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Application of Wireless Sensor Networks and Remote Monitoring Systems in Smart Buildings

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 7310

Special Issue Editors


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Guest Editor
Faculty of Engineering, Università eCampus, Via Isimbardi 10, 22060 Novedrate, Italy
Interests: measurements; sensors, IR sensors; wearable sensors; thermal comfort; indoor air quality; buildings monitoring; signal processing; data analysis; energy efficiency
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, v. Brecce Bianche 12, 60131 Ancona, Italy
Interests: non-invasive measurement techniques; measurement procedures; measurement uncertainty; wearable sensors; physiological signals; comfort and wellbeing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The continuous monitoring of buildings is a pivotal element in the management of structures and infrastructures, in a view to optimizing their life cycle (thus reducing maintenance costs), as well as enhancing energy efficiency and the occupants’ well-being. Indeed, wireless sensor networks and monitoring systems can be designed in order to enable multidomain data gathering for optimized buildings’ design and operation, measuring: structural quantities, energy consumption, envelope performance, indoor environmental quality, and occupants’ behaviours. Hence, several types of sensors can be installed in order to make a building “smart”, and thus able to perceive diverse quantities and share them remotely, starting with traditional sensors for structural health monitoring (SHM, e.g., accelerometers, fibre optics sensors, electrical impedance sensors, etc.) to environmental sensors (e.g., thermometers, hygrometers, etc.) through non-contact sensors acquiring signals related to occupants (e.g., thermal cameras, passive infrared—PIR—occupancy sensors, etc.).

On the other hand, thanks to Internet-of-Things (IoT) technologies, it is possible to realize remote monitoring systems (also composed by different wireless sensing nodes, placed in strategic positions) capable to share data on Cloud services. This plethora of different signals can also be used as inputs for artificial intelligence (AI) algorithms for classification of prediction purposes, with the aim of depicting the building status from different perspectives, such as structural integrity, thermal comfort, energy consumption, and so on.

It is clear that data quality is of utmost importance to obtain reliable results, usable as support for control, maintenance, or management strategies and decision-making processes. Hence, optimizing the whole measurement chain, from the hardware choice and installation to AI algorithms optimization through signal processing techniques is fundamental to minimising measurement uncertainty and also to characterize the entire system from a metrological point of view.

This Special Issue intendeds to publish original research and review papers dealing with wireless sensor networks and monitoring systems thought for buildings, not limited to structural monitoring but also aiming at optimizing the energy efficiency and occupants’ well-being. Particular attention is paid to the aspects concerning measurement uncertainty and metrological characterization of sensors.

Suitable topics include, but are not limited to, the following ones:

  • Wireless sensor network for buildings;
  • Sensors for BIM and digital twin;
  • Remote monitoring systems;
  • Data fusion for building monitoring;
  • Artificial intelligence technologies applied to smart buildings monitoring;
  • Measurement and assessment of indoor environmental quality and well-being;
  • Self-sensing techniques.

Dr. Marco Arnesano
Dr. Gloria Cosoli
Guest Editors

Manuscript Submission Information

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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.

Keywords

  • wireless sensor network
  • monitoring systems and techniques
  • measurement uncertainty
  • IoT
  • indoor comfort
  • energy efficiency
  • indoor environmental quality (IEQ)

Published Papers (4 papers)

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Research

23 pages, 20107 KiB  
Article
Multi-Sensor Device for Traceable Monitoring of Indoor Environmental Quality
by Virginia Isabella Fissore, Giuseppina Arcamone, Arianna Astolfi, Alberto Barbaro, Alessio Carullo, Pietro Chiavassa, Marina Clerico, Stefano Fantucci, Franco Fiori, Davide Gallione, Edoardo Giusto, Alice Lorenzati, Nicole Mastromatteo, Bartolomeo Montrucchio, Anna Pellegrino, Gabriele Piccablotto, Giuseppina Emma Puglisi, Gustavo Ramirez-Espinosa, Erica Raviola, Antonio Servetti and Louena Shtrepiadd Show full author list remove Hide full author list
Sensors 2024, 24(9), 2893; https://doi.org/10.3390/s24092893 - 1 May 2024
Viewed by 2591
Abstract
The Indoor Environmental Quality (IEQ) combines thermal, visual, acoustic, and air-quality conditions in indoor environments and affects occupants’ health, well-being, and comfort. Performing continuous monitoring to assess IEQ is increasingly proving to be important, also due to the large amount of time that [...] Read more.
The Indoor Environmental Quality (IEQ) combines thermal, visual, acoustic, and air-quality conditions in indoor environments and affects occupants’ health, well-being, and comfort. Performing continuous monitoring to assess IEQ is increasingly proving to be important, also due to the large amount of time that people spend in closed spaces. In the present study, the design, development, and metrological characterization of a low-cost multi-sensor device is presented. The device is part of a wider system, hereafter referred to as PROMET&O (PROactive Monitoring for indoor EnvironmenTal quality & cOmfort), that also includes a questionnaire for the collection of occupants’ feedback on comfort perception and a dashboard to show end users all monitored data. The PROMET&O multi-sensor monitors the quality conditions of indoor environments thanks to a set of low-cost sensors that measure air temperature, relative humidity, illuminance, sound pressure level, carbon monoxide, carbon dioxide, nitrogen dioxide, particulate matter, volatile organic compounds, and formaldehyde. The device architecture is described, and the design criteria related to measurement requirements are highlighted. Particular attention is paid to the calibration of the device to ensure the metrological traceability of the measurements. Calibration procedures, based on the comparison to reference standards and following commonly employed or ad hoc developed technical procedures, were defined and applied to the bare sensors of air temperature and relative humidity, carbon dioxide, illuminance, sound pressure level, particulate matter, and formaldehyde. The next calibration phase in the laboratory will be aimed at analyzing the mutual influences of the assembled multi-sensor hardware components and refining the calibration functions. Full article
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18 pages, 1772 KiB  
Article
Enhancing Occupant Comfort and Building Sustainability: Lessons from an Internet of Things-Based Study on Centrally Controlled Indoor Shared Spaces in Hot Climatic Conditions
by Parag Kulkarni, Bivin Pradeep, Rahemeen Yusuf, Henry Alexander and Hesham ElSayed
Sensors 2024, 24(5), 1406; https://doi.org/10.3390/s24051406 - 22 Feb 2024
Viewed by 768
Abstract
It is well known that buildings have a sizeable energy and environmental footprint. In particular, in environments like university campuses, the occupants as well as occupancy in shared spaces varies over time. Systems for cooling in such environments that are centrally controlled are [...] Read more.
It is well known that buildings have a sizeable energy and environmental footprint. In particular, in environments like university campuses, the occupants as well as occupancy in shared spaces varies over time. Systems for cooling in such environments that are centrally controlled are typically threshold driven and do not account for occupant feedback and thus are often relying on a reactive approach (fix after identifying problems). Therefore, having a fixed thermal operating set point may not be optimal in such cases—both from an occupant comfort and well-being as well as an energy efficiency perspective. To address this issue, a study was conducted which involved development and deployment of an experimental Internet of Things (IoT) prototype system and an Android application that facilitated people engagement on a university campus located in the UAE which typically exhibits hot climatic conditions. This paper showcases data driven insights obtained from this study, and in particular, how to achieve a balance between the conflicting goals of improving occupant comfort and energy efficiency. Findings from this study underscore the need for regular reassessments and adaptation. The proposed solution is low cost and easy to deploy and has the potential to reap significant savings through a reduction in energy consumption with estimates indicating around 50–100 kWh/day of savings per building and the resulting environmental impact. These findings would appeal to stakeholders who are keen to improve energy efficiency and reduce their operating expenses and environmental footprint in such climatic conditions. Furthermore, collective action from a large number of entities could result in significant impact through this cumulative effect. Full article
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20 pages, 7445 KiB  
Article
In the Direction of an Artificial Intelligence-Enabled Monitoring Platform for Concrete Structures
by Gloria Cosoli, Maria Teresa Calcagni, Giovanni Salerno, Adriano Mancini, Gagan Narang, Alessandro Galdelli, Alessandra Mobili, Francesca Tittarelli and Gian Marco Revel
Sensors 2024, 24(2), 572; https://doi.org/10.3390/s24020572 - 16 Jan 2024
Viewed by 1396
Abstract
In a seismic context, it is fundamental to deploy distributed sensor networks for Structural Health Monitoring (SHM). Indeed, regularly gathering data from a structure/infrastructure gives insight on the structural health status, and Artificial Intelligence (AI) technologies can help in exploiting this information to [...] Read more.
In a seismic context, it is fundamental to deploy distributed sensor networks for Structural Health Monitoring (SHM). Indeed, regularly gathering data from a structure/infrastructure gives insight on the structural health status, and Artificial Intelligence (AI) technologies can help in exploiting this information to generate early warnings useful for decision-making purposes. With a perspective of developing a remote monitoring platform for the built environment in a seismic context, the authors tested self-sensing concrete beams in loading tests, focusing on the measured electrical impedance. The formed cracks were objectively assessed through a vision-based system. Also, a comparative analysis of AI-based and statistical prediction methods, including Prophet, ARIMA, and SARIMAX, was conducted for predicting electrical impedance. Results show that the real part of electrical impedance is highly correlated with the applied load (Pearson’s correlation coefficient > 0.9); hence, the piezoresistive ability of the manufactured specimens has been confirmed. Concerning prediction methods, the superiority of the Prophet model over statistical techniques was demonstrated (Mean Absolute Percentage Error, MAPE < 1.00%). Thus, the exploitation of electrical impedance sensors, vision-based systems, and AI technologies can be significant to enhance SHM and maintenance needs prediction in the built environment. Full article
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20 pages, 4700 KiB  
Article
Assessment of the Performance of a Portable, Low-Cost and Open-Source Device for Luminance Mapping through a DIY Approach for Massive Application from a Human-Centred Perspective
by Francesco Salamone, Sergio Sibilio and Massimiliano Masullo
Sensors 2022, 22(20), 7706; https://doi.org/10.3390/s22207706 - 11 Oct 2022
Cited by 2 | Viewed by 1479
Abstract
Ubiquitous computing has enabled the proliferation of low-cost solutions for capturing information about the user’s environment or biometric parameters. In this sense, the do-it-yourself (DIY) approach to build new low-cost systems or verify the correspondence of low-cost systems compared to professional devices allows [...] Read more.
Ubiquitous computing has enabled the proliferation of low-cost solutions for capturing information about the user’s environment or biometric parameters. In this sense, the do-it-yourself (DIY) approach to build new low-cost systems or verify the correspondence of low-cost systems compared to professional devices allows the spread of application possibilities. Following this trend, the authors aim to present a complete DIY and replicable procedure to evaluate the performance of a low-cost video luminance meter consisting of a Raspberry Pi and a camera module. The method initially consists of designing and developing a LED panel and a light cube that serves as reference illuminance sources. The luminance distribution along the two reference light sources is determined using a Konica Minolta luminance meter. With this approach, it is possible to identify an area for each light source with an almost equal luminance value. By applying a frame that covers part of the panel and shows only the area with nearly homogeneous luminance values and applying the two systems in a dark space in front of the low-cost video luminance meter mounted on a professional reference camera photometer LMK mobile air, it is possible to check the discrepancy in luminance values between the low-cost and professional systems when pointing different homogeneous light sources. In doing so, we primarily consider the peripheral shading effect, better known as the vignetting effect. We then differentiate the correction factor S of the Radiance Pcomb function to better match the luminance values of the low-cost system to the professional device. We also introduce an algorithm to differentiate the S factor depending on the light source. In general, the DIY calibration process described in the paper is time-consuming. However, the subsequent applications in various real-life scenarios allow us to verify the satisfactory performance of the low-cost system in terms of luminance mapping and glare evaluation compared to a professional device. Full article
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