Institutes & CentersCenter for Sensing Solutions - Advanced Computing - Projects - DPS4ESLAB

DPS4ESLAB

Datenplattform und Sensorentechnologie für das ESLAB

Die Forschung für das Umweltmonitoring in Südtirol erfordert den Zugriff auf heterogene Informationen von Umweltsensoren.

Das Environmental Sensing LAB (https://www.eurac.edu/en/institutes-centers/center-for-sensing-solutions/pages/infrastructure-ss) im Technologiepark Bozen - NOI ist eine innovative Forschungsinfrastruktur, die aus einer gemeinsamen Initiative der Freien Universität Bozen und der Eurac Research hervorgegangen ist und sich der Entwicklung innovativer Lösungen für das Umweltmonitoring und die Automatisierung von Sensormessungen widmet. Die Infrastruktur nutzt innovative Technologien in Richtung IoT, sowie eine gemeinsame Plattform für die Organisation und Nutzung von Umweltdaten zum Nutzen von Eurac und LUB, anderen wissenschaftlichen Partnern in der Provinz und außerhalb, privaten Unternehmen und der öffentlichen Verwaltung.

Das Projekt DPS4ESLAB - Data Platform and Sensing Technologies for ESLAB - zielt darauf ab, die Arbeiten zur Schaffung des Labors ES LAB abzuschließen, Räume für (i) die Charakterisierung von Umweltsensoren, (ii) die Integration, Verarbeitung und Verteilung von Signalen und Daten und (iii) die Validierung und Verifizierung der Funktionalität integrierter Systeme und Überwachungsanwendungen einzurichten. Darüber hinaus sieht das Projekt die Stärkung der Dateninfrastruktur für die Forschung durch die Entwicklung einer interoperablen und föderierten Plattform für Umweltdaten vor, die die Sammlung, Analyse und Verbreitung von Daten, Produkten und Dienstleistungen für die Umweltüberwachung in verschiedenen Fallstudien ermöglicht.

Kontaktperson: Roberto Monsorno

roberto.monsorno@eurac.edu

Project duration: May 2018 - September 2021
Project status:
Funding:
FESR (EU funding / Project)
Website: https://noi.bz.it/en/dps4eslab
Institutes: Center for Sensing Solutions, Institut für Erdbeobachtung, Institut für Alpine Umwelt, Center for Climate Change and Transformation

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Scientific Publications

Journal article2023

Surface Textured Double Layer Triboelectric Nanogenerator for Autonomous and Ultra‐Sensitive Biomedical Sensing

Riaz R, Dudem B, Costa-Angeli MA, Douaki A, Ahmad M, Mejia‐Aguilar A, Monsorno R, Lugli P,

Roberto Monsorno

Triboelectric nanogenerators (TENGs) have demonstrated great promise, especially for the realization of self-powered biomedical sensors. Nevertheless, developing TENG sensors able to detect the broad range of biomechanical movements experienced on the human body is still a challenge. Herein, a unique ridge-structured device sensitive to wide range of forces is reported (i.e., low-forced pulse monitoring to high-forced gait monitoring). The device is composed of thermoplastic polyurethane layer sandwiched between two textured silicon elastomeric layers. Compared to non-textured surface configurations, the proposed ridged-structure provides an increased frictional contact area between the triboelectric materials, while also acting as a spacer between the triboelectric materials. The influence of ridge dimensions on the output performance is investigated by mechanical simulations and electromechanical experimental tests. The optimized device shows a maximum peak output power and current densities of 490 mW m−2and 1750μAm−2, respectively at 30 N and 7 Hz of compressive forces. The proposed device exhibits stable electrical output for 10000 cycles. As a proof of concept, the proposed device is used as wearable sensors for monitoring pulse rate, breath patterns, and gait movements. The study suggests the possibility of utilization of novel-structured sandwich-type elastomer ridge-based TENG in different aspects of biomedical sensing and smart wearable application

DOI Quelle

DOI: 10.1002/admt.202202106

Journal article2022

Numerical and Experimental Characterization of LoRa-Based Helmet-to-Unmanned Aerial Vehicle Links on Flat Lands: A Numerical-Statistical Approach to Link Modeling

Bianco GM, Mejia-Aguilar A, Marrocco G

The use of the LoRa communication protocol in a new generation of transceivers is attractive for search and rescue (SaR) procedures because they can operate in harsh environments covering vast areas while maintaining a low power consumption. The possibility of wearing helmets equipped with LoRa-radios and installing LoRa transceivers in unmanned aerial vehicles (UAVs) will accelerate the localization of the targets, probably unconscious. In this paper, the achievable communication ranges of such links are theoretically and experimentally evaluated by considering the possible positions of the helmet wearer (standing or lying) on a flat field, representing a simple SaR scenario. Simulations and experimental tests demonstrated that, for the standing position, the ground-bounce multi-path produces strong fluctuations of the received power versus the Tx-Rx distances. Such fluctuations can be kept confined within 100 m from the target by lowering the UAV altitude. Instead, for a more critical lying position, the received power profile is monotonic and nearly insensitive to the posture. For all the considered cases, the signal emitted by the body-worn transceiver can be exploited to localize the helmet wearer based on its strength, and it is theoretically detectable by the UAV radio up to 5 km on flat terrain.

DOI Quelle

DOI: 10.1109/MAP.2022.3176590

Conference proceedings article2021

Flexible, biocompatible, and ridged silicone elastomers based robust sandwich-type triboelectric nanogenerator

Riaz R, Dudem B, Costa M, Douaki A, Ahmed M, Petrelli M, Mejia-Aguilar A, Monsorno R,

Roberto Monsorno

Energy harvesters for smart wearables are gaining increasing attention world-wide. Among the various type of options available, triboelectric nanogenerators harvesting mechanical energy from human body movements are especially attractive. In this work, a flexible sandwich-type triboelectric nanogenerator (FS-TENG) based on ridged and biocompatible silicone elastomers and thermoplastic polyurethane is proposed. The proposed FS-TENG exhibits the open-circuit voltages and maximum power density of ~80 volts and 0.35 mW/cm2,
respectively. This preliminary performance already makes the device extremely promising to power low energy smart wearable devices, as well as monitor pressure in bespoke biomedical applications

DOI Quelle

DOI: 10.1109/IFETC49530.2021.9580514

Journal article2021

LoRa System for Search and Rescue: Path Loss Models and Procedures in Mountain Scenarios

Bianco GM, Giuliano R, Marrocco G, Mazzenga F, Mejia-Aguilar A

Typical mountain Search and Rescue (SaR) operations require the localization of the persons involved in accidents in harsh environments. ARVA and RECCO are the current standards for the localization in snowy environments although their radio range is limited to some tens of meters. In this paper, we prove by experimental results that the Long Range (LoRa) Low-Power Wide-Area Network (LPWAN) technology is very promising for SaR applications due to its extended radio range. A LoRa-based system for SaR operations is presented and analyzed. The localization of the persons is obtained through an algorithm based on path loss measurements. Radio path loss models of body-worn LoRa devices in harsh mountain environments are derived by measurements. We observed that, although the communication range of LoRa decreases from kilometres to hundreds of meters in the tested environments, at least 50% of the transmitted packets can be received at distances about five times greater than those achievable with golden standard technologies such as ARVA. The performances of the considered localization algorithm are analyzed on the basis of the collected data. The achievable accuracy is in the order of meters around the true position for a relatively large number of available path loss measurements. Lastly, we propose and detail a LoRa-based system for SaR operations

DOI Quelle

DOI: 10.1109/JIOT.2020.3017044

All Publications

Innovating Alpine Agriculture: Recent and Future R&D Advancements in Precision Pest and Disease Identification through Intelligent Sensors and Traps
Monsorno R (2024)
Vortrag

Conference: Agri-Automation in agriculture | Bolzano | 7.3.2024 - 7.3.2024

Weitere Informationen: https://noi.bz.it/en/events/agri-automation-automation-i ...

Surface Textured Double Layer Triboelectric Nanogenerator for Autonomous and Ultra‐Sensitive Biomedical Sensing
Riaz R, Dudem B, Costa-Angeli MA, Douaki A, Ahmad M, Mejia‐Aguilar A, Monsorno R, Lugli P, Silva R, Petti L (2023)
Wiley
Zeitschriftenartikel
Advanced Materials Technologies

Weitere Informationen: http://dx.doi.org/10.1002/admt.202202106

https://doi.org/10.1002/admt.202202106

Numerical and Experimental Characterization of LoRa-Based Helmet-to-Unmanned Aerial Vehicle Links on Flat Lands: A Numerical-Statistical Approach to Link Modeling
Bianco GM, Mejia-Aguilar A, Marrocco G (2022)
Institute of Electrical and Electronics Engineers (IEEE)
Zeitschriftenartikel
IEEE Antennas and Propagation Magazine

Weitere Informationen: https://doi.org/10.1109/MAP.2022.3176590

https://doi.org/10.1109/MAP.2022.3176590

Our partners

Business Location Südtirol · Alto Adige AG

Project Team

Roberto Monsorno

Project Manager

Abraham Mejia Aguilar

Vice Manager

Mattia Rossi

Team Member

Racheline Bonadio

Giacomo Bertoldi

Stefano Tondini

Armin Costa

Simone Tritini

Bartolomeo Ventura

Andrea Vianello

Alice Crespi

Marc Zebisch

Alexander Jacob

Michele Claus

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DPS4ESLAB