Ear-phone: an end-to-end participatory urban noise mapping system
12 Apr 2010-pp 105-116
TL;DR: Ear-Phone, for the first time, leverages Compressive Sensing to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection.
Abstract: A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory 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 participatory urban noise mapping system called Ear-Phone. Ear-Phone, for the first time, leverages Compressive Sensing 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 and HP iPAQ mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. 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.
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TL;DR: In this article, the authors present a cloud centric vision for worldwide implementation of Internet of Things (IoT) and present a Cloud implementation using Aneka, which is based on interaction of private and public Clouds, and conclude their IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.
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Cites background or methods from "Ear-phone: an end-to-end participat..."
...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....
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...Importantly, this encompasses both fixed andmobile sensing infrastructure [50] aswell as continuous and random sampling....
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Abstract: Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.
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967 citations
References
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Proceedings Article•
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TL;DR: This paper overviews the recent work on compressive sensing, a new approach to data acquisition in which analog signals are digitized for processing not via uniform sampling but via measurements using more general, even random, test functions.
Abstract: This paper overviews the recent work on compressive sensing, a new approach to data acquisition in which analog signals are digitized for processing not via uniform sampling but via measurements using more general, even random, test functions. In stark contrast with conventional wisdom, the new theory asserts that one can combine "low-rate sampling" with digital computational power for efficient and accurate signal acquisition. Compressive sensing systems directly translate analog data into a compressed digital form; all we need to do is "decompress" the measured data through an optimization on a digital computer. The implications of compressive sensing are promising for many applications and enable the design of new kinds of analog-to-digital converters, cameras, and imaging systems.
1,537 citations
28 May 2009
TL;DR: A new approach for the assessment of noise pollution involving the general public is presented, to turn GPS-equipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment.
Abstract: In this paper we present a new approach for the assessment of noise pollution involving the general public. The goal of this project is to turn GPS-equipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment. Thus each user can contribute by sharing their geo-localised measurements and further personal annotation to produce a collective noise map.
404 citations
19 Apr 2006
TL;DR: This paper proposes a distributed matched source-channel communication scheme, based in part on recent results in compressive sampling theory, for estimation of sensed data at the fusion center and analyzes the trade-offs between power, distortion and latency.
Abstract: Compressive sampling is an emerging theory that is based on the fact that a relatively small number of random projections of a signal can contain most of its salient information. In this paper, we introduce the concept of compressive wireless sensing for sensor networks in which a fusion center retrieves signal field information from an ensemble of spatially distributed sensor nodes. Energy and bandwidth are scarce resources in sensor networks and the relevant metrics of interest in our context are 1) the latency involved in information retrieval; and 2) the associated power-distortion trade-off. It is generally recognized that given sufficient prior knowledge about the sensed data (e.g., statistical characterization, homogeneity etc.), there exist schemes that have very favorable power-distortion-latency trade-offs. We propose a distributed matched source-channel communication scheme, based in part on recent results in compressive sampling theory, for estimation of sensed data at the fusion center and analyze, as a function of number of sensor nodes, the trade-offs between power, distortion and latency. Compressive wireless sensing is a universal scheme in the sense that it requires no prior knowledge about the sensed data. This universality, however, comes at the cost of optimality (in terms of a less favorable power-distortion-latency trade-off) and we quantify this cost relative to the case when sufficient prior information about the sensed data is assumed.
351 citations
22 Apr 2008
TL;DR: In this article, the authors describe principles of community sensing that offer mechanisms for sharing data from privately held sensors, taking into account the likely availability of sensors, the context-sensitive value of sensor information, and sensor owners' preferences about privacy and resource usage.
Abstract: A great opportunity exists to fuse information from populations of privately-held sensors to create useful sensing applications. For example, GPS devices, embedded in cellphones and automobiles, might one day be employed as distributed networks of velocity sensors for traffic monitoring and routing. Unfortunately, privacy and resource considerations limit access to such data streams. We describe principles of community sensing that offer mechanisms for sharing data from privately held sensors. The methods take into account the likely availability of sensors, the context-sensitive value of sensor information, based on models of phenomena and demand, and sensor owners' preferences about privacy and resource usage. We present efficient and well-characterized approximations of optimal sensing policies. We provide details on key principles of community sensing and highlight their use within a case study for road traffic monitoring.
245 citations
"Ear-phone: an end-to-end participat..." refers methods in this paper
...We present the design and implementation of an end-to-end noise mapping system, called Ear-Phone, to generate the noise map of an area using participatory urban sensing....
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01 Jan 2006
TL;DR: The need for a new architecture to support people-centric sensing at Internet scale is motivated, the MetroSense architecture is outlined, and progress to date in designing and deploying prototype implementations of the Metro Sense architecture is highlighted via the deployment of the campus area sensor network.
Abstract: Looking forward 10-20 years we envision Internet scale sensing where the majority of the traffic on the network is sensor data and the majority of applications used every day by the general populace integrates sensing and actuation in some form. Sensing will be people-centric. On the other hand, nearly all published sensor network research over the last five years has focused on isola ted, small scale testbeds designed for specialized applications (e.g., environmental sensing, industrial sensing, etc.) of interest t o engineers and scientists. We believe the gap between the state of the art and our future vision can be bridged through the development of a new wireless sensor edge for the Internet. To this end, in the MetroSense Project we are developing a general purpose sensing infrastructure capable of realizing a wealth of sensing applications with mass appeal for producers and consumers of sensed data. In this paper we motivate the need for a new architecture to support people-centric sensing at Internet scale, outline our MetroSense architecture [1], and highlight our progress to date in designing and deploying prototype implementations of the MetroSense architecture via the deployment of our campus area sensor network.
154 citations
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