Smart Street Light Monitoring and Visualization Platform for Campus Management

Deepaisarn, Somrudee; Yiwsiw, Paphana; Tantiwattanapaibul, Chanon; Buaruk, Suphachok; Sornlertlamvanich, Virach

doi:10.1109/isai-nlp56921.2022.9960257

Cited by 4 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The platform provides a user-friendly interface for monitoring and controlling the lighting equipment, and it also visualizes the data collected from the devices and sensors in the area, making it easier for users to analyze and understand the information, and for the efficient maintenance by campus staff as well. The platform, moreover, this platform aims to improve energy efficiency and align with Thammasat University's sustainable development goals through monitoring and data analysis for optimal energy consumption [17].…”

Section: Smart Street Light Monitoring and Visualization Platformmentioning

confidence: 99%

Thammasat AI City Distributed Platform: Enhancing Social Distribution and Ambient Lighting

Sornlertlamvanich,

Charoenporn,

Deepaisarn

2024

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

The Thammasat AI City distributed platform is a proposed AI platform designed to enhance city intelligent management. It addresses the limitations of current smart city architecture by incorporating cross-domain data connectivity and machine learning to support comprehensive data collection. In this study, we delve into two main areas, that is, monitoring and visualization of city ambient lighting, and indoor human physical distance tracking. The smart street light monitoring system provides real-time visualization of street lighting status, energy consumption, and maintenance requirement, which helps to optimize energy consumption and maintenance reduction. The indoor camera-based system for human physical distance tracking can be used in public spaces to monitor social distancing and ensure public safety. The overall goal of the platform is to improve the quality of life in urban areas and align with sustainable urban development concepts.

show abstract

Section: Smart Street Light Monitoring and Visualization Platformmentioning

confidence: 99%

Thammasat AI City Distributed Platform: Enhancing Social Distribution and Ambient Lighting

Sornlertlamvanich,

Charoenporn,

Deepaisarn

2024

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

show abstract

“…According to the proposed web application platform made in our previous work, i.e., Deepaisarn et al [ 19 ], the functionality of the system includes the device connection state monitoring system, environmental data dashboard, and the device disconnection detection system. In the current implementation, the essential data collected using the installed environmental sensor appears on the dashboard, as demonstrated in Figure 2 .…”

Section: Functionalities and Data Requirementsmentioning

confidence: 99%

“…In this work, the target variable to be predicted by a time-series model was a future natural illuminance value at a specific timestamp. The dataset was collected from the environmental sensors, which were stored in the external platform database and imported via the API [ 19 ]. The environmental values from February 2022 to October 2022, were used to train and test the prediction model.…”

Section: Data Analytics and Prediction Modelsmentioning

confidence: 99%

“…In our previous implementation of the smart street light system installed at Thammasat University, Rangsit Campus, Thailand, Deepaisarn et al [ 19 ] proposed a web application platform for monitoring smart street lighting device status and visualizing data collected by the system, which also connected the flow of data with a city-scale platform of the Thammasat model [ 20 ]. The system included 167 smart street lights, which were installed on the campus in February, 2022.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics

Chaisawat

Lerttomolsakul

Cheewakriengkrai

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

The smart city concept has been popularized in the urbanization of major metropolitan areas through the implementation of intelligent systems and technology to serve the increasing human population. This work developed an automatic light adjustment system at Thammasat University, Rangsit Campus, Thailand, with a primary objective of optimizing energy efficiency, while providing sufficient illumination for the campus. The development consists of two sections: the device control and the prediction model. The device control functionalities were developed with the user interface to enable control of the smart street light devices and the application programming interface (API) to send the light-adjusting command. The prediction model was created using an AI-assisted data analytic platform to obtain the predicted illuminance values so as to, subsequently, suggest light-dimming values according to the current environment. Four machine-learning models were performed on a nine-month environmental dataset to acquire predictions. The result demonstrated that the three-day window size setting with the XGBoost model yielded the best performance, attaining the correlation coefficient value of 0.922, showing a linear relationship between actual and predicted illuminance values using the test dataset. The prediction retrieval API was established and connected to the device control API, which later created an automated system that operated at a 20-min interval. This allowed real-time feedback to automatically adjust the smart street lighting devices through the purpose-designed data analytics features.

show abstract

“…The back-end system development is fully explained in our previous conference paper titled "Smart Street Light Monitoring and Visualization Platform for Campus Management" [22], which was presented at the 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) in Chiang Mai, Thailand. This study proposes an additional maintenance and logging system as follows: The backend system process is shown in Fig.…”

Section: Back-end System Developmentmentioning

confidence: 99%

Data-centric Smart Street Light Monitoring and Visualization Platform for Campus Management

Deepaisarn,

Yiwsiw,

Tantiwattanapaibul

et al. 2023

JICCE

Self Cite

View full text Add to dashboard Cite

Smart lighting systems have become increasingly popular in several public sectors because of trends toward urbanization and intelligent technologies. In this study, we designed and implemented a web application platform to explore and monitor data acquired from lighting devices at Thammasat University (Rangsit Campus, Thailand). The platform provides a convenient interface for administrative and operative staff to monitor, control, and collect data from sensors installed on campus in real time for creating geographically specific big data. Platform development focuses on both back-and front-end applications to allow a seamless process for recording and displaying data from interconnected devices. Responsible persons can interact with devices and acquire data effortlessly, minimizing workforce and human error. The collected data were analyzed using an exploratory data analysis process. Missing data behavior caused by system outages was also investigated.

show abstract

Smart Street Light Monitoring and Visualization Platform for Campus Management

Cited by 4 publications

References 0 publications

Thammasat AI City Distributed Platform: Enhancing Social Distribution and Ambient Lighting

Thammasat AI City Distributed Platform: Enhancing Social Distribution and Ambient Lighting

Automated Street Light Adjustment System on Campus with AI-Assisted Data Analytics

Data-centric Smart Street Light Monitoring and Visualization Platform for Campus Management

Contact Info

Product

Resources

About