Smart Environmental Monitoring System Using Raspberry PI and DHT11 Sensor

Abstract

To achieve monitoring, the Smart Environmental System relies on Raspberry Pi along with DHT11. sensors to continuously track atmospheric parameters Thermal condition and humidity, aiming to support sustainable living and enhance decision-making in various sectors. Traditional environmental monitoring methods rely heavily on manual data collection and laboratory testing, which are often time-consuming, less efficient, and prone to human error. The proposed approach automates the process by integrating low-cost IoT hardware with real-time data logging and analysis capabilities. By collecting, storing, and transmitting environmental data, the system ensures accuracy, reduces human intervention, and enables remote access to information. This solution can be deployed in homes, industries, agriculture, and smart cities, where maintaining environmental balance and predicting trends is crucial. Overall, the system provides an affordable, scalable, and user-friendly mechanism for real-time environmental monitoring, supporting modern requirements for sustainability and public safety.

Introduction

Due to the growing impacts of climate change, urbanization, and industrialization, real-time environmental monitoring has become essential. Traditional manual methods are limited in accuracy and responsiveness, prompting the development of smarter solutions.

This project introduces an IoT-based Environmental Monitoring System using Raspberry Pi and DHT11 sensors to monitor temperature and humidity in real time. The system automates data collection, minimizes human error, and provides timely alerts when environmental conditions exceed safe thresholds. It supports cloud integration for remote access, data visualization, and trend analysis, making it useful in fields like precision agriculture, industrial safety, disaster management, and smart homes.

Key Components and Methods:

• Hardware:

  • DHT11 Sensor (temperature & humidity)

  • Raspberry Pi (central processor)

  • Cloud/database for storage

  • Web/mobile interface for visualization

• Working Process:

  1. Data collection from sensors

  2. Processing via Raspberry Pi

  3. Storage locally or on cloud

  4. Visualization with dashboards and alerts on threshold breaches

• Data Processing Techniques:

  • Noise filtering

  • Threshold comparison

  • Long-term trend analysis

Implementation & Testing:

• Experimental Setup: Raspberry Pi (Model 3B/4), DHT11, Python programming

• Performance:

  • Accuracy: 94% (temperature), 87% (humidity)

  • Data logged every 10 seconds

  • 48+ hours of continuous operation

  • Remote monitoring with minimal delay

Applications & Impact:

• Agriculture: Improved irrigation scheduling

• Home Automation: Alerts for maintaining comfort levels

• Scalability: Affordable and adaptable to larger setups

• Sustainability: Promotes informed, data-driven decisions for environmental health

Conclusion

This project demonstrates that an IoT-based environmental monitoring system using Raspberry Pi and DHT11 sensors is a practical, cost-effective, and reliable solution for real-time monitoring. By ensuring continuous measurement, accurate data logging, and remote accessibility, the system addresses the limitations of manual monitoring methods. The experimental Findings suggest significant promise for deployment in agriculture, smart homes, and industrial safety. Future work could enhance the system by integrating additional sensors (CO2, air quality, light intensity) and implementing predictive analytics for advanced decision-making.

References

[1] Sharma, P., & Gupta, R. (2021). “IoT-Based Real-Time Environmental Monitoring System.”, 174(2), 22-28. [2] Kumar, A., & Singh, M. (2020). “Low-Cost IoT Solutions for Smart Cities: Environmental Monitoring Using Raspberry Pi.” Journal of Emerging Technologies, 8(3), 110-118. [3] Li, X., & Chen, Y. (2019). “Cloud-Integrated IoT for Environmental Monitoring.” IEEE Sensors Journal, 19(14), 5678–5686. [4] Patel, D., & Verma, S. (2022). “IoT in Precision Agriculture: Enhancing Productivity through Smart Monitoring.” Agricultural Informatics Review, 10(1), 45-59. [5] National Institute of Standards and Technology (NIST). (2020). “Evaluation of IoT Systems for Real-Time Environmental Applications.” Technical Report NISTIR 8400.

Copyright

Copyright © 2025 Sharana Kumar B, Prakash O. Sarangamath. 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.