IoT Street Lighting System with Real-Time Fog and Light Intensity Sensing and Cloud-Enabled Data Monitoring for Smart Cities

Abstract

Today’s urbanization has led us to the need of conservation of energy. One of the routine applications is city street lighting system for road safety. This paper proposes a low budget environment friendly street light controller that activates lighting automated to darkness and fog presence. The system uses ESP32 microcontroller interface with LDR to measure light intensity and DHT22 sensor to monitor temperature and humidity. The algorithm for fog detection used a threshold of humidity exceeding 90% and temperature lowering to 20°C. Data from multiple sensors are processed at real time, which is controlling a LED to simulate the status of street light on/off. Parallel to this a code is transmitting environment readings, activation status to a Google Sheet via Wi-Fi (serverless webhook for real time cloud logging). A series of test cases are collected for accuracy and responsiveness of the system. Comparative analysis demonstrates advantages of this system over traditional one. The proposed system is cost effective, scalable, easy to deploy. Future enhancement includes solar power integration, predictive analysis using machine learning, user friendly dashboards. This work gives a feasible effective solution for adaptive urban or rural area street lighting.

Introduction

Modern infrastructure is integrating smart technologies for energy efficiency and public safety. One example is intelligent street lighting, which addresses the limitations of traditional systems that fail to respond dynamically to environmental conditions like fog or low light.

2. Proposed Solution

A low-cost, IoT-based smart lighting system was developed using:

• ESP32 microcontroller

• LDR (Light Dependent Resistor) for ambient light detection

• DHT22 sensor for temperature and humidity

???? Activation Criteria:

• Darkness: LDR value > 3000

• Foggy Conditions: Humidity > 90% and Temperature < 20°C

If either condition is true, the streetlight (simulated by an LED) is turned ON. Otherwise, it remains OFF.

3. System Architecture

• ESP32 reads sensor data and executes local decision logic.

• Sends data (temperature, humidity, light, LED status, activation reason) to Google Sheets via Webhook using Google Apps Script.

• Offers serverless, cloud-based, real-time data logging for planners and developers.

• Designed to be scalable, low-cost, and easily deployable in urban or rural settings.

4. Related Work Comparison

5. System Implementation & Behavior

• Hardware Used: ESP32, DHT22, LDR, LED

• Polling interval: Every 10 seconds

• LED Actuation Logic:

  • Turns ON in darkness or fog

  • OFF during clear, well-lit conditions

? Key Observations:

• Real-time data logging worked reliably

• LED responded with <1-second delay

• Accurate environmental monitoring

• Minimal cost and energy consumption

6. Applications

• Smart Cities: Roadways, pedestrian lighting, parking lots

• Rural Electrification: Affordable, solar-powered setups

• Campuses/Industries: Retrofitting existing lights with IoT control

7. Future Work

Enhancements may include:

• Solar integration for off-grid use

• Machine learning for predictive fog detection

• Mobile/Web dashboard for real-time control and alerts

• Edge computing for local group decisions (mesh networking)

• Air quality monitoring to extend relevance for public health

Conclusion

This paper presented a low-cost, IoT-based street light control system that intelligently responds to ambient light and fog conditions using an ESP32 microcontroller, DHT22 sensor, LDR, and serverless cloud logging via Google Sheets. Experimental evaluation in a simulated environment demonstrated reliable fog detection (humidity > 90% & temperature < 20 °C), accurate light sensing (LDR threshold = 3000 ADC), and robust real-time data transmission. Comparative analysis showed significant advantages over traditional PIR- or vision-based systems in terms of cost, simplicity, and scalability. The proposed architecture holds promise for smart city deployments, rural electrification projects, and facility management applications. Ongoing and future work will focus on solar integration, predictive analytics, and user-centric dashboards, further enhancing the system’s autonomy and utility.

References

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Copyright

Copyright © 2025 Annapurna Singha Roy, Arunava Kundu, Kaustav Sinha. This 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.