Citizens as smart, active sensors for a quiet and just city: the case of the “open source soundscapes” approach to identify, assess and plan “everyday quiet areas” in cities

Today the so-called “smart city” is connoted by massive implementation of novel, digital technology, which is often considered as the best solution to global issues affecting contemporary cities. Sophisticated and low-cost technological solutions are developed also in the field of noise monitoring and they are expected to play an important role for acousticians, city planners and policy makers. However, the “smart city” paradigm is controversial: it relies on advanced technological solutions, yet it fails to consider the city as a social construct and it often overlooks the role of citizens, in the quest for technological advances and novel methods. This is especially true in the field of smart acoustic solutions addressing the issue of urban quiet areas: main methods and technologies developed so far barely involve citizens and consider their preferences. This contribution tackles this challenge, by illustrating a novel mixed methodology, which combines the soundscape approach, the citizen science paradigm and a novel mobile application – the Hush City app – with the ultimate goal of involving people in identifying, assessing and planning urban quiet areas. Firstly, the theoretical background and the methods applied are described; secondly initial findings are discussed; thirdly potential impact and future work are outlined

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

EdgeSense: Edge-Mediated Spatial-Temporal Crowdsensing

Edge computing recently is increasingly popular due to the growth of data size and the need of sensing with the reduced center. Based on Edge computing architecture, we propose a novel crowdsensing framework called Edge-Mediated Spatial-Temporal Crowdsensing. This algorithm targets on receiving the environment information such as air pollution, temperature, and traffic flow in some parts of the goal area, and does not aggregate sensor data with its location information. Specifically, EdgeSense works on top of a secured peer-To-peer network consisted of participants and propose a novel Decentralized Spatial-Temporal Crowdsensing framework based on Parallelized Stochastic Gradient Descent. To approximate the sensing data in each part of the target area in each sensing cycle, EdgeSense uses the local sensor data in participants\u27 mobile devices to learn the low-rank characteristic and then recovers the sensing data from it. We evaluate the EdgeSense on the real-world data sets (temperature [1] and PM2.5 [2] data sets), where our algorithm can achieve low error in approximation and also can compete with the baseline algorithm which is designed using centralized and aggregated mechanism

Mobile sensing for behavioral research: A component-based approach for rapid deployment of sensing campaigns

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was partially funded by the National Council for Science and Technology (CONACYT) in Mexico through a scholarship provided to I.R.F. Also, this work was partially funded by the Instituto Tecnológico de Sonora (ITSON) through the PROFAPI program.Collecting experimental data from multiple sensing devices has just recently become quite popular in behavioral and social sciences. Among existing devices, mobile phones stand out as they allow researchers to collect data from individuals in an unbiased, precise, unobtrusive, and timely manner. Current mobile sensing applications are typically developed from scratch, provide no reusable components, and frequently do not take advantage of the devices’ processing capabilities. In light of such limitations, this work presents a novel tool that leverages mobile phones not only to collect data via their sensors but also to process them on the device as soon as they are gathered. The tool provides researchers with easy-to-use services that allow them to configure the required processing routines on the mobile phones. This work proposes a new approach for rapid deployment of sensing campaigns targeted at scientists with basic technical knowledge and requiring low effort. We performed an evaluation aimed at determining whether there is a significant improvement in terms of user effectiveness and efficiency in the definition of new components. The results suggest that the proposed tool speeds up the time and reduces the effort taken for setting up and deploying a sensing campaign

Assignment of sensing tasks to IoT devices: Exploitation of a Social Network of Objects

The Social Internet of Things (SIoT) is a novel communication paradigm according to which the objects connected to the Internet create a dynamic social network that is mostly used to implement the following processes: route information and service requests, disseminate data, and evaluate the trust level of each member of the network. In this paper, the SIoT paradigm is applied to a scenario where geolocated sensing tasks are assigned to fixed and mobile devices, providing the following major contributions. The SIoT model is adopted to find the objects that can contribute to the application by crawling the social network through the nodes profile and trust level. A new algorithm to address the resource management issue is proposed so that sensing tasks are fairly assigned to the objects in the SIoT. To this, an energy consumption profile is created per device and task, and shared among nodes of the same category through the SIoT. The resulting solution is also implemented in the SIoT-based Lysis platform. Emulations have been performed, which showed an extension of the time needed to completely deplete the battery of the first device of more than 40% with respect to alternative approaches

GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing

[EN] Noise pollution is an emerging and challenging problem of all large metropolitan areas, affecting the health of citizens in multiple ways. Therefore, obtaining a detailed and real-time map of noise in cities becomes of the utmost importance for authorities to take preventive measures. Until now, these measurements were limited to occasional sampling made by specialized companies, that mainly focus on major roads. In this paper, we propose an alternative approach to this problem based on crowdsensing. Our proposed architecture empowers participating citizens by allowing them to seamlessly, and based on their context, sample the noise in their surrounding environment. This allows us to provide a global and detailed view of noise levels around the city, including places traditionally not monitored due to poor accessibility, even while using their vehicles. In the paper, we detail how the different relevant issues in our architecture, i.e., smartphone calibration, measurement adequacy, server design, and client¿server interaction, were solved, and we have validated them in real scenarios to illustrate the potential of the solution achieved.This work was partially supported by Valencia's Traffic Management Department, by the "Ministerio de Economia y Competitividad, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I + D + I 2014", Spain, under Grant TEC2014-52690-R, and the "Universidad Laica Eloy Alfaro de Manabi, and the Programa de Becas SENESCYT" de la Republica del Ecuador.Zamora-Mero, WJ.; Vera, E.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P. (2018). GRC-Sensing: An Architecture to Measure Acoustic Pollution Based on Crowdsensing. Sensors. 18(8):1-25. https://doi.org/10.3390/s18082596S12518