SoundOfTheCity - Continuous noise monitoring for a healthy city

Ruge, Lukas; Altakrouri, Bashar; Schräder, Andreas

doi:10.1109/percomw.2013.6529577

Cited by 42 publications

(31 citation statements)

References 10 publications

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“…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). SoundOfTheCity [Ruge et al 2013] allowed users to link their feelings and experiences with the measured noise level, for example, is it in a party or in a crowded street.…”

Section: Crowd-powered Environment Monitoringmentioning

confidence: 99%

Mobile Crowd Sensing and Computing

et al. 2015

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With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and largescale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human-machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC. . 2015. Mobile crowd sensing and computing: The review of an emerging human-powered sensing paradigm.

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Section: Crowd-powered Environment Monitoringmentioning

confidence: 99%

Mobile Crowd Sensing and Computing

et al. 2015

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“…The application, implemented by the European Environment Agency (EEA), adds a new layer to the Eye on Earth¹⁶ online project that collects and shares environmental data as information concerning water, air, climate change, biodiversity and land use. SoundOfTheCity¹⁷ rests on a continuous, contextaware and unobtrusive participatory sensing approach for measuring noise levels and for monitoring the community exposure under the context of a healthy city [62]. By default, one-second noise measurements are performed every 30 s. The anonymised measured data are sent to a cen- tral server that aggregates and generates noise maps.…”

Section: Existing Tools Devoted To Noise Harnessingmentioning

confidence: 99%

“…and energy awareness [42,70,71]. A list of relevant criteria inspired from [62] are pointed in Table 1 for the major smartphone applications. Note that a few features are not necessarily addressed in a similar way (e.g.…”

Section: Existing Tools Devoted To Noise Harnessingmentioning

confidence: 99%

Noise mapping based on participative measurements

Guillaume¹,

Can

Petit

et al. 2016

Noise Mapping

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International audienceThe high temporal and spatial granularities recommended by the European regulation for the purpose of environmental noise mapping leads to consider new alternatives to simulations for reaching such information. While more and more European cities deploy urban environmental observatories, the ceaseless rising number of citizens equipped with both a geographical positioning system and environmental sensors through their smartphones legitimates the design of outsourced systems that promote citizen participatory sensing. In this context, the OnoM@p system aims at offering a framework for capitalizing on crowd noise data recorded by inexperienced individuals by means of an especially designed mobile phone application. The system fully rests upon open source tools and interoperability standards defined by the Open Geospatial Consortium. Moreover, the implementation of the Spatial Data Infrastructure principle enables to break up as services the various business modules for acquiring, analysing and mapping sound levels. The proposed architecture rests on outsourced processes able to filter outlier sensors and untrustworthy data, to cross- reference geolocalised noise measurements with both geographical and statistical data in order to provide higherlevel indicators, and to map the collected and processed data based on web services

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“…Widespread availability of cheap sensing devices, especially when provided as on-board components built into a wide range of systems (e.g., smartphones, indash units, body sensor networks...), as well as advances in pervasive computing techniques, have turned the vision about smart environments from a theoretical research topic into a feasible plan. On the top of such heterogeneous infrastructure several application scenarios may be enabled, ranging from traffic management [1] to air pollution monitoring [2], from homeland security [3] to noise monitoring [4]. From a developer viewpoint, device variety, highly dynamic nature of the setting, and huge datasets to be processed still factor in as hurdles in making powerful applications available to the public at large.…”

Section: Introductionmentioning

confidence: 99%

An Architecture for Runtime Customization of Smart Devices

Fazio

Merlino

Bruneo

et al. 2013

2013 IEEE 12th International Symposium on Network Computing and Applications

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Smart environments represent a relatively uncharted ICT territory where plenty of sensor and actuator devices can be enrolled on-demand in order to realize high value-added services. A few application scenarios, such as Smart Cities, have already been explored. However, in order to finally enable such a paradigm, several issues have to be dealt with. In particular, from a developer perspective the high degree of heterogeneity for devices (ranging from cheap sensors to smartphones) could represent a hurdle for software design. In this paper, we present an innovative architecture that aims at providing a common reference platform for repurposing of devices i.e, reshaping their operational behavior for emergent and unforeseen requirements. Thanks to its modular and plugin based design, the proposed architecture is poised to ease implementation of both low-level (e.g., device discovery, code compilation, binary deployment) and high-level (e.g., service composition, data management) duties. We present the general architecture, then focusing on device-side aspects, while also providing two simple use cases that demonstrate the suitability of the proposed approach.

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SoundOfTheCity - Continuous noise monitoring for a healthy city

Cited by 42 publications

References 10 publications

Mobile Crowd Sensing and Computing

Mobile Crowd Sensing and Computing

Noise mapping based on participative measurements

An Architecture for Runtime Customization of Smart Devices

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