NoiseSPY: a real-time mobile phone platform for urban noise monitoring and mapping

In this paper we present the design, implementation, evaluation, and user experiences of the NoiseSpy application, our sound sensing system that turns the mobile phone into a low-cost data logger for monitoring environmental noise. It allows users to explore a city area while collaboratively visualizing noise levels in real-time. The software combines the sound levels with GPS data in order to generate a map of sound levels that were encountered during a journey. We report early findings from the trials which have been carried out by cycling couriers who were given Nokia mobile phones equipped with the NoiseSpy software to collect noise data around Cambridge city. Indications are that, not only is the functionality of this personal environmental sensing tool engaging for users, but aspects such as personalization of data, contextual information, and reflection upon both the data and its collection, are important factors in obtaining and retaining their interest

2Loud? Monitoring traffic noise with mobile phones

The World Health Organization has recently focused attention on guidelines for night noise in urban areas, based on significant medical evidence of the adverse impacts of exposure to excessive traffic noise on health, especially caused by sleep disturbance. This includes serious illnesses, such as hypertension, arteriosclerosis and myocardial infarction. 2Loud? is a research project with the aim of developing and testing a mobile phone application to allow a community to monitor traffic noise in their environment, with focus on the night period and indoor measurement. Individuals, using mobile phones, provide data on characteristics of their dwellings and systematically record the level of noise inside their homes overnight. The records from multiple individuals are sent to a server, integrated into indicators and shared through mapping. The 2Loud? application is not designed to replace existing scientific measurements, but to add information which is currently not available. Noise measurements to assist the planning and management of traffic noise are normally carried out by designated technicians, using sophisticated equipment, and following specific guidelines for outdoors locations. This process provides very accurate records, however, for being a time consuming and expensive system, it results in a limited number of locations being surveyed and long time between updates. Moreover, scientific noise measurements do not survey inside dwellings. In this paper we present and discuss the participatory process proposed, and currently under implementation and test, to characterize the levels of exposure to traffic noise of residents living in the vicinity of highways in the City of Boroondara (Victoria, Australia) using the 2Loud? application

Evaluation of the environmental noise levels in Abuja Municipality using mobile phones

Background: Noise remains a nuisance which impacts negatively on the physical, social and psychological wellbeing of man. It aggravates chronic illnesses like hypertension and other cardiopulmonary diseases. Unfortunately, increased activities from industrialization and technological transfers/drifts have tumultuously led to increased noise pollution in most of our fast growing cities today and hence the need for concerted efforts in monitoring and regulating our environmental noise.Objectives: To assess the equivalent noise level (Leq) in Abuja municipality and promote a simple method for regular assessment of Leq within our environment.Methods: This is a cross-sectional community based study of the environmental Leq of Abuja municipality conducted between January 2014 and January 2016. The city was divided into 12 segments including residential, business and market areas via the Abuja Geographic Information System. The major markets were captured separately on a different scale. Measurements were taken with the mobile phone softwares having validated this withExtech 407730 digital sound level meter, serial no Z310135 . Leq(A) were measured at different points and hours of the day and night. The average Leq(A) were classified according to localities and compared with WHO standard safety levels.Results: LeqD ranged 71-92dB(A); 42-79dB(A) and 69-90dB(A) in business/ parks, residential and market places respectively. The Night measurements were similar 18dB(A)-56dB(A) and the day-night Leq(A)=77.2dB(A) and 90.4dB(A) for residential and business zones.Conclusion: The night noise levels are satisfactory but the day and daynight levels are above the recommended tolerable values by WHO and therefore urgently call for awareness and legislative regulations

Accurate Ambient Noise Assessment Using Smartphones

[EN] Nowadays, smartphones have become ubiquitous and one of the main communication resources for human beings. Their widespread adoption was due to the huge technological progress and to the development of multiple useful applications. Their characteristics have also experienced a substantial improvement as they now integrate multiple sensors able to convert the smartphone into a flexible and multi-purpose sensing unit. The combined use of multiple smartphones endowed with several types of sensors gives the possibility to monitor a certain area with fine spatial and temporal granularity, a procedure typically known as crowdsensing. In this paper, we propose using smartphones as environmental noise-sensing units. For this purpose, we focus our study on the sound capture and processing procedure, analyzing the impact of different noise calculation algorithms, as well as in determining their accuracy when compared to a professional noise measurement unit. We analyze different candidate algorithms using different types of smartphones, and we study the most adequate time period and sampling strategy to optimize the data-gathering process. In addition, we perform an experimental study comparing our approach with the results obtained using a professional device. Experimental results show that, if the smartphone application is well tuned, it is possible to measure noise levels with a accuracy degree comparable to professional devices for the entire dynamic range typically supported by microphones embedded in smartphones, i.e., 35 95 dB.This work was partially supported by the “Programa Estatal de Investigación, Desarrollo e InnovaciOn Orientada a Retos de la Sociedad, Proyecto TEC2014-52690-R”, the “Universidad Laica Eloy Alfaro de Manabí” and the “Programa de Becas SENESCYTde la República del Ecuador.”Zamora-Mero, WJ.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P. (2017). Accurate Ambient Noise Assessment Using Smartphones. Sensors. 17(4):1-18. doi:10.3390/s17040917S118174Noise European Environment Agencyhttp://www.eea.europa.eu/themes/noise/introZannin, P. H. T., Ferreira, A. M. C., & Szeremetta, B. (2006). Evaluation of Noise Pollution in Urban Parks. Environmental Monitoring and Assessment, 118(1-3), 423-433. doi:10.1007/s10661-006-1506-6Kanjo, E. (2009). NoiseSPY: A Real-Time Mobile Phone Platform for Urban Noise Monitoring and Mapping. Mobile Networks and Applications, 15(4), 562-574. doi:10.1007/s11036-009-0217-yAssessment and management of environmental noise (EU Directive)http://eur-lex.europa.eu/eli/dir/2002/49/ojCommission Directive (EU) 2015/ 996 of 19 May 2015http://eur-lex.europa.eu/eli/dir/2015/996/ojLane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140-150. doi:10.1109/mcom.2010.5560598Ganti, R., Ye, F., & Lei, H. (2011). Mobile crowdsensing: current state and future challenges. IEEE Communications Magazine, 49(11), 32-39. doi:10.1109/mcom.2011.6069707Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., & Zhou, X. (2015). Mobile Crowd Sensing and Computing. ACM Computing Surveys, 48(1), 1-31. doi:10.1145/2794400Maisonneuve, N., Stevens, M., Niessen, M. E., & Steels, L. (2009). NoiseTube: Measuring and mapping noise pollution with mobile phones. Environmental Science and Engineering, 215-228. doi:10.1007/978-3-540-88351-7_16Rana, R., Chou, C. T., Bulusu, N., Kanhere, S., & Hu, W. (2015). Ear-Phone: A context-aware noise mapping using smart phones. Pervasive and Mobile Computing, 17, 1-22. doi:10.1016/j.pmcj.2014.02.001Kardous, C. A., & Shaw, P. B. (2014). Evaluation of smartphone sound measurement applications. The Journal of the Acoustical Society of America, 135(4), EL186-EL192. doi:10.1121/1.4865269Le Prell, C., Nast, D., & Speer, W. (2014). Sound level measurements using smartphone «apps»: Useful or inaccurate? Noise and Health, 16(72), 251. doi:10.4103/1463-1741.140495Sonometer PCE322Ahttp://www.pce-iberica.es/medidor-detalles-tecnicos/instrumento-de-ruido/sonometro-con-logger-de-datos-sl-322.htmKardous, C. A., & Shaw, P. B. (2016). Evaluation of smartphone sound measurement applications (apps) using external microphones—A follow-up study. The Journal of the Acoustical Society of America, 140(4), EL327-EL333. doi:10.1121/1.4964639Zamora, W., Calafate, C. T., Cano, J.-C., & Manzoni, P. (2016). A Survey on Smartphone-Based Crowdsensing Solutions. Mobile Information Systems, 2016, 1-26. doi:10.1155/2016/9681842Electroacoustics—Sound level meters—Part 1: Specificationshttps://webstore.iec.ch/publication/5708Samsung Galaxy S7 edge SM-G935T Complimentary Teardown Report with Additional Commentaryhttp://www.techinsights.com/about-techinsights/overview/blog/samsung-galaxy-s7-edge-teardown

Development of a methodology for monitoring acoustic noise using mobile phones for ordinary citizens

In the context of the "4 th industrial revolution" that is taking shape, people with certain knowledge, experience and ecological culture are more actively involved in individual and collective decision-making to overcome various environmental problems. One of these environmental problems is the acoustic noise of the environment. Today, many citizens are wondering: what is the level of acoustic noise pollution in the place where I live, work, study or stay and whether it meets the standards. Is this sound level harmful to my health? To answer these questions, many international and national standards and other regulatory documents have been developed. However, these documents are complex, require the use of expensive equipment, i.e. inaccessible to ordinary citizens. On the other hand, the computational, communication and sensory capabilities of modern mobile phones allow them to measure noise performance in order to conduct noise monitoring. To do this, citizens need simple and clear methods, methodologies, instructions for assessing the noise situation in a certain area and creating noise maps using these measurements. The article attempts to develop a simplified methodology for conducting noise monitoring for citizens using their mobile phones. Also, for clarity of the process, monitoring experiments were carried out with mobile phones and professional sound level meters, in order to assess the harmful effects of traffic noise (B. Vahabzade Avenue) on an educational institution (Baku State University – BSU) and research activities (Institute Information Technology-IIT). In preparing the article, general scientific methods and methodologies were used, such as analysis and synthesis, comparison, generalization, and a systematic approach. Citizens can use the survey results to identify areas of noise discomfort using their mobile phone

Noise mapping based on participative measurements

The 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 higher level indicators, and to map the collected and processed data based on web services

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

Crowdsensing solutions for urban pollution monitoring using smartphones

La contaminación ambiental es uno de los principales problemas que afecta a nuestro planeta. El crecimiento industrial y los aglomerados urbanos, entre otros, están contribuyendo a que dicho problema se diversifique y se cronifique. La presencia de contaminantes ambientales en niveles elevados afecta la salud humana, siendo la calidad del aire y los niveles de ruido ejemplos de factores que pueden causar efectos negativos en las personas tanto psicológicamente como fisiológicamente. Sin embargo, la ubiquidad de los microcomputadores, y el aumento de los sensores incorporados en nuestros smartphones, han hecho posible la aparición de nuevas estrategias para medir dicha contaminación. Así, el Mobile Crowdsensing se ha convertido en un nuevo paradigma mediante el cual los teléfonos inteligentes emergen como tecnología habilitadora, y cuya adopción generalizada proporciona un enorme potencial para su crecimiento, permitiendo operar a gran escala, y con unos costes asumibles para la sociedad. A través del crowdsensing, los teléfonos inteligentes pueden convertirse en unidades de detección flexibles y multiuso que, a través de los sensores integrados en dichos dispositivos, o combinados con nuevos sensores, permiten monitorizar regiones de interés con una buena granularidad tanto espacial como temporal. En esta tesis nos centramos en el diseño de soluciones de crowdsensing usando smartphones donde abordamos problemas de contaminación ambiental, específicamente del ruido y de la contaminación del aire. Con este objetivo, se estudian, en primer lugar, las propuestas de crowdsensing que han surgido en los últimos años. Los resultados de nuestro estudio demuestran que todavía hay mucha heterogeneidad en términos de tecnologías utilizadas y métodos de implementación, aunque los diseños modulares en el cliente y en el servidor parecen ser dominantes. Con respecto a la contaminación del aire, proponemos una arquitectura que permita medir la contaminación del aire, concretamente del ozono, dentro de entornos urbanos. Nuestra propuesta utiliza smartphones como centro de la arquitectura, siendo estos dispositivos los encargados de leer los datos de un sensor móvil externo, y de luego enviar dichos datos a un servidor central para su procesamiento y tratamiento. Los resultados obtenidos demuestran que la orientación del sensor y el período de muestreo, dentro de ciertos límites, tienen muy poca influencia en los datos capturados. Con respecto a la contaminación acústica, proponemos una arquitectura para medir los niveles de ruido en entornos urbanos basada en crowdsensing, y cuya característica principal es que no requiere intervención del usuario. En esta tesis detallamos aspectos tales como la calibración de los smartphones, la calidad de las medidas obtenidas, el instante de muestreo, el diseño del servidor, y la interacción cliente-servidor. Además, hemos validado nuestra solución en escenarios reales para demostrar el potencial de la solución alcanzada. Los resultados experimentales muestran que, con nuestra propuesta, es posible medir niveles de ruido en diferentes zonas urbanas o rurales con un grado de precisión comparable al de los dispositivos profesionales, todo ello sin requerir intervención del usuario, y con un consumo reducido en cuanto a recursos del sistema. En general, las diferentes contribuciones de esta tesis doctoral ofrecen un punto de partida para nuevos desarrollos, ofreciendo estrategias de calibración y algoritmos eficientes de cara a realizar medidas representativas. Además, una importante ventaja de nuestra propuesta es que puede ser implementada de forma directa tanto en instituciones públicas como no gubernamentales en poco tiempo, ya que utiliza tecnología accesible y soluciones basadas en código abierto.La contaminació ambiental és un dels principals problemes que afecten el nostre planeta. El creixement industrial i els aglomerats urbans, entre altres, estan contribuint al fet que aquest problema es diversifique i es cronifique. La presència de contaminants ambientals en nivells elevats afecta la salut humana, sent la qualitat de l'aire i els nivells de soroll exemples de factors que poden causar efectes negatius en les persones, tant psicològicament com fisiològicament. No obstant això, la ubiqüitat de les microcomputadores i l'augment dels sensors incorporats als nostres telèfons intel·ligents han fet possible l'aparició de noves estratègies per a mesurar aquesta contaminació. Així, el mobile crowdsensing s'ha convertit en un nou paradigma mitjançant el qual els telèfons intel·ligents emergeixen com a tecnologia habilitadora, i l'adopció generalitzada d'aquest proporciona un enorme potencial per al seu creixement, ja que permet operar a gran escala i amb uns costos assumibles per a la societat. A través del crowdsensing, els telèfons intel·ligents poden convertir-se en unitats de detecció flexibles i multiús que, a través dels sensors integrats en els esmentats dispositius, o combinats amb nous sensors, permeten monitoritzar regions d'interès amb una bona granularitat, tant espacial com temporal. En aquesta tesi ens centrem en el disseny de solucions de crowdsensing usant telèfons intel·ligents, on abordem problemes de contaminació ambiental, específicament del soroll i de la contaminació de l'aire. Amb aquest objectiu, s'estudien, en primer lloc, les propostes de crowdsensing que han sorgit en els últims anys. Els resultats del nostre estudi demostren que encara hi ha molta heterogeneïtat en termes de tecnologies utilitzades i mètodes d'implementació, encara que els dissenys modulars en el client i en el servidor semblen ser dominants. Pel que fa a la contaminació de l'aire, proposem una arquitectura que permeta mesurar la contaminació d'aquest, concretament de l'ozó, dins d'entorns urbans. La nostra proposta utilitza telèfons intel·ligents com a centre de l'arquitectura, sent aquests dispositius els encarregats de llegir les dades d'un sensor mòbil extern, i d'enviar després aquestes dades a un servidor central per al seu processament i tractament. Els resultats obtinguts demostren que l'orientació del sensor i el període de mostratge, dins de certs límits, tenen molt poca influència en les dades capturades. Pel que fa a la contaminació acústica, proposem una arquitectura per a mesurar els nivells de soroll en entorns urbans basada en crowdsensing, i la característica principal de la qual és que no requereix intervenció de la persona usuària. En aquesta tesi detallem aspectes com ara el calibratge dels telèfons intel·ligents, la qualitat de les mesures obtingudes, l'instant de mostratge, el disseny del servidor i la interacció client-servidor. A més, hem validat la nostra solució en escenaris reals per a demostrar el potencial de la solució assolida. Els resultats experimentals mostren que, amb la nostra proposta, és possible mesurar nivells de soroll en diferents zones urbanes o rurals amb un grau de precisió comparable al dels dispositius professionals, tot això sense requerir intervenció de l'usuari o usuària, i amb un consum reduït quant a recursos del sistema. En general, les diferents contribucions d'aquesta tesi doctoral ofereixen un punt de partida per a nous desenvolupaments, i ofereixen estratègies de calibratge i algorismes eficients amb vista a realitzar mesures representatives. A més, un important avantatge de la nostra proposta és que pot ser implementada de forma directa tant en institucions públiques com no governamentals en poc de temps, ja que utilitza tecnologia accessible i solucions basades en el codi obert.Environmental pollution is one of the main problems that affect our planet. Industrial growth and urban agglomerations, among others, are contributing to the diversification and chronification of this problem. The presence of environmental pollutants at high levels affect human health, with air quality and noise levels being examples of factors that can cause negative effects on people both psychologically and physiologically. Traditionally, environmental pollution is measured through monitoring centers, which are usually fixed and have a high cost. However, the ubiquity of microcomputers and the increase in the number of sensors embedded in our smartphones, have paved the way for the appearance of new strategies to measure such pollution. Thus, Mobile Crowdsensing has become a new paradigm through which smartphones emerge as an enabling technology, and whose widespread adoption provides enormous potential for growth, allowing large-scale operations, and with costs acceptable to our society. Through crowdsensing, smartphones can become flexible and multipurpose detection units that, through the sensors integrated into these devices, or combined with new sensors, allow monitoring regions of interest with good spatial and temporal granularity. In this thesis, we focus on the design of crowdsensing solutions using smartphones. We deal with environmental pollution problems, specifically noise and air pollution. With this objective, the crowdsensing proposals that have emerged in recent years are studied in the first place. The results of our study show that there is still a lot of heterogeneity in terms of technologies used and implementation methods, although modular designs at both client and server seem to be dominant. Concerning air pollution, we propose an architecture that allows measuring air pollution, specifically ozone, in urban environments. Our proposal uses smartphones as the center of the architecture, being these devices responsible for reading the data obtained by an external mobile sensor, and then sending such data to a central server for processing and analysis. In this proposal, several problems have been analyzed with regard to the orientation of the external sensor and the sampling time, and the proposed solution has been validated in real scenarios. The results obtained show that the orientation of the sensor and the sampling period, within certain limits, have very little influence on the captured data. Also, by comparing the heat maps generated by our solution with the data from the existing monitoring stations in the city of Valencia, we demonstrate that our approach is capable of providing greater data granularity. Concerning noise pollution, we propose an architecture to measure noise levels in urban environments based on crowdsensing, and whose main characteristic is that it does not require user intervention. In this thesis, we detail aspects such as the calibration of smartphones, the quality of the measurements obtained, the sampling instant, the server design, and the client-server interaction. Besides, we have validated our solution in real scenarios to demonstrate the potential of the proposed solution. Experimental results show that, with our proposal, it is possible to measure noise levels in different urban or rural areas with a degree of precision comparable to that of professional devices, all without requiring the intervention of the user, and with reduced consumption of system resources. In general, the different contributions of this doctoral thesis provide a starting point for new developments, offering efficient calibration strategies and algorithms to make representative measurements. Besides, a significant advantage of our proposal is that it can be implemented straightforwardly by both public and non-governmental institutions in a short time, as it relies on accessible technology and open source softwareZamora Mero, WJ. (2018). Crowdsensing solutions for urban pollution monitoring using smartphones [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/115483TESI