, traffic, and air pollution [2].”
A survey: Internet of Things (IOT) technologies, applications and challenges (2016, background): “This can be by hovering your mobile phone over the tag within its reading range so that the additional information about the marker can be displayed on the screen [21].”