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      Awareness and Learning in Participatory Noise Sensing

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          Abstract

          The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.

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          Most cited references12

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          Robust dynamic classes revealed by measuring the response function of a social system

          We study the relaxation response of a social system after endogenous and exogenous bursts of activity using the time series of daily views for nearly 5 million videos on YouTube. We find that most activity can be described accurately as a Poisson process. However, we also find hundreds of thousands of examples in which a burst of activity is followed by an ubiquitous power-law relaxation governing the timing of views. We find that these relaxation exponents cluster into three distinct classes and allow for the classification of collective human dynamics. This is consistent with an epidemic model on a social network containing two ingredients: a power-law distribution of waiting times between cause and action and an epidemic cascade of actions becoming the cause of future actions. This model is a conceptual extension of the fluctuation-dissipation theorem to social systems [Ruelle, D (2004) Phys Today 57:48-53] and [Roehner BM, et al., (2004) Int J Mod Phys C 15:809-834], and provides a unique framework for the investigation of timing in complex systems.
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            Noise exposure and public health.

            Exposure to noise constitutes a health risk. There is sufficient scientific evidence that noise exposure can induce hearing impairment, hypertension and ischemic heart disease, annoyance, sleep disturbance, and decreased school performance. For other effects such as changes in the immune system and birth defects, the evidence is limited. Most public health impacts of noise were already identified in the 1960s and noise abatement is less of a scientific but primarily a policy problem. A subject for further research is the elucidation of the mechanisms underlying noise-induced cardiovascular disorders and the relationship of noise with annoyance and nonacoustical factors modifying health outcomes. A high priority study subject is the effects of noise on children, including cognitive effects and their reversibility. Noise exposure is on the increase, especially in the general living environment, both in industrialized nations and in developing world regions. This implies that in the twenty-first century noise exposure will still be a major public health problem. Images Figure 2
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              Urban sensing

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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                11 December 2013
                : 8
                : 12
                : e81638
                Affiliations
                [1 ]Complex Networks and Systems Lagrange Laboratory, Institute for Scientific Interchange Foundation, Turin, Italy
                [2 ]Extreme Citizen Science Research Group, Department of Civil, Environmental and Geomatic Engineering, University College London, London, United Kingdom
                [3 ]Department for Artificial Intelligence and Applied Computer Science, University of Würzburg, Würzburg, Germany
                [4 ]Physics Department, Sapienza University, Rome, Italy
                [5 ]CSP Innovazione nelle ICT, Turin, Italy
                [6 ]Department of Electrical Engineering/Computer Science, University of Kassel, Kassel, Germany
                [7 ]L3S Research Center, Leibniz Universität, Hannover, Germany
                US Naval Reseach Laboratory, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: MB SC DF LF PG MH AH VL JM FR VDPS AS FT. Analyzed the data: MB PG AH VL VDPS AS FT. Wrote the paper: MB SC DF LF PG MH AH VL JM FR VDPS AS FT. App implementation and maintenance: MB DF AH JM FR. Web platform implementation and maintenance: MB SC PG AH VL JM VDPS FT. Participant recruiting activities: SC LF PG MH VL AS VDPS FT. Data analysis: MB PG AH VL VDPS AS FT.

                Article
                PONE-D-13-35375
                10.1371/journal.pone.0081638
                3859489
                24349102
                711079eb-0ac9-4822-b2b8-8a3f51d4e9ef
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 August 2013
                : 15 October 2013
                Page count
                Pages: 12
                Funding
                This research has been supported by the EveryAware project funded by the Future and Emerging Tech-nologies program of the European Commission under the EU RD contract IST-265432. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Research Article

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