[Paper Reading] How to utilize IoT technologies for smart building research and applications?

Publication year: 2021
Authors: Xinghua Gao, Pardis Pishdad-Bozorgi, Dennis R. Shelden, and Shu Tang
DOI: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001983

Introduction

• Background
  • According to the UN, the world’s urban population is expected to grow by 2.5 billion from 2014 to 2050. It would be connected to the fundamental demands such as housing, utilities, medical care, welfare, education, and employment.
  • To deal with these challenges, smart city has been envisioned that integrates physical, digital and human systems in the built environment.
    • Definitions
      • BAS: Building Automation System
      • BMS: Building Management System
      • BEMS: Building Energy Management System
      • EMS: Environmental Management System
      • CMMS: Computerized Maintenance Management Information System
      • CMMIS: Computerized Maintenance Management Information System
    • Problem
      • Smart building applications require extensive resources; however, they generate data based on various data standard and protocols. It causes data to be proprietary, single-purpose, and difficult to be integrated.
      • A method to establish an integrated, comprehensive, and real-time building database is needed.
      • Currently, industrial solutions for collecting indoor data have multiple shortcomings to widely used in buildings: cost, data proprietary, scalability, and portability
    • Research Goal
      • To propose an IoT-enabled data acquisition framework to establish a central facility database that is cost-effective, platform-neutral, scalable, and portable
      • To use the proposed framework to develop a system prototype for collecting and integrating building data
      • To implement and evaluate the prototype in a case study

Research Method & Result

• IoT-enabled data acquisition framework was proposed to enable using the data generated by low-cost sensors.
• Prototype system consisting of several minicomputers, multiple sensor modules, and developed databases, and software agents was designed and built to examine the usability of the proposed data acquisition framework.
• Distributed data agent for extracting data from building systems
  • It’s designed to acquire the data published by a building system on a limited-access website
• distributed data agent integrated with sensors for indoor environment data generation
  • It receives indoor environment data generated by sensor modules through the general-purpose input/output (GPIO) port and is programmed to run in an infinite loop to collect data every 60s.
• The utilized sensors are listed as follows:
  • DHT11: temperature and humidity sensor - Potodiode light sensor - YL 56 sound sensor - SW-420 vibration sensor - HC-SR501 motion detection sensor - MQ-5 combustible gas sensor - MQ-7 carbon monoxide gas sensor
  • Also, it’s programmed to communicated with the central server once every hour.
  • This system can automatically acquire data from a given building system, generate indoor environment data, and integrate and store them in a central database server.
    • Central database server
      • MariaDB is adopted as database system. A Python to MariaDB Connector provides the support for Python to manage the data stored in the MariaDB database (extract, transform, and load(ETL)). The extracted data are stored in the local MariaDB database.
  • The case study was conducted to demonstrate the proposed system prototype in a campus building

Validation

• The case study is divided into two parts: - To evaluate the data agent for extracting data from building systems - The data generated by building systems of Net-Zero Energy Residential Test Facility (NZERTF) were extracted, stored in the local data base, and pushed to the central database
  • To evaluate the data agent integrated with sensors for indoor environment data generation
    • Eight data agents were deployed in a campus building and generated data for two weeks.
    • The indoor environment data were integrated and stored in the central database server.

Discussion: How it can be used?

• Use Case 1: Facility Life-Cycle Cost Analysis
  • Use Case2: Improved Energy Modeling and Calibration
  • Use Case3: Occupant Status Sensing
  • Use Case4: Data-Driven, Automated Building Control
  • Low-Level Integration: BIM Standards and Building System Data Protocols
  • Vision for the Future Smart City
    • “The Basic Facility Data Package” provides the fundamental data for the smart building network.
    • The proposed architecture not only provide data to the network but also require services from it. For instance, security, emergency assistance, data connection, and operation and maintenance.

Conclusion

• Summary
  • Contribution
    • This research contributes to the body of knowledge by proposing an IoT-enabled data acquisition framework. It can be used for establishing cost-effective, platform-neutral, scalable, and portable building data acquisition systems.
  • Limitation
    • Extensive software program development work is required to extract data from different building systems with various user interfaces and data access methods
    • How to guarantee the cybersecurity of the data network
  • Future work
    • More sensor modules can be installed on the developed data agents to generate and collect more indoor environment data, and more software agents can be developed to extract data from different building systems
    • More innovative use cases can be developed using the established central facility database, and more case studies are needed to demonstrate their applicability and effectiveness

Comment & Interesting things