A Mobile and Low-Cost System for Environmental Monitoring: A Case Study

Velasco, Alejandro David; Ferrero, Renato; Gandino, Filippo; Montrucchio, Bartolomeo; Rebaudengo, Maurizio

doi:10.3390/s16050710

Cited by 49 publications

(39 citation statements)

References 20 publications

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“…The lifetime objective function corresponds to the maximum number of timeframes where the network is operating. b) Mobile deployment: Indeed, our models are designed for static networks, which is argued by the fact that pollution sensors operate well when they are static [32]. However, sinks can be considered as mobile nodes and this can be integrated in our model based on existing mobile-sinks formulations such as the work of [9].…”

Section: Future Workmentioning

confidence: 99%

Optimal WSN Deployment Models for Air Pollution Monitoring

Boubrima

Bechkit

Rivano

2017

IEEE Trans. Wireless Commun.

145

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Air pollution has become a major issue of modern megalopolis because of industrial emissions and increasing urbanization along with traffic jams and heating/cooling of buildings. Monitoring urban air quality is therefore required by municipalities and by the civil society. Current monitoring systems rely on reference sensing stations that are precise but massive, costly and therefore seldom. In this paper, we focus on an alternative or complementary approach, with a network of low cost and autonomic wireless sensors, aiming at a finer spatiotemporal granularity of sensing. Generic deployment models of the literature are not adapted to the stochastic nature of pollution sensing. Our main contribution is to design integer linear programming models that compute sensor deployments capturing both the coverage of pollution under time-varying weather conditions and the connectivity of the infrastructure. We evaluate our deployment models on a real data set of Greater London. We analyze the performance of the proposed models and show that our joint coverage and connectivity formulation is tight and compact, with a reasonable enough execution time. We also conduct extensive simulations to derive engineering insights for effective deployments of air pollution sensors in an urban environment.

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Section: Future Workmentioning

confidence: 99%

Optimal WSN Deployment Models for Air Pollution Monitoring

Boubrima

Bechkit

Rivano

2017

IEEE Trans. Wireless Commun.

145

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“…The SensorWebBike provides only a web application that shows a pollution map of the city to citizens without other user-related services such as anti-theft. Finally, Velasco et al [19] present a mobile wireless sensor network system aiming at complementing the already existing official air quality monitoring systems of the city of Torino. Some tests were carried out by mounting the proposed system on bicycles in order to increase their mobility.…”

Section: Related Workmentioning

confidence: 99%

SmartBike: an IoT Crowd Sensing Platform for Monitoring City Air Pollution

Corno¹,

Montanaro²,

Migliore³

et al. 2017

IJECE

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In recent years, the Smart City concept is emerging as a way to increase efficiency, reduce costs, and improve the overall quality of citizen life. The rise of Smart City solutions is encouraged by the increasing availability of Internet of Things (IoT) devices and crowd sensing technologies. This paper presents an IoT Crowd Sensing platform that offers a set of services to citizens by exploiting a network of bicycles as IoT probes. Based on a survey conducted to identify the most interesting bike-enabled services, the SmartBike platform provides: real time remote geo-location of users' bikes, anti-theft service, information about traveled route, and air pollution monitoring. The proposed SmartBike platform is composed of three main components: the SmartBike mobile sensors for data collection installed on the bicycle; the end-user devices implementing the user interface for geo-location and anti-theft; and the SmartBike central servers for storing and processing detected data and providing a web interface for data visualization. The suitability of the platform was evaluated through the implementation of an initial prototype. Results demonstrate that the proposed SmartBike platform is able to provide the stated services, and, in addition, that the accuracy of the acquired air quality measurements is compatible with the one provided by the official environmental monitoring system of the city of Turin. The described platform will be adopted within a project promoted by the city of Turin, that aims at helping people making their mobility behavior more sustainable.

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“…Kumar et al [18] presented an overview of environmental monitoring systems focused on energy efficiency and low-cost environmental monitoring systems. Velasco et al [19,20] discussed the benefits of a distributed approach and analyzed the main elements to take into account during the design of air quality monitoring systems.…”

Section: Related Workmentioning

confidence: 99%

“…According to the sensor type, motion could modify the sensor response, and this should be quantified (e.g., Spinelle, 2016 personal communication, and [20]). In addition, since sensors need to be routinely compared with reference networks, field campaigns should be planned with time scheduled passages and stops in the proximity of reference station networks and/or more reliable sensor measurements.…”

Section: Sensors In Motionmentioning

confidence: 99%

“…In recent years, according to Penza et al [13], several national and international R&D projects on wireless sensor networks deployed in the urban areas and cities have been funded for air quality monitoring with emphasis on sensor performance [15], fixed sensor-nodes distributed in the city (e.g., [4,[16][17][18][19][20], dust sensors [21]), mobile sensors mounted over public buses [5] and participatory environmental sensing involving citizens ( [6,7]). Several studies conducted overviews of the existing methodologies.…”

Section: Related Workmentioning

confidence: 99%

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The Smart Ring Experience in l’Aquila (Italy): Integrating Smart Mobility Public Services with Air Quality Indexes

Villani

Cignini²,

Ortenzi

et al. 2016

Chemosensors

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This work presents the "City Dynamics and Smart Environment" activities of the Smart Ring project, a model for the smart city, based on the integration of sustainable urban transport services and environmental monitoring over a 4-5-km circular path, the "Smart Ring", around the historical center of l'Aquila (Italy). We describe our pilot experience performed during an experimental on-demand public service electric bus, "SmartBus", which was equipped with a multi-parametric air quality low-cost gas electrochemical sensor platform, "NASUS IV". For five days (28-29 August 2014 and 1-3 September 2014), the sensor platform was installed inside the SmartBus and measured air quality gas compounds (nitrogen dioxide, carbon oxide, sulfur dioxide, hydrogen sulfide) during the service. Data were collected and analyzed on the bases of an air quality index, which provided qualitative insights on the air status potentially experienced by the users. The results obtained are in agreement with the synoptic meteorological conditions, the urban background air quality reference measurements and the potential traffic flow variations. Furthermore, they indicated that the air quality status was influenced by the gas component NO 2 , followed by H 2 S, SO 2 and CO. We discuss the features of our campaign, and we highlight the potential, limitations and key factors to consider for future project designs.

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A Mobile and Low-Cost System for Environmental Monitoring: A Case Study

Cited by 49 publications

References 20 publications

Optimal WSN Deployment Models for Air Pollution Monitoring

Optimal WSN Deployment Models for Air Pollution Monitoring

SmartBike: an IoT Crowd Sensing Platform for Monitoring City Air Pollution

The Smart Ring Experience in l’Aquila (Italy): Integrating Smart Mobility Public Services with Air Quality Indexes

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