Citizen noise pollution monitoring

Trabajo presentado a la 10th Annual International Conference on Digital Government Research: Social Networks: Making Connections between Citizens, Data and Government, celebrada en Puebla (México) del 17 al 21 de mayo de 2009.In this paper we present a new approach to monitor noise pollution involving citizens and built upon the notions of participatory sensing and citizen science. We enable citizens to measure their personal exposure to noise in their everyday environment by using GPS-equipped mobile phones as noise sensors. The geo-localised measures and user-generated meta-data can be automatically sent and shared online with the public to contribute to the collective noise mapping of cities. Our prototype, called NoiseTube, can be found online.This work was partially supported by the EU under contract IST- 34721 (TAGora). The TAGora project is funded by the Future and Emerging Technologies program (IST-FET) of the European Commission. Matthias Stevens is a Research Assistant of the Fund for Scientific Research, Flanders (Aspirant van het Fonds Wetenschappelijk Onderzoek - Vlaanderen).Peer reviewe

Mobile Application for Noise Pollution Monitoring through Gamification Techniques

Full data coverage of urban environments is crucial to monitor the status of the area to detect, for instance, trends and detrimental environmental changes. Collecting observations related to environmental factors such as noise pollution in urban environments through classical approaches implies the deployment of Sensor Networks. The cost of deployment and maintenance of such infrastructure might be relatively high for local and regional governments. On the other hand recent mass-market mobile devices such as smartphones are full of sensors. For instance, it is possible to perform measurements of noise through its microphone. Therefore they become low-cost measuring devices that many citizens have in their pocket. In this paper we present an approach for gathering noise pollution data by using mobile applications. The applications are designed following gamification techniques to encourage users to participate using their personal smartphones. In this way the users are involved in taking and sharing noise pollution measurements in their cities that other stakeholders can use in their analysis and decision making processes

Environmental Noise Pollution Monitoring And Impacts On Human Health In Dehradun City, Uttarakhand, India

Noise pollution monitoring and environmental impacts on human health in Dehradun City of India are discussed. Major source of noise pollution includes transportation and frequent use of horn in vehicles. Dehradun is at a cross road and prominent national institutions like Survey of India, Oil and Natural Gas Corporation, Forest Research Institute, Indian Military Academy, Indian Institute of Remote Sensing, Wadia Institute of Himalayan Geology, Central and State Government offices are changing Dehradun into a busy, economically active vibrant city. Noise pollution levels (50.70 – 82.54 dB) more than recommended permissible limits (30 -75 dB) are observed in the Survey Chock, Prince Chock, Saharanpur Chock, Gandhi Park and Clock Tower. Exposure to high level of noise cause stress on human health such as auditory, nervous system, insomnia, hearing loss, reducing efficiency, sexual impotency, cardio-vascular, respiratory, neurological damages and limiting the human life. The execution of an appropriate management strategy for limiting noise pollution on affected sites is recommended. Keywords: Environmental monitoring, Noise pollution, Human health, Dehradun, Uttarakhand, India

Towns conquer: a gamified application to collect geographical names (vernacular names/toponyms)

The traditional model for geospatial crowd sourcing asks the public to use their free time collecting geospatial data for no obvious reward. This model has shown to work very well on projects such as Open Street Map, but comes with some clear disadvantages such as reliance on small communities of ‘Neo-geographers’ and variability in quality and content of collected data. This project aims at tackling these problems by providing alternative motivation specifically a smartphone based computer game service. Geographical names (vernacular names/ toponyms) have been identified as potential targets as they are difficult to collect on a large scale and easy to collect locally, thus ideal for crowd sourcing. The data set will be a toponyms database provided by the Spanish National Geographic Institute (IGN Spain). A location based game is targeted as it is easy to guide data collection with in-game rewards (prizes, points, badges etc.). Android is chosen for its accessible API and wide use

Wireless Intelligent Sensors Management Application Protocol-WISMAP

Although many recent studies have focused on the development of new applications for wireless sensor networks, less attention has been paid to knowledge-based sensor nodes. The objective of this work is the development in a real network of a new distributed system in which every sensor node can execute a set of applications, such as fuzzy ruled-base systems, measures, and actions. The sensor software is based on a multi-agent structure that is composed of three components: management, application control, and communication agents; a service interface, which provides applications the abstraction of sensor hardware and other components; and an application layer protocol. The results show the effectiveness of the communication protocol and that the proposed system is suitable for a wide range of applications. As real world applications, this work presents an example of a fuzzy rule-based system and a noise pollution monitoring application that obtains a fuzzy noise indicator

Exploring the use of mobile sensors for noise and black carbon measurements in an urban environment

Mobile measurements have been collected on a bicycle equipped with a global positioning system (GPS) in a few connecting streets in Gent (Belgium). The 1-s sound pressure levels and 1-s black carbon concentrations were measured. In addition, 5 continuous monitoring fixed stations connected to building facades were used. Different processing methods are compared, based on different temporal and spatial weighting aggregations. The possibility to take profit of the fixed stations to refine estimations is tested, according to the noise levels collected at fixed stations and the distance between mobile and fixed sensors. In a last step, route selection based on travel distance, noise levels and black-carbon measurements is explored based on the data obtained

A comparative study on VGI and professional noise data

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.The ubiquitous nature of mobile devices and its growing presence in urban areas, turn them up into low cost environmental monitoring platforms. In this field, several authors made different efforts to provide alternatives to Sensor Networks, to assess noise pollution in cities using crowdsourcing techniques. In this sense, citizens might potentially produce large spatio-temporal datasets using their mobile devices to measure noise levels. There are few attempts of assessing the quality of the mobile noise samples on a real scenario and compare them to commercial data to evaluate if they are reliable enough. This contribution reviews the existing applications to collect or assess the quality of noise samples when they are used as sound level meters. Moreover, it presents the results of our experiment: the volunteer noise dataset generated in a ‘mapping party’ on our campus is compared to professional data. Results show that VGI data might be sufficient for multiple daily situations

Raveguard: A noise monitoring platform using low-end microphones and machine learning

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time\u2013space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring

From Sensor to Observation Web with Environmental Enablers in the Future Internet

This paper outlines the grand challenges in global sustainability research and the objectives of the FP7 Future Internet PPP program within the Digital Agenda for Europe. Large user communities are generating significant amounts of valuable environmental observations at local and regional scales using the devices and services of the Future Internet. These communities’ environmental observations represent a wealth of information which is currently hardly used or used only in isolation and therefore in need of integration with other information sources. Indeed, this very integration will lead to a paradigm shift from a mere Sensor Web to an Observation Web with semantically enriched content emanating from sensors, environmental simulations and citizens. The paper also describes the research challenges to realize the Observation Web and the associated environmental enablers for the Future Internet. Such an environmental enabler could for instance be an electronic sensing device, a web-service application, or even a social networking group affording or facilitating the capability of the Future Internet applications to consume, produce, and use environmental observations in cross-domain applications. The term ?envirofied? Future Internet is coined to describe this overall target that forms a cornerstone of work in the Environmental Usage Area within the Future Internet PPP program. Relevant trends described in the paper are the usage of ubiquitous sensors (anywhere), the provision and generation of information by citizens, and the convergence of real and virtual realities to convey understanding of environmental observations. The paper addresses the technical challenges in the Environmental Usage Area and the need for designing multi-style service oriented architecture. Key topics are the mapping of requirements to capabilities, providing scalability and robustness with implementing context aware information retrieval. Another essential research topic is handling data fusion and model based computation, and the related propagation of information uncertainty. Approaches to security, standardization and harmonization, all essential for sustainable solutions, are summarized from the perspective of the Environmental Usage Area. The paper concludes with an overview of emerging, high impact applications in the environmental areas concerning land ecosystems (biodiversity), air quality (atmospheric conditions) and water ecosystems (marine asset management)