Environmental Noise Mapping with Smartphone Applications: A Participatory Noise Map of West Hartford, CT

New England Noise-Con 2016: Revolution in Noise Control, Providence, Rhode Island, USA, 13-15 June 2016This paper reports on the second phase of an on-going study concerning the use of smartphone applications to measure environmental noise at the University of Hartford. This phase involved the development of two strategic noise maps of West Hartford town center: i) a standard noise map developed using traditional mapping techniques and ii) a participatory noise map utilizing smartphone-based measurement data (a citizen-science approach to noise mapping). The objective of the study was to assess the feasibility of developing a noise map using a citizen science based approach. Results suggest that smartphone applications can be used to collect environmental noise data and these data may be used in the development of a participatory noise map.Irish Research CouncilCollege of Engineering, Technology and Architecture Faculty Student Engagement Grant at the University of Hartford, USAFulbright Scholarshi

Data quality issues in environmental sensing with smartphones

This paper presents the results of a study about the performance and, consequently, challenges of using smartphones as data gatherers in mobile sensing campaigns to environmental monitoring. It is shown that there are currently a very large number of devices technologically enabled for tech-sensing with minimal interference of the users. On other hand, the newest devices seem to broke the sensor diversity trend, therefore making the approach of environmental sensing in the ubiquitous computing scope using smartphones sensors a more difficult task. This paper also reports on an experiment, emulating different common scenarios, to evaluate if the performance of environmental sensor-rich smartphones readings obtained in daily situations are reliable enough to enable useful collaborative sensing. The results obtained are promising for temperature measurements only when the smartphone is not being handled because the typical use of the device pollutes the measurements due to heat transfer and other hardware aspects. Also, we have found indicators of data quality issues on humidity sensors embedded in smartphones. The reported study can be useful as initial information about the behaviour of smartphones inner sensors for future crowdsensing application developers.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

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

Deviation prediction and correction on low-cost atmospheric pressure sensors using a machine-learning algorithm

The datasets used in this work are available in the Zenodo repository, with digital identifier (DOI) as 10.5281/zenodo.3560299.Atmospheric pressure sensors are important devices for several applications, including environment monitoring and indoor positioning tracking systems. This paper proposes a method to enhance the quality of data obtained from low-cost atmospheric pressure sensors using a machine learning algorithm to predict the error behaviour. By using the extremely Randomized Trees algorithm, a model was trained with a reference sensor data for temperature and humidity and with all low-cost sensor datasets that were co-located into an artificial climatic chamber that simulated different climatic situations. Fifteen low-cost environmental sensor units, composed by five different models, were considered. They measure - together - temperature, relative humidity and atmospheric pressure. In the evaluation, three categories of output metrics were considered: raw; trained by the independent sensor data; and trained by the low-cost sensor data. The model trained by the reference sensor was able to reduce the Mean Absolute Error (MAE) between atmospheric pressure sensor pairs by up to 67%, while the same ensemble trained with all low-cost data was able to reduce the MAE by up to 98%. These results suggest that low-cost environmental sensors can be a good asset if their data are properly processed.- (undefined