Saverio De Vito

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.

An Ontology Framework for Flooding Forecasting

Floods can cause significant damage and disruption as they often affect highly urbanized areas. The capability of knowledge using and sharing is the main reason why the ontologies are suited for supporting the phases of forecasting in (near-) real time disastrous flooding events and managing the flooding alert and emergency. This research work develops an ontology, FloodOntology for floods forecasting based on continuous measurements of water parameters gathered in the watersheds and in the sewers and simulation models. Concepts are captured across the main involved domains i.e. hydrological/hydraulic domains and SN-based monitoring domain. Classes hierarchies, properties and semantic constraints are defined related to all involved entities, obtaining a structured and unified knowledge-base on the flooding risk forecasting, to be integrated in expert systems.

An Holistic Approach to e-Nose Response Patterns Analysis—An Application to Nondestructive Tests

Artificial olfaction is an emerging application field for machine learning practitioners. In this paper, we propose a holistic approach to pattern classification in electronic noses applications. In particular, we show how classification results based on a complete measurement cycle can be combined with an assessment provided by real-time classifiers acting on the single instantaneous measurement sample. A running classification confidence measure allows for obtaining fast and reliable outcomes. A safety critical scenario has been selected for the testing of the proposed pattern analysis strategy involving the identification and discrimination of surface contaminants on composite panels in pre-bonding nondestructive tests during lightweight aircraft assembly. A reject option has been introduced to refuse low classification confidence panels improving both FP and FN rates. Results show how this strategy can efficiently exploit two different views of the electronic nose olfactive fingerprinting process that is currently seen as alternative.

7.4.5 Wireless Chemical Sensor Networks for Air Quality Monitoring

Air quality assessment and monitoring could be efficiently performed by a distributed network of cost effective chemical sensors cooperating for the reconstruction of a chemical image of the sensed environment. The obtainable insights are of paramount importance in many applications ranging from city air pollution monitoring to energy efficiency in smart buildings. However several research studies have highlighted the challenging nature of developing such architecture. Actually, the intrinsic properties of the sensed phenomena, those of the commercially available chemical sensors including power requirement and stability, together with the needed communication infrastructure make a real world implementation of such a system a problem worth of significant investigation efforts. This paper encompass the ENEA-UTTP efforts in providing solutions to several challenges both in indoor and outdoor air quality monitoring setups.

A SWE Architecture for Real Time Water Quality Monitoring Capabilities Within Smart Drinking Water and Wastewater Network Solutions

The world is facing a water quantity and quality crisis. These global concerns are addressing water sector operators to smart technological solutions that realize the so-called smart drinking water and wastewater networks. Water quality preservation is one of the essential services that smart water utilities have to guaranteed. The water quality monitoring systems include a variety of in situ sensors with several sensor protocols and interfaces. Sensor integration as well as real time sensor readings accessibility and interoperability across the interconnected layers of functionality needed for a comprehensive smart water network solution are the challenges should be tackled. The objective of this research work has been to develop a standardized OGC SWE (Sensor Web Enablement) architecture that enables the integration and real time access to the various continuous and networked sensors can be installed along drinking water and wastewater networks, and real time sensor data browsing, querying and analyzing capabilities across the components of a smart water network solution. Furthermore, a web based geo-console and a QGIS SOS client application have been developed ad hoc for supporting utilities to effectively manage their water treatment and optimize quality-testing processes.

A Semi-Supervised Learning Approach to Artificial Olfaction

In the last decade, semi-supervised learning (SSL) has gained an increasing attention in machine learning. SSL may obtain performance gains by adding to the supervised information, provided by a limited labelled training set, the information content embedded in an unsupervised sample set. This may be very helpful, since obtaining supervised samples can be difficult and costly, as in several artificial olfaction (AO) problems. In this work, co-training style semi-supervised algorithms are applied to air pollution monitoring, an on-field artificial olfaction problem. The primary purpose is to adapt a regressor knowledge to the well known sensors and concept drift issues that characterize the use of solid state chemical sensors in harsh environments. The response of an array of solid state chemical sensors, located in a city street affected by heavy cars traffic, has been monitored for more than 1year and used to estimate hourly pollutants concentrations. Conventional analyzers provided the needed ground truth. Results obtained by the proposed approach show that it can both reduce the number of labeled samples needed for the multivariate calibration of the device and the performance decay due to drift effects.

Power Savvy Wireless E‐Nose Network using In‐Network Intelligence

Fluid dynamics effects make single point of measure not a viable solution for toxic/dangerous gases detection both in complex indoor and outdoor environments. Efficient distributed chemical sensing needs reliable wireless battery operated devices capable to operate for long time performing detection and quantification of gases in complex mixtures. Single solid state sensor approach would suffer form interferents influences, enose platform would help to reduce this problem but power consumption is still an issue. Dramatic reduction in this parameter can be achieved by making an e‐nose platform capable to perform local estimations avoiding unnecessary data transmission towards datasinks. Here we present the results of experimenting local sensor fusion algorithms implementation on the ENEA Tinynose platform, a wireless e‐nose based on room temperature operating sensors and TinyOS over TelosB commercial platform. Results show how the w‐nose was able to infer local qualitative and quantitative estimations for ...

Computational Intelligence for Smart Air Quality Monitors Calibration

Machine learning techniques will take an increasingly central role in the distributed sensing realm and specifically in smart cities scenarios. Pervasive air quality monitoring as one of the primary city service requires a significant effort in term of data processing for extracting the needed semantic value. In this paper, after briefly reviewing the emerging relevant literature, we compare several machine learning tools for the purpose of devising intelligent calibration components to be run on board or in cloud computing architectures for pollutant concentration estimation. Two cities field experiments provide the needed on field recorded datasets to validate the approaches. Results are discussed both in terms of performance and computational impact for the specific application.

Evaluation and design of a rain gauge network using a statistical optimization method in a severe hydro-geological hazard prone area

Rainfall data collection gathered in continuous by a distributed rain gauge network is instrumental to more effective hydro-geological risk forecasting and management services though the input estimated rainfall fields suffer from prediction uncertainty. Optimal rain gauge networks can generate accurate estimated rainfall fields. In this research work, a methodology has been investigated for evaluating an optimal rain gauges network aimed at robust hydrogeological hazard investigations. The rain gauges of the Sarno River basin (Southern Italy) has been evaluated by optimizing a two-objective function that maximizes the estimated accuracy and minimizes the total metering cost through the variance reduction algorithm along with the climatological variogram (time-invariant). This problem has been solved by using an enumerative search algorithm, evaluating the exact Pareto-front by an efficient computational time.

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.