F. Formisano

A maker friendly mobile and social sensing approach to urban air quality monitoring

Novel model of citizenship calls for a new approach to the policy making, characterized by the wish to be part of the information building process. The citizen wants to become an active member of the smart city. This has its impact also on the air quality monitoring and control process. In this work, we try to answer to these needs by investigating a citizen centered air quality monitoring concept. The goal is to enable individuals to monitor their exposure to air pollution and simultaneously to contribute creating a map of the state of urban air quality through the sharing of data.

Calibrating chemical multisensory devices for real world applications: An in-depth comparison of quantitative machine learning approaches

Abstract Chemical multisensor devices need calibration algorithms to estimate gas concentrations. Their possible adoption as indicative air quality measurements devices poses new challenges due to the need to operate in continuous monitoring modes in uncontrolled environments. Several issues, including slow dynamics, continue to affect their real world performances. At the same time, the need for estimating pollutant concentrations on board the devices, especially for wearables and IoT deployments, is becoming highly desirable. In this framework, several calibration approaches have been proposed and tested on a variety of proprietary devices and datasets; still, no thorough comparison is available to researchers. This work attempts a benchmarking of the most promising calibration algorithms according to recent literature with a focus on machine learning approaches. We test the techniques against absolute and dynamic performances, generalization capabilities and computational/storage needs using three different datasets sharing continuous monitoring operation methodology. Our results can guide researchers and engineers in the choice of optimal strategy. They show that non-linear multivariate techniques yield reproducible results, outperforming linear approaches. Specifically, the Support Vector Regression method consistently shows good performances in all the considered scenarios. We highlight the enhanced suitability of shallow neural networks in a trade-off between performance and computational/storage needs. We confirm, on a much wider basis, the advantages of dynamic approaches with respect to static ones that only rely on instantaneous sensor array response. The latter have been shown to be best choice whenever prompt and precise response is needed.

Electronic Noses for Composites Surface Contamination Detection in Aerospace Industry

The full exploitation of Composite Fiber Reinforced Polymers (CFRP) in so-called green aircrafts design is still limited by the lack of adequate quality assurance procedures for checking the adhesive bonding assembly, especially in load-critical primary structures. In this respect, contamination of the CFRP panel surface is of significant concern since it may severely affect the bonding and the mechanical properties of the joint. During the last years, the authors have developed and tested an electronic nose as a non-destructive tool for pre-bonding surface inspection for contaminants detection, identification and quantification. Several sensors and sampling architectures have been screened in view of the high Technology Readiness Level (TRL) scenarios requirements. Ad-hoc pattern recognition systems have also been devised to ensure a fast and reliable assessment of the contamination status, by combining real time classifiers and the implementation of a suitable rejection option. Results show that e-noses could be used as first line low cost Non Destructive Test (NDT) tool in aerospace CFRP assembly and maintenance scenarios.

Tinynose, an Auxiliary Smart Gas Sensor for RFID Tag in Vegetables Ripening Monitoring During Refrigerated Cargo Transport

Current state of the art’s real time monitoring of fresh produce, provides only temperature and humidity information. The idea is to develop new sensors and transport service assisted by ICT platform to monitor fresh produce quality. The technology will be based on radio frequency (RF) wireless network, and will provide real-time and in-site data-logging, enabling active management of food products in storage and transit. The RF sensor network design, allows an auxiliary sensor node to be “plugged in”. In this contribution we present the commercial platform developed starting from o fork of open hardware on which we assemble an array of commercial sensors in an “open air” configuration. We developed for this Embedded Gas Sensor System Device (Tinynose) two set-up: the “Development set-up” that uses a star wireless network infrastructure to store all raw sensor output and associated measurement error in a server DB to define parameters in ripening mathematical model and the “TAG set-up” that performs ripening evaluation in ICT in-site platform.

Electronic Nose Detection of Hydraulic-Oil Fingerprint Contamination in Relevant Aircraft Maintenance Scenarios

Modern aircraft structure, by making use of lightweight composite materials based on carbon fiber reinforced plastics (CFRP), succeeds in reducing CO2 emissions and transport fuel costs. Nevertheless, its usage cannot leave Non Destructive Tests out to consideration in order to set up a quality assurance procedure of surfaces’ contamination status. Here, we show and compare two different e-nose solutions able to detect and quantify hydraulic-oil fingerprint contamination at significantly low contamination levels occurring during aircraft maintenance operations.

Cooperative Air Quality Sensing with Crowdfunded Mobile Chemical Multisensor Devices

In this work, we describe a cooperative air quality sensor architecture based on crowdfunded, mobile, electrochemical sensor based, monitoring systems. The platform aims to produce enhanced information on personal pollutant exposure and enable cooperative reconstruction of high resolution pictures of air pollution in the urban landscape. The calibrated devices are connected to smartphones that provide georeferenced visualization of personal exposure and session based log capabilities. A cloud based interface provides a sensor fusion based mapping capability exploiting google maps APIs. An in-lab calibration by linear regression with temperature correction has been computed and preliminary results have been reported. A small set of calibrated devices will be shipped to crowdfunders for extended field tests in different italian cities.

Hydraulic oil fingerprint contamination detection for aircraft CFRP maintenance by electronic nose

The full-scale adoption of light-weighted carbon fiber reinforced plastics (CFRP) in the primary aircraft structures cannot be safely achieved without a reliable quality assurance protocol. A pre-bond cleaning state inspection of adherents surfaces by means of Non-Destructive Tests (NDTs) is absolutely necessary in order to guarantee the strength of bonded joints and so the structural aircraft integrity. Hydraulic-oil fingerprint contamination mainly occurs during aircraft maintenance operations, due to wrong handling by workers. It may severely affect the mechanical properties of bonded joints. Here, an ENEA customized electronic nose solution and a suitable measurement methodology are proposed as NDT tool for pre-bond detection of the Skydrol contamination state of the involved surfaces, at least at high concentration level. The protocol provides a reliable and efficient strategy to uplift and extend the technology readiness level (TRL) of NDTs allowing to overcome existing limitations in adhesive bonding for CFRP materials.

A crowdfunded personal air quality monitor infrastructure for active life applications

This work aims to present the results of the first phase of crowdfunding campaign devised to involve general public in the development of a portable device for air quality monitoring and the associated infrastructure. A portable device based on an array of electrochemical sensors has been developed and calibrated in lab in order to build detailed maps of personal exposure to air pollutants with the help of an Android App and a sensor NOSQL based backend. Two associated calibration procedures are depicted, one based on in-lab recordings while the second, based on the emerging on field calibration paradigm, will refine the performance of the node. Preliminary tests shows that the project is now ready for its second step in which a small fleet of devices will be shipped to the crowdfunding supporters.

A Distributed Sensor Network for Waste Water Management Plant Protection

Waste water management process has a significant role in guarantee sea and surface water bodies water quality with direct impact on tourism based economy and public health. Protection of this critical infrastructure form illicit discharges is hence paramount for the whole society. Here, We propose a pervasive monitoring centered approach to the protection of wastewater management plant. An hybrid sensor network is actually deployed along the wastewater network including several different transducers. Incepted data are harmonized and processed with an integrated SWMM model and machine learning based approach in order to forecast water qualitative and quantitative aspects, detect and localize anomalies. An advanced WEBGIS-SOS based interface conveys relevant information to the management entity allowing it to take appropriate actions in a timely way, reducing and mitigating the impacts of illicit discharges.

An integrated infrastructure for distributed waste water quality monitoring and decision support

Waste water management plant protection is a major concern for water cycle management entities. The rapid identification and possible localization of anomalous or even malicious waste liquids immission may allow for undertaking pollution risk mitigation actions (e.g. using of ancillary basins) and reduce maintenance costs. Pervasive monitoring of the transport network is hence needed although economic and technical issues prevent its implementation. The SIMONA project is aimed to design, deploy and test an integrated, intelligent, pervasive monitoring infrastructure based on a network of low cost/low maintenance quali-quantitative multisensor nodes. A scalable data processing facility permit the ingestion and the processing of the data stream while a set of models provide for quali-quantitative forecasting increasing the manager situational awareness about the smart infrastructure. All the information is made available via a GIS based Web HCI.