A Sensing System Based on Public Cloud to Monitor Indoor Environment of Historic Buildings
Abstract
:1. Introduction
- An adaptable digitalization framework for smart preservation of historic buildings is proposed. This framework has a flexible architecture that other people can refer to in implementing their systems.
- A sensing system following the architecture of the proposed digitalization framework is implemented by using public cloud services, open-source software, and hardware. This system supports data collection, transmission, storage, and visualization. This system also supports adding more functionalities in future research.
- The implemented sensing system is applied on three historic buildings in Sweden for field testing. The stability for long-term operation of the sensing system is evaluated. A preliminary analysis for the indoor environment is also showcased by following industry standards.
2. System Design
3. System Implementation
3.1. Collector and Sensors
- A temperature and relative humidity (T&RH) sensor (DHT22, Seeed Technology, Shenzhen, CHN) is used to measure temperature and relative humidity. The detecting range is −40–80 °C for temperature and 5–99% for relative humidity. The accuracy reaches up to 0.5 °C and 2% RH.
- A CO2 sensor (MH-Z16, Winsen Electronics Technology, Zhengzhou, CHN) is adopted to measure CO2 concentration. This sensor uses non-dispersive infrared to detect CO2 in the air. The measurement range is 0–2000 parts per million (PPM). The resolution is one PPM, while the accuracy is 200 PPM. The CO2 sensor can be operated at temperature 0–50 °C and humidity 0–90% RH.
- A dust sensor (PPD42NS, Shinyei Corporation, New York, NY, USA) is utilized to measure suspended particulate matter concentration in the air. This sensor can detect particles with a diameter larger than one µm. The particulate matter level in the air is measured by counting the low pulse occupancy (LPO) time in a given time unit. LPO time is proportional to the particulate matter concentration. The detecting range is 0–28,000 pieces per liter (pcs/L).
- An air quality (AQ) sensor (MIKROE-1630, MikroElektronika, Beograd, SRB), which carries an MQ-135 sensor, is used to detect poisonous gases, e.g., ammonia, nitrogen oxides, and benzene.
- A vibration sensor (Grove-Piezo Vibration Sensor, Seeed Technology, Shenzhen, CHN) based on PZT film sensor LDT0-028 is used to measure vibration and impact generated by human activities.
3.2. Edge Platform
- Compact size and flexible mounting options for easy deployment in historic buildings;
- Sufficient computing and storage resources for processing and analyzing collected data or performing partitioned tasks assigned to the edge platform;
- Rich peripheral interfaces for connecting with perception devices.
3.3. Cloud Platform
- “IoT Hub” [43] acts as a central message hub for reliable and secure bi-directional communication between the edge and cloud platforms. The IoT Hub supports multiple messaging patterns such as device-to-cloud telemetry, cloud-to-device messages, and invoking direct methods on devices from the cloud. In this study, the device-to-cloud telemetry is used to deliver collected environmental data from the edge platform to the cloud platform.
- “Event Hubs” [44] is used to build a pipeline for ingesting data in real-time. When the IoT Hub receives device-to-cloud telemetries from the edge platform, Event Hubs notifies subscribed consumers to consume the messages.
- “Functions” [45] is an event-driven serverless compute platform. A serverless function is implemented by utilizing Functions to consume events from Event Hubs, parse sensing data from device-to-cloud telemetry, and insert sensing data into the database.
- “SQL Database” [46] provides scalable storage resources and is used for storing structured data in this study. Metadata of historic buildings, metadata of edge devices, and sensing data are stored in separated tables.
- “Blob Storage” [47] helps to store and access unstructured data at scale. Images or documents produced in this study are stored in Blob Storage.
- “Web Apps” [48] facilitates deployment of web applications. The sensing system provides data visualization for collected data by using a web application deployed by Web Apps service.
4. Case Studies
4.1. Description of Three Historic Buildings
4.2. Metric to Evaluate the Stability
4.3. Fluctuation Analysis of Relative Humidity
- Average level over a selected period: This level is calculated as the arithmetic mean of the relative humidity readings as follows:
- Monthly cycles: This cycle is determined by computing the central moving average (MA) for each reading, which is the arithmetic mean (see Equation (2) for calculation) of all the relative humidity readings recorded over a 30-day period that includes 15 days before and 15 days after the average is computed. In this paper, relative humidity readings measured from 21 March 2021 12:00 a.m. (CET) to 15 June 2021 12:00 a.m. (CET) are used to calculate monthly cycles between 5 April 2021 12:00 a.m. (CET) and 31 May 2021 12:00 a.m. (CET).
- Short-term fluctuations: A short-term fluctuation is defined as the difference between a current reading and the 30-day MA calculated for that reading as mentioned above. As a result, the short-term fluctuations consider both natural seasonal variability and the stress relaxation time constant of the materials.
- If the relative humidity is steady, there is no need to adjust the relative humidity or temperature.
- If the relative humidity is unsteady, the 7th and 93rd percentiles of the fluctuations recorded during the monitoring period are used to determine the lower and upper boundaries of the target range of relative humidity, respectively.
- The 7th and 93rd percentiles are obtained by ordering the fluctuations from the lowest negative value to the greatest positive value and picking the values below which 7th or 93rd percent of observations are found, respectively.
5. Results and Discussion
5.1. Data Visualization and Sharing
- buildings.csv
- – Id: Primary key for buildings. Each building has a unique value;
- – BuildingName: Name of a building.
- devices.csv
- – Id: Primary key for edge devices. Each edge device has a unique value;
- – DeviceName: Name of an edge device;
- – BuildingId: Foreign key. References the primary key of buildings.csv.
- sensing.csv
- – Id: Primary key for sensing data. Each record has a unique value;
- – UtcTimestampMs: Milliseconds from 1 January 1970 at Coordinated Universal Time (UTC), for indicating when the measurement was taken;
- – PartitionKey: Days from 1 January 1970 at UTC, for helping partition table;
- – DeviceId: Foreign key, references the primary key of devices.csv;
- – CollectorId: Unique identification for collectors under an edge device;
- – Humidity: Relative humidity. The real value is dividing the raw value by 100;
- – Temperature: Degree Celsius. The real value is dividing the raw value by 100;
- – CO2: CO2 concentration in PPM. The raw value is the real value;
- – Dust: Dust concentration in pcs/L. The raw value is the real value;
- – AirQuality: An integer value (0–1023) mapped from output voltage (0–5 V) of the AQ sensor;
- – Vibration: Rising edge count in a period of 15 s.
5.2. System Stability
5.3. Fluctuation Analysis of Relative Humidity in the City Museum
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
App | Application; |
AQ | Air quality; |
BMS | Building management system; |
CET | Central European Time; |
CO2 | Carbon dioxide; |
COVID-19 | 2019 novel coronavirus; |
CSV | Comma-separated values; |
DTU | Database transaction unit; |
eMMC | Embedded multimedia card; |
GB | Gigabyte; |
GPIO | General-purpose input/output; |
HDMI | High-definition multimedia interface; |
HVAC | Heating, ventilation, and air conditioning; |
IO | Input/output; |
IoT | Internet of Things; |
KB | Kilobyte; |
LPO | Low pulse occupancy; |
MA | Moving average; |
MB | Megabyte; |
OS | Operating system; |
pcs/L | Pieces per liter; |
PPM | Parts per million; |
RISE | Research Institute of Sweden; |
RPi CM3+ Dev Kit | Raspberry Pi Compute Module 3+ Development Kit; |
SDK | Software development kit; |
SDRAM | Synchronous dynamic random-access memory; |
SLA | Service level agreement; |
SPI | Serial peripheral interface; |
SQL | Structured query language; |
T&RH | Temperature and relative humidity; |
UART | Universal asynchronous receiver-transmitter; |
USB | Universal serial bus; |
UTC | Coordinated Universal Time. |
Appendix A
Azure Service | Scale Tier | Key Specifications |
---|---|---|
IoT Hub | Standard S1 | 400 K messages/day per unit 4 KB message meter size |
Functions | Shared Environment (Windows) | 1 GB disk space Shared compute |
SQL Database | Basic | 2 GB storage size 5 DTUs |
Blob Storage | Standard/Hot | Milliseconds latency Low access lost |
Web App | Basic Dedicated Environment (Linux) | 10 GB disk space Dedicated compute |
References
- Camuffo, D. Microclimate for Cultural Heritage; Elsevier: Amsterdam, The Netherlands, 2019. [Google Scholar]
- Camuffo, D.; Brimblecombe, P.; Van Grieken, R.; Busse, H.J.; Sturaro, G.; Valentino, A.; Bernardi, A.; Blades, N.; Shooter, D.; De Bock, L.; et al. Indoor air quality at the Correr museum, Venice, Italy. Sci. Total Environ. 1999, 236, 135–152. [Google Scholar] [CrossRef]
- Camuffo, D.; Van Grieken, R.; Busse, H.J.; Sturaro, G.; Valentino, A.; Bernardi, A.; Blades, N.; Shooter, D.; Gysels, K.; Deutsch, F.; et al. Environmental monitoring in four European museums. Atmos. Environ. 2001, 35, S127–S140. [Google Scholar] [CrossRef]
- Corgnati, S.P.; Fabi, V.; Filippi, M. A methodology for microclimatic quality evaluation in museums: Application to a temporary exhibit. Build. Environ. 2009, 44, 1253–1260. [Google Scholar] [CrossRef]
- Thomson, G. The Museum Environment; Elsevier: Amsterdam, The Netherlands, 2013. [Google Scholar]
- Camuffo, D.; Bernardi, A.; Sturaro, G.; Valentino, A. The microclimate inside the Pollaiolo and Botticelli rooms in the Uffizi Gallery, Florence. J. Cult. Herit. 2002, 3, 155–161. [Google Scholar] [CrossRef]
- Loupa, G.; Charpantidou, E.; Kioutsioukis, I.; Rapsomanikis, S. Indoor microclimate, ozone and nitrogen oxides in two medieval churches in Cyprus. Atmos. Environ. 2006, 40, 7457–7466. [Google Scholar] [CrossRef]
- Zarzo, M.; Fernández-Navajas, A.; García-Diego, F.J. Long-term monitoring of fresco paintings in the Cathedral of Valencia (Spain) through humidity and temperature sensors in various locations for preventive conservation. Sensors 2011, 11, 8685–8710. [Google Scholar] [CrossRef] [PubMed]
- Diego, F.J.G.; Esteban, B.; Merello, P. Design of a hybrid (wired/wireless) acquisition data system for monitoring of cultural heritage physical parameters in smart cities. Sensors 2015, 15, 7246–7266. [Google Scholar] [CrossRef] [PubMed]
- Lombardo, L.; Corbellini, S.; Parvis, M.; Elsayed, A.; Angelini, E.; Grassini, S. Wireless sensor network for distributed environmental monitoring. IEEE Trans. Instrum. Meas. 2017, 67, 1214–1222. [Google Scholar] [CrossRef]
- Grassini, S.; Angelini, E.; Elsayed, A.; Corbellini, S.; Lombardo, L.; Parvis, M. Cloud infrastructure for museum environmental monitoring. In Proceedings of the 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Turin, Italy, 22–25 May 2017; pp. 1–6. [Google Scholar]
- Huynh, A.; Zhang, J.; Ye, Q.Z.; Gong, S. Wireless Remote Monitoring System for Cultural Heritage. Sens. Transducers J. 2010, 118, 1–12. [Google Scholar]
- Zhang, J.; Huynh, A.; Ye, Q.Z.; Gong, S. Remote sensing system for cultural buildings utilizing ZigBee Technology. In Proceedings of the 8th International Conference on Computing, Communications and Control Technologies (CCCT 2010), Orlando, FL, USA, 6–9 April 2010; pp. 71–77. [Google Scholar]
- Perles, A.; Pérez-Marín, E.; Mercado, R.; Segrelles, J.D.; Blanquer, I.; Zarzo, M.; Garcia-Diego, F.J. An energy-efficient internet of things (IoT) architecture for preventive conservation of cultural heritage. Future Gener. Comput. Syst. 2018, 81, 566–581. [Google Scholar] [CrossRef]
- Burri, N.; Von Rickenbach, P.; Wattenhofer, R. Dozer: Ultra-low power data gathering in sensor networks. In Proceedings of the 6th International Conference on Information Processing in Sensor Networks, Cambridge, MA, USA, 25–27 April 2007; pp. 450–459. [Google Scholar]
- Lazarescu, M.T. Design of a WSN platform for long-term environmental monitoring for IoT applications. IEEE J. Emerg. Sel. Top. Circuits Syst. 2013, 3, 45–54. [Google Scholar] [CrossRef] [Green Version]
- Klein, L.J.; Bermudez, S.A.; Schrott, A.G.; Tsukada, M.; Dionisi-Vici, P.; Kargere, L.; Marianno, F.; Hamann, H.F.; López, V.; Leona, M. Wireless sensor platform for cultural heritage monitoring and modeling system. Sensors 2017, 17, 1998. [Google Scholar] [CrossRef] [Green Version]
- Fernández-Navajas, Á.; Merello, P.; Beltrán, P.; García-Diego, F.J. Software for storage and management of microclimatic data for preventive conservation of cultural heritage. Sensors 2013, 13, 2700–2718. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Moon, J.; Kum, S.; Lee, S. A heterogeneous IoT data analysis framework with collaboration of edge-cloud computing: Focusing on indoor PM10 and PM2. 5 status prediction. Sensors 2019, 19, 3038. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Merello, P.; García-Diego, F.J.; Beltrán, P.; Scatigno, C. High frequency data acquisition system for modelling the impact of visitors on the thermo-hygrometric conditions of archaeological sites: A Casa di Diana (Ostia Antica, Italy) case study. Sensors 2018, 18, 348. [Google Scholar] [CrossRef] [Green Version]
- Guo, Z.; Chen, P.; Zhang, H.; Jiang, M.; Li, C. IMA: An integrated monitoring architecture with sensor networks. IEEE Trans. Instrum. Meas. 2012, 61, 1287–1295. [Google Scholar] [CrossRef]
- Bolchini, C.; Geronazzo, A.; Quintarelli, E. Smart buildings: A monitoring and data analysis methodological framework. Build. Environ. 2017, 121, 93–105. [Google Scholar] [CrossRef]
- Akrivopoulos, O.; Zhu, N.; Amaxilatis, D.; Tselios, C.; Anagnostopoulos, A.; Chatzigiannakis, I. A fog computing-oriented, highly scalable iot framework for monitoring public educational buildings. In Proceedings of the 2018 IEEE international conference on communications (ICC), Kansas City, MO, USA, 20–24 May 2018; pp. 1–6. [Google Scholar]
- Liu, Y.; Hassan, K.A.; Karlsson, M.; Pang, Z.; Gong, S. A data-centric internet of things framework based on azure cloud. IEEE Access 2019, 7, 53839–53858. [Google Scholar] [CrossRef]
- Gysels, K.; Delalieux, F.; Deutsch, F.; Van Grieken, R.; Camuffo, D.; Bernardi, A.; Sturaro, G.; Busse, H.J.; Wieser, M. Indoor environment and conservation in the royal museum of fine arts, Antwerp, Belgium. J. Cult. Herit. 2004, 5, 221–230. [Google Scholar] [CrossRef]
- Corbellini, S.; Di Francia, E.; Grassini, S.; Iannucci, L.; Lombardo, L.; Parvis, M. Cloud based sensor network for environmental monitoring. Measurement 2018, 118, 354–361. [Google Scholar] [CrossRef]
- Cloud Computing Services|Microsoft Azure. Available online: https://azure.microsoft.com/ (accessed on 17 June 2021).
- Amazon Web Services (AWS)—Cloud Computing Services. Available online: https://aws.amazon.com/ (accessed on 17 June 2021).
- Cloud Computing Services|Google Cloud. Available online: https://cloud.google.com/ (accessed on 17 June 2021).
- Liu, Y.; Pang, Z.; Karlsson, M.; Gong, S. Anomaly detection based on machine learning in IoT-based vertical plant wall for indoor climate control. Build. Environ. 2020, 183, 107212. [Google Scholar] [CrossRef]
- Shi, W.; Cao, J.; Zhang, Q.; Li, Y.; Xu, L. Edge computing: Vision and challenges. IEEE Internet Things J. 2016, 3, 637–646. [Google Scholar] [CrossRef]
- Tse, R.; Aguiari, D.; Chou, K.S.; Tang, S.K.; Giusto, D.; Pau, G. Monitoring cultural heritage buildings via low-cost edge computing/sensing platforms: The Biblioteca Joanina de Coimbra case study. In Proceedings of the 4th EAI International Conference on Smart Objects and Technologies for Social Good, Bologna, Italy, 28–30 November 2018; pp. 148–152. [Google Scholar]
- Sands, T. Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems. Sensors 2021, 21, 4603. [Google Scholar] [CrossRef]
- Sands, T. Development of deterministic artificial intelligence for unmanned underwater vehicles (UUV). J. Mar. Sci. Eng. 2020, 8, 578. [Google Scholar] [CrossRef]
- Grzonka, D.; Jakobik, A.; Kołodziej, J.; Pllana, S. Using a multi-agent system and artificial intelligence for monitoring and improving the cloud performance and security. Future Gener. Comput. Syst. 2018, 86, 1106–1117. [Google Scholar] [CrossRef]
- Software|Arduino. Available online: https://www.arduino.cc/en/software (accessed on 17 June 2021).
- Arduino—SoftwareSerial. Available online: https://www.arduino.cc/en/Reference/softwareSerial (accessed on 17 June 2021).
- Raspberry Pi OS—Raspberry Pi. Available online: https://www.raspberrypi.org/software/ (accessed on 17 June 2021).
- MobaXterm Free Xserver and Tabbed SSH Client for Windows. Available online: https://mobaxterm.mobatek.net/ (accessed on 17 June 2021).
- Spidev · PyPI. Available online: https://pypi.org/project/spidev/ (accessed on 17 June 2021).
- Azure-Iot-Device · PyPI. Available online: https://pypi.org/project/azure-iot-device/ (accessed on 17 June 2021).
- Home|Remote.it. Available online: https://remote.it/ (accessed on 17 June 2021).
- IoT Hub|Microsoft Azure. Available online: https://azure.microsoft.com/en-us/services/iot-hub/ (accessed on 17 June 2021).
- Event Hubs—Real-Time Data Ingestion|Microsoft Azure. Available online: https://azure.microsoft.com/en-us/services/event-hubs/ (accessed on 17 June 2021).
- Azure Functions Serverless Compute|Microsoft Azure. Available online: https://azure.microsoft.com/en-us/services/functions/ (accessed on 17 June 2021).
- SQL Database—Managed Cloud Database Service|Microsoft Azure. Available online: https://azure.microsoft.com/en-us/products/azure-sql/database/ (accessed on 17 June 2021).
- Azure Blob Storage|Microsoft Azure. Available online: https://azure.microsoft.com/en-us/services/storage/blobs/ (accessed on 17 June 2021).
- Web App Service|Microsoft Azure. Available online: https://azure.microsoft.com/en-us/services/app-service/web/ (accessed on 17 June 2021).
- Dash Overview. Available online: https://plotly.com/dash/ (accessed on 17 June 2021).
- Conservation of Cultural Property—Specifications for Temperature and Relative Humidity to Limit Climate-Induced Mechanical Damage in Organic Hygroscopic Materials; Standard; European Committee for Standardization: Brussels, Belgium, 2010.
- Minerva. Available online: https://historicbuildings.azurewebsites.net/ (accessed on 18 June 2021).
- Azure Subscription Limits and Quotas—Azure Resource Manager|Microsoft Docs. Available online: https://docs.microsoft.com/en-us/azure/azure-resource-manager/management/azure-subscription-service-limits#app-service-limits (accessed on 28 June 2021).
Sensor Box Deployed In | Expected | Actual | Lost | Loss Rate |
---|---|---|---|---|
The City Museum | 322,560 | 316,251 | 6309 | 1.96% |
The City Theatre | 322,560 | 316,121 | 6439 | 2.00% |
The Auditorium | 322,560 | 315,978 | 6582 | 2.04% |
Total | 967,680 | 948,350 | 19,330 | 2.00% |
Location | Amount | Proportion |
---|---|---|
Edge to IoT Hub | ∼3140 | ∼16% |
IoT Hub to Functions | ∼16,190 | ∼84% |
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Ni, Z.; Liu, Y.; Karlsson, M.; Gong, S. A Sensing System Based on Public Cloud to Monitor Indoor Environment of Historic Buildings. Sensors 2021, 21, 5266. https://doi.org/10.3390/s21165266
Ni Z, Liu Y, Karlsson M, Gong S. A Sensing System Based on Public Cloud to Monitor Indoor Environment of Historic Buildings. Sensors. 2021; 21(16):5266. https://doi.org/10.3390/s21165266
Chicago/Turabian StyleNi, Zhongjun, Yu Liu, Magnus Karlsson, and Shaofang Gong. 2021. "A Sensing System Based on Public Cloud to Monitor Indoor Environment of Historic Buildings" Sensors 21, no. 16: 5266. https://doi.org/10.3390/s21165266
APA StyleNi, Z., Liu, Y., Karlsson, M., & Gong, S. (2021). A Sensing System Based on Public Cloud to Monitor Indoor Environment of Historic Buildings. Sensors, 21(16), 5266. https://doi.org/10.3390/s21165266