Construction site environmental quality monitoring system based on Internet of Things technology and gray prediction model

Shilong Liu

doi:10.1117/12.3034971

7 August 2024 Construction site environmental quality monitoring system based on Internet of Things technology and gray prediction model

Shilong Liu

Author Affiliations +

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 132242G (2024) https://doi.org/10.1117/12.3034971
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

The present study explores the application of Internet of Things (IoT) technology in the environmental quality and safety monitoring system of construction sites. Environmental pollution has become a global challenge, and long-term exposure to unhealthy environments can induce various diseases. The IoT technology can provide real-time monitoring of environmental parameters such as temperature, humidity, and air quality, which can help identify potential hazards and prevent accidents. The data collected from the monitoring system can be analyzed using machine learning algorithms to develop predictive models for environmental quality. The study highlights the benefits of using IoT technology in improving environmental quality and safety in construction sites and discusses the challenges in implementing the technology. Overall, the study provides insights into how technology can be leveraged to address environmental challenges and promote sustainable development.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shilong Liu "Construction site environmental quality monitoring system based on Internet of Things technology and gray prediction model", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 132242G (7 August 2024); https://doi.org/10.1117/12.3034971

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available