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Metrology for Living Environment

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 29213

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Dr. Francesco Lamonaca

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1. Department of Computer Engineering, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, 87036 Rende, CS, Italy
2. CNR-NANOTEC, 87036 Rende, CS, Italy
Interests: measurements; distributed measurement systems; measurement and monitoring systems based on the IoT; measurement and monitoring systems based on AI; wireless sensor network; synchronization of measurement instruments and sensors; non-invasive measurements; non-destructive testing
Special Issues, Collections and Topics in MDPI journals

Dr. Michela Ricca

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Guest Editor

Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, 87036 Cosenza, Italy
Interests: cultural heritage; characterization and diagnostics of stone building materials and their decay processes; experimentation of innovative protective products for materials; archaeometry; underwater archaeology
Special Issues, Collections and Topics in MDPI journals

Dr. Domenico Carnì

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Guest Editor

Department of Informatics, Modelling, Electronics and Systems Science, University of Calabria, 87036 Arcavacata, Italy
Interests: measurements; structural health monitoring; noninvasive monitoring; distributed monitoring; Internet of Things; time synchronization; wireless sensor network; signal processing; automatic classifiers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2022 IEEE International Workshop on Metrology for Living Environment (https://metrolivenv.org/) will be held in Cosenza, Italy, on 25–27 May 2022.

The authors of papers presented at the workshop related to Sensors are invited to submit extended versions of their work to this Special Issue for publication. The IEEE MetroLivEnv 2022 aims to be a solid reference of the technical community to present and discuss the most recent results of scientific and technological research for the living environment, with particular emphasis on applications and new trends.

Attention is paid, but not limited to, on new technologies for metrology assisted solutions for design, construction, efficient, safe, comfortable and healthy operation of the built environment including active and assisted living (AAL). Innovative solutions can be based on the IoT paradigm, BIM, sensors, signal processing, data analytics, artificial intelligence, sensor networks, interoperability standards.

Topics:

Building diagnostic during and after constructions;
IoT based monitoring systems;
Measurements for BIM and digital twins;
Indoor environmental quality;
Sensors and sensor networks for smart buildings;
Robots in living environment;
Unmanned systems for living environment monitoring;
Comfort and well being;
Active and assisted living;
Building energy performance assessment;
Use of artificial intelligence for living environment measurements;
Infrared and hyperspectral monitoring system for living environment;
Historical buildings and cultural heritage;
Standards and norms for measurements in built environment;
Uncertainty models for decision making.

Dr. Francesco Lamonaca
Dr. Michela Ricca
Dr. Domenico Carnì
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (12 papers)

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Research

15 pages, 966 KiB

Open AccessArticle

Local Distributed Node for Power Quality Event Detection Based on Multi-Sine Fitting Algorithm

by Domenico Luca Carní and Francesco Lamonaca

Sensors 2024, 24(8), 2474; https://0-doi-org.brum.beds.ac.uk/10.3390/s24082474 - 12 Apr 2024

Viewed by 237

Abstract

The new power generation systems, the increasing number of equipment connected to the power grid, and the introduction of technologies such as the smart grid, underline the importance and complexity of the Power Quality (PQ) evaluation. In this scenario, an Automatic PQ Events Classifier (APQEC) that detects, segments, and classifies the anomaly in the power signal is needed for the timely intervention and maintenance of the grid. Due to the extension and complexity of the network, the number of points to be monitored is large, making the cost of the infrastructure unreasonable. To reduce the cost, a new architecture for an APQEC is proposed. This architecture is composed of several Locally Distributed Nodes (LDNs) and a Central Classification Unit (CCU). The LDNs are in charge of the acquisition, the detection of PQ events, and the segmentation of the power signal. Instead, the CCU receives the information from the nodes to classify the PQ events. A low-computational capability characterizes low-cost LDNs. For this reason, a suitable PQ event detection and segmentation method with low resource requirements is proposed. It is based on the use of a sliding observation window that establishes a reasonable time interval, which is also useful for signal classification and the multi-sine fitting algorithm to decompose the input signal in harmonic components. These components can be compared with established threshold values to detect if a PQ event occurs. Only in this case, the signal is sent to the CCU for the classification; otherwise, it is discarded. Numerical tests are performed to set the sliding window size and observe the behavior of the proposed method with the main PQ events presented in the literature, even when the SNR varies. Experimental results confirm the effectiveness of the proposal, highlighting the correspondence with numerical results and the reduced execution time when compared to FFT-based methods. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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19 pages, 5509 KiB

Open AccessArticle

A Multi-Sensor Fusion Approach Based on PIR and Ultrasonic Sensors Installed on a Robot to Localise People in Indoor Environments

by Ilaria Ciuffreda, Sara Casaccia and Gian Marco Revel

Sensors 2023, 23(15), 6963; https://0-doi-org.brum.beds.ac.uk/10.3390/s23156963 - 05 Aug 2023

Cited by 5 | Viewed by 1769

Abstract

This work illustrates an innovative localisation sensor network that uses multiple PIR and ultrasonic sensors installed on a mobile social robot to localise occupants in indoor environments. The system presented aims to measure movement direction and distance to reconstruct the movement of a person in an indoor environment by using sensor activation strategies and data processing techniques. The data collected are then analysed using both a supervised (Decision Tree) and an unsupervised (K-Means) machine learning algorithm to extract the direction and distance of occupant movement from the measurement system, respectively. Tests in a controlled environment have been conducted to assess the accuracy of the methodology when multiple PIR and ultrasonic sensor systems are used. In addition, a qualitative evaluation of the system’s ability to reconstruct the movement of the occupant has been performed. The system proposed can reconstruct the direction of an occupant with an accuracy of 70.7% and uncertainty in distance measurement of 6.7%. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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28 pages, 11710 KiB

Open AccessArticle

A Wireless Sensor Network for Residential Building Energy and Indoor Environmental Quality Monitoring: Design, Instrumentation, Data Analysis and Feedback

by Mathieu Bourdeau, Julien Waeytens, Nedia Aouani, Philippe Basset and Elyes Nefzaoui

Sensors 2023, 23(12), 5580; https://0-doi-org.brum.beds.ac.uk/10.3390/s23125580 - 14 Jun 2023

Cited by 5 | Viewed by 1700

Abstract

This article outlines the implementation and use of a large wireless instrumentation solution to collect data over a long time period of a few years for three collective residential buildings. The sensor network consists of a variety of 179 sensors deployed in building common areas and in apartments to monitor energy consumption, indoor environmental quality, and local meteorological conditions. The collected data are used and analyzed to assess the building performance in terms of energy consumption and indoor environmental quality following major renovation operations on the buildings. Observations from the collected data show energy consumption of the renovated buildings in agreement with expected energy savings calculated by an engineering office, many different occupancy patterns mainly related to the professional situation of the households, and seasonal variation in window opening rates. The monitoring was also able to detect some deficiencies in the energy management. Indeed, the data reveal the absence of time-of-day-dependent heating load control and higher than expected indoor temperatures because of a lack of occupant awareness on energy savings, thermal comfort, and the new technologies installed during the renovation such as thermostatic valves on the heaters. Lastly, we also provide feedback on the performed sensor network from the experiment design and choice of measured quantities to data communication, through the sensors’ technological choices, implementation, calibration, and maintenance. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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25 pages, 13229 KiB

Open AccessArticle

Prognostic Health Management Using IR Thermography: The Case of a Digital Twin of a NiTi Endodontic File

by Filippo Ruffa, Mariacarla Lugarà, Gaetano Fulco, Damiano Alizzio, Fabio Lo Savio and Claudio De Capua

Sensors 2023, 23(9), 4296; https://0-doi-org.brum.beds.ac.uk/10.3390/s23094296 - 26 Apr 2023

Viewed by 1510

Abstract

Prognostic and health management technologies are increasingly important in many fields where reducing maintenance costs is critical. Non-destructive testing techniques and the Internet of Things (IoT) can help create accurate, two-sided digital models of specific monitored objects, enabling predictive analysis and avoiding risky situations. This study focuses on a particular application: monitoring an endodontic file during operation to develop a strategy to prevent breakage. To this end, the authors propose an innovative, non-invasive technique for early fault detection based on digital twins and infrared thermography measurements. They developed a digital twin of a NiTi alloy endodontic file that receives measurement data from the real world and generates the expected thermal map of the object under working conditions. By comparing this virtual image with the real one acquired by an IR camera, the authors were able to identify an anomalous trend and avoid breakage. The technique was calibrated and validated using both a professional IR camera and an innovative low-cost IR scanner previously developed by the authors. By using both devices, they could identify a critical condition at least 11 s before the file broke. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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20 pages, 2915 KiB

Open AccessArticle

An Improvement Strategy for Indoor Air Quality Monitoring Systems

by Claudio De Capua, Gaetano Fulco, Mariacarla Lugarà and Filippo Ruffa

Sensors 2023, 23(8), 3999; https://0-doi-org.brum.beds.ac.uk/10.3390/s23083999 - 14 Apr 2023

Cited by 8 | Viewed by 2588

Abstract

Air quality has a huge impact on the comfort and healthiness of various environments. According to the World Health Organization, people who are exposed to chemical, biological and/or physical agents in buildings with low air quality and poor ventilation are more prone to be affected by psycho-physical discomfort, respiratory tract and central nervous system diseases. Moreover, in recent years, the time spent indoors has increased by around 90%. If we consider that respiratory diseases are mainly transmitted from human to human through close contact, airborne respiratory droplets and contaminated surfaces, and that there is a strict relationship between air pollution and the spread of the diseases, it becomes even more necessary to monitor and control these environmental conditions. This situation has inevitably led us to consider renovating buildings with the aim of improving both the well-being of the occupants (safety, ventilation, heating) and the energy efficiency, including monitoring the internal comfort using sensors and the IoT. These two objectives often require opposite approaches and strategies. This paper aims to investigate indoor monitoring systems to increase the quality of life of occupants, proposing an innovative approach consisting of the definition of new indices that consider both the concentration of the pollutants and the exposure time. Furthermore, the reliability of the proposed method was enforced using proper decision-making algorithms, which allows one to consider measurement uncertainty during decisions. Such an approach allows for greater control over the potentially harmful conditions and to find a good trade-off between well-being and the energy efficiency objectives. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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21 pages, 5105 KiB

Open AccessArticle

Low-Cost Internet-of-Things Water-Quality Monitoring System for Rural Areas

by Razvan Bogdan, Camelia Paliuc, Mihaela Crisan-Vida, Sergiu Nimara and Darius Barmayoun

Sensors 2023, 23(8), 3919; https://0-doi-org.brum.beds.ac.uk/10.3390/s23083919 - 12 Apr 2023

Cited by 7 | Viewed by 8060

Abstract

Water is a vital source for life and natural environments. This is the reason why water sources should be constantly monitored in order to detect any pollutants that might jeopardize the quality of water. This paper presents a low-cost internet-of-things system that is capable of measuring and reporting the quality of different water sources. It comprises the following components: Arduino UNO board, Bluetooth module BT04, temperature sensor DS18B20, pH sensor—SEN0161, TDS sensor—SEN0244, turbidity sensor—SKU SEN0189. The system will be controlled and managed from a mobile application, which will monitor the actual status of water sources. We propose to monitor and evaluate the quality of water from five different water sources in a rural settlement. The results show that most of the water sources we have monitored are proper for consumption, with a single exception where the TDS values are not within proper limits, as they outperform the maximum accepted value of 500 ppm. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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14 pages, 679 KiB

Open AccessArticle

Tackling Age of Information in Access Policies for Sensing Ecosystems

by Alberto Zancanaro, Giulia Cisotto and Leonardo Badia

Sensors 2023, 23(7), 3456; https://0-doi-org.brum.beds.ac.uk/10.3390/s23073456 - 25 Mar 2023

Cited by 1 | Viewed by 1102

Abstract

Recent technological advancements such as the Internet of Things (IoT) and machine learning (ML) can lead to a massive data generation in smart environments, where multiple sensors can be used to monitor a large number of processes through a wireless sensor network (WSN). This poses new challenges for the extraction and interpretation of meaningful data. In this spirit, age of information (AoI) represents an important metric to quantify the freshness of the data monitored to check for anomalies and operate adaptive control. However, AoI typically assumes a binary representation of the information, which is actually multi-structured. Thus, deep semantic aspects may be lost. In addition, the ambient correlation of multiple sensors may not be taken into account and exploited. To analyze these issues, we study how correlation affects AoI for multiple sensors under two scenarios of (i) concurrent and (ii) time-division multiple access. We show that correlation among sensors improves AoI if concurrent transmissions are allowed, whereas the benefits are much more limited in a time-division scenario. Furthermore, we discuss how ML can be applied to extract relevant information from data and show how it can further optimize the transmission policy with savings of resources. Specifically, we demonstrate, through simulations, that ML techniques can be used to reduce the number of transmissions and that classification errors have no influence on the AoI of the system. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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22 pages, 3871 KiB

Open AccessArticle

Long-Term Thermal Comfort Monitoring via Wearable Sensing Techniques: Correlation between Environmental Metrics and Subjective Perception

by Veronica Martins Gnecco, Ilaria Pigliautile and Anna Laura Pisello

Sensors 2023, 23(2), 576; https://0-doi-org.brum.beds.ac.uk/10.3390/s23020576 - 04 Jan 2023

Cited by 12 | Viewed by 1778

Abstract

The improvement of comfort monitoring resources is pivotal for a better understanding of personal perception in indoor and outdoor environments and thus developing personalized comfort models maximizing occupants’ well-being while minimizing energy consumption. Different daily routines and their relation to the thermal sensation remain a challenge in long-term monitoring campaigns. This paper presents a new methodology to investigate the correlation between individuals’ daily Thermal Sensation Vote (TSV) and environmental exposure. Participants engaged in the long-term campaign were instructed to answer a daily survey about thermal comfort perception and wore a device continuously monitoring temperature and relative humidity in their surroundings. Normalized daily profiles of monitored variables and calculated heat index were clustered to identify common exposure profiles for each participant. The correlation between each cluster and expressed TSV was evaluated through the Kendall tau-b test. Most of the significant correlations were related to the heat index profiles, i.e., 49% of cases, suggesting that a more detailed description of physical boundaries better approximates expressed comfort. This research represents the first step towards personalized comfort models accounting for individual long-term environmental exposure. A longer campaign involving more participants should be organized in future studies, involving also physiological variables for energy-saving purposes. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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17 pages, 5766 KiB

Open AccessArticle

Performance Evaluation of an IoT Sensor Node for Health Monitoring of Artwork and Ancient Wooden Structures

by Ada Fort, Elia Landi, Marco Mugnaini, Lorenzo Parri and Valerio Vignoli

Sensors 2022, 22(24), 9794; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249794 - 13 Dec 2022

Cited by 2 | Viewed by 1368

Abstract

In this paper, an IoT sensor node, based on smart Bluetooth low energy (BLE), for the health monitoring of artworks and large wooden structures is presented. The measurements from sensors on board the node are collected in real-time and sent to a remote gateway. The sensor node allows for the monitoring of environmental parameters, in particular, temperature and humidity, with accurate and robust integrated sensors. The developed node also embeds an accelerometer, which also allows other mechanical quantities (such as tilt) to be derived. This feature can be exploited to perform structural monitoring, exploiting the processing of data history to detect permanent displacements or deformations. The node is triggered by acceleration transients; therefore, it can also generate alarms related to shocks. This feature is crucial, for instance, in the case of transportation. The developed device is low-cost and has very good performance in terms of power consumption and compactness. A reliability assessment showed excellent durability, and experimental tests proved very satisfactory robustness against working condition variations. The presented results confirm that the developed device allows for the realization of pervasive monitoring systems, in the context of the IoT paradigm, with sensor nodes devoted to the monitoring of each artwork present in a museum or in a church. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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19 pages, 7355 KiB

Open AccessArticle

Statistical Study on Human Temperature Measurement by Infrared Thermography

by Michal Švantner, Vladislav Lang, Jiří Skála, Tomáš Kohlschütter, Milan Honner, Lukáš Muzika and Eliška Kosová

Sensors 2022, 22(21), 8395; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218395 - 01 Nov 2022

Cited by 9 | Viewed by 2586

Abstract

Increased temperature in humans is the symptom of many infectious diseases and it is thus an important diagnostic tool. Infrared temperature measurement methods have been developed and applied over long periods due to their advantage of non-contact and fast measurements. This study deals with a statistical evaluation of the possibilities and limitations of infrared/thermographic human temperature measurement. A short review of the use of infrared temperature measurement in medical applications is provided. Experiments and statistics-based evaluation to confirm the expected accuracy and limits of thermography-based human temperature measurement are introduced. The results presented in this study show that the standard deviation of the thermographic measurement of the eyes maximum temperature was 0.4–0.9 °C and the mean values differences from the armpit measurement were up to 0.5 °C, based on the used IR camera, even though near ideal measurement conditions and permanent blackbody correction were used. It was also shown that a certain number of outliers must be assumed in such measurements. Extended analyses including simulations of true negative/false positive, sensitivity/specificity and receiver operating characteristics (ROC) curves are presented. The statistical evaluation as well as the extended analyses show that maximum eyes temperature is more relevant than a forehead temperature examination. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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20 pages, 31105 KiB

Open AccessArticle

Evaluating a Novel Gas Sensor for Ambient Monitoring in Automated Life Science Laboratories

by Mohammed Faeik Ruzaij Al-Okby, Thomas Roddelkopf, Heidi Fleischer and Kerstin Thurow

Sensors 2022, 22(21), 8161; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218161 - 25 Oct 2022

Cited by 4 | Viewed by 2358

Abstract

Air pollution and leakages of hazardous and toxic gases and chemicals are among the dangers that frequently occur at automated chemical and life science laboratories. This type of accident needs to be processed as soon as possible to avoid the harmful side effects that can happen when a human is exposed. Nitrogen oxides and volatile organic compounds are among the most prominent indoor air pollutants, which greatly affect the lifestyles in these places. In this study, a commercial MOX gas sensor, SGP41, was embedded in an IoT environmental sensor node for hazardous gas detection and alarm. The sensor can detect several parameters, including nitrogen oxide index (NOx-Index) and volatile organic compound index (VOC-Index). Several tests were conducted to detect the leakage of nitrogen oxides and volatile organic compounds in different concentrations and volumes, as well as from different leakage distances, to measure the effect of these factors on the response speed and recovery time of the sensors used. These factors were also compared between the different sensors built into the sensor node to give a comprehensive picture of the system used. The system testing results revealed that the SGP41 sensor is capable of implementing the design purposes for the target parameters, can detect a small NO₂ gas leakage starting from 0.3% volume, and can detect all the tested VOC solvents ≥ 100 µL Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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20 pages, 1003 KiB

Open AccessArticle

Dynamic Segmentation of Sensor Events for Real-Time Human Activity Recognition in a Smart Home Context

by Houda Najeh, Christophe Lohr and Benoit Leduc

Sensors 2022, 22(14), 5458; https://0-doi-org.brum.beds.ac.uk/10.3390/s22145458 - 21 Jul 2022

Cited by 8 | Viewed by 2017

Abstract

Human activity recognition (HAR) is fundamental to many services in smart buildings. However, providing sufficiently robust activity recognition systems that could be confidently deployed in an ordinary real environment remains a major challenge. Much of the research done in this area has mainly focused on recognition through pre-segmented sensor data. In this paper, real-time human activity recognition based on streaming sensors is investigated. The proposed methodology incorporates dynamic event windowing based on spatio-temporal correlation and the knowledge of activity trigger sensor to recognize activities and record new events. The objective is to determine whether the last event that just happened belongs to the current activity, or if it is the sign of the start of a new activity. For this, we consider the correlation between sensors in view of what can be seen in the history of past events. The proposed algorithm contains three steps: verification of sensor correlation (SC), verification of temporal correlation (TC), and determination of the activity triggering the sensor. The proposed approach is applied to a real case study: the “Aruba” dataset from the CASAS database. F1 score is used to assess the quality of the segmentation. The results show that the proposed approach segments several activities (sleeping, bed to toilet, meal preparation, eating, housekeeping, working, entering home, and leaving home) with an F1 score of 0.63–0.99. Full article

(This article belongs to the Special Issue Metrology for Living Environment)

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