prediction-based smart street lighting and environmental monitoring system using IoT and cloud

  • Unique Paper ID: 173406
  • PageNo: 198-203
  • Abstract:
  • Smart Street lighting systems are increasingly being adopted to optimize energy usage. These systems automatically control street lights based on ambient light levels, ensuring they are only on when needed. This automation not only conserves energy but also reduces operational costs and manual intervention. Data collected from light sensors can be stored in the cloud, allowing the local authorities to monitor and analyze lighting patterns. This system contributes to smarter infrastructure and helps in reducing energy waste. The work addresses the limitations of existing smart street lighting systems, which typically rely on basic sensors for ambient light detection and often lack comprehensive environmental monitoring. To overcome these limitations, we are developing an advanced system that integrates additional sensors, including temperature, humidity, and MQ-135 air pollution sensors. The data collected from these sensors will be stored in the cloud and processed using machine learning algorithms to predict future environmental conditions. This approach enables a more efficient and intelligent system, combining hardware components like sensors and microcontrollers with software components for cloud storage and analytics, resulting in a robust and versatile solution.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{173406,
        author = {Mandapati Sathvika and Mekala Sivaiah and Aniket kumar and Pillala chandra sekhar and B.Srinivas raja},
        title = {prediction-based smart street lighting and environmental monitoring system using IoT and cloud},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {198-203},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173406},
        abstract = {Smart Street lighting systems are increasingly being adopted to optimize energy usage. These systems automatically control street lights based on ambient light levels, ensuring they are only on when needed. This automation not only conserves energy but also reduces operational costs and manual intervention. Data collected from light sensors can be stored in the cloud, allowing the local authorities to monitor and analyze lighting patterns. This system contributes to smarter infrastructure and helps in reducing energy waste.
The work addresses the limitations of existing smart street lighting systems, which typically rely on basic sensors for ambient light detection and often lack comprehensive environmental monitoring. To overcome these limitations, we are developing an advanced system that integrates additional sensors, including temperature, humidity, and MQ-135 air pollution sensors. The data collected from these sensors will be stored in the cloud and processed using machine learning algorithms to predict future environmental conditions. This approach enables a more efficient and intelligent system, combining hardware components like sensors and microcontrollers with software components for cloud storage and analytics, resulting in a robust and versatile solution.},
        keywords = {Environmental Monitoring, Ambient Light Control, IoT Sensors, ThingSpeak Cloud, Data Representation, LSTM Model, Future Value Prediction, Machine Learning in IoT, Energy Efficiency, Real-Time Monitoring, Intelligent Lighting System.},
        month = {March},
        }

Cite This Article

Sathvika, M., & Sivaiah, M., & kumar, A., & sekhar, P. C., & raja, B. (2025). prediction-based smart street lighting and environmental monitoring system using IoT and cloud. International Journal of Innovative Research in Technology (IJIRT), 11(10), 198–203.

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