A Survey on - Deforestation and Wild Life Protection System Using IoT

Abstract

Deforestation and wildlife endangerment remain critical environmental challengesacrosstheglobe.Thisprojectproposes an IoT-based monitoring and alert system designed to track environmental parameters, detect unusual activities, and protect forest ecosystems. Leveraging the ESP WROOM 32, various sensors (DHT11, PIR, Sound, MQ-2, GPS, Ultrasonic, RFID, Buzzer etc), and the ESP32-CAM module, the system enables real- timedataacquisition,imagecapture,andlocation tracking. Sensor data is transmitted to a PHP- MySQL-based web dashboard with OpenStreetMap integration for visualization and anomaly detection. Automated alerts via SMS and email are sent through the TwilioAPI when predefined thresholds are breached. This paper details the system architecture, sensor integration, data communication, and user interface, demonstrating a low-cost and scalable solution for forest surveillance and wildlife conservation.

Introduction

Forests face threats like illegal logging, poaching, and fires, but traditional monitoring systems are often costly and limited. This project proposes an integrated IoT solution to enhance forest and wildlife protection through real-time sensor data, automated alerts, GPS tracking, and camera feedback, reducing manual monitoring efforts.

Literature Review:
Recent studies show IoT’s potential in environmental monitoring, including forest fire detection, animal tracking, and biodiversity monitoring. However, many existing systems lack comprehensive integration of sensors, real-time alerts, camera verification, and scalable communication, highlighting a need for a unified platform.

Methodology:

• Hardware: ESP32 microcontroller with sensors for temperature, humidity, motion, sound, gas detection, GPS, RFID, and an ESP32-CAM for live video.

• Software: Uses Arduino IDE, PHP/MySQL backend, Bootstrap-based dashboard, OpenStreetMap for visualization, and Twilio API for alerting via SMS/email.

• Data Flow: Sensor data is collected, transmitted to a server, logged in a database, visualized on a dashboard, and triggers alerts on detecting unusual activity.

Results:
The system was tested successfully, showing accurate sensor readings, efficient data transmission, real-time updates every 10 seconds, immediate alerts for gas or motion events, and live image streaming integrated with GPS mapping.

Discussion:
Multi-sensor integration improves monitoring reliability. The use of low-cost ESP32 ensures affordability and scalability. Future enhancements could include AI for anomaly detection, drone surveillance, and solar-powered operation.

Conclusion

ThisIoT-basedforestmonitoringsystemprovides a proactive approach to deforestation prevention and wildlife protection. The proposed system offersarobust,real-time,andremotesurveillance mechanism that can help mitigate illegal deforestation and protect wildlife. By leveraging modern communication and sensor technology, the system lays a foundation for smart forest management.

References

[1] Sharma et al. (2020): This study, titled \"A Review to Forest Fires and Its Detection Techniques Using Wireless Sensor Network,\" provides a comprehensive review of various forest fire detection techniques using wireless sensor networks. Engineering series. ResearchGate [2] Basha and Sree (2019): Their research focuses on an IoT-enabled forest surveillance system utilizing motion sensors and RF communication to detect human and animal movement. Link [3] Chenetal.(2022):Thestudyintroduces a modular biodiversity monitoring system using the ESP32 and LoRa technologyforwide-arealow-powerdata transmission. This system, published in Sensors by MDPI, focuses on energy- efficient environmental monitoring frameworks but primarily addresses species detection rather than detecting illegalhumaninterventionorfirehazards. Link [4] Rao et al. (2021): This hybrid model combinesIoTdatastreamswithmachine learningalgorithmstopredictforestfires based on various environmental parameters. The study demonstrates the value of integrating AI to improve prediction accuracy but also emphasizes theneedforfasteredgecomputationto reducelatencyinalertgeneration.The paper is published in IEEE Access. Link [5] Gupta and Rani (2021):They proposed an RFID-based wildlife tracking system thatmonitorsanimalmovementusingtag readers.Theexact publication details are not readily available in the provided sources. Link [6] Aniket Gat, HrishikeshGaikwad, Rahul Giri, Dr. Mohini P Sardey, Milind P Gajare(2022): This study implementedreal-timevideosurveillance in forest paths using Raspberry Pi and OpenCV. While it supports real-time observation, it often lacks integrated communicationprotocolstotriggeralerts or fails to scale effectively over wide forestareas.Theexactpublicationdetails are not readily available in the provided sources. Link [7] TwilioAPIDocs.(2024).\"SMSand EmailIntegration.\"https://www.twilio.com [8] OpenStreetMap.(2024).\"Open-source mappingplatform.\"https://www.openstreetmap.org [9] Images&diagram: https://www.electronicwings.com/esp32 https://www.digikey.in/en/products/detail/stmicroelectronics/L7805CV/585964 https://chatgpt.com https://gemini.google.comhttps://chat.deepseek.com https://console.twilio.comhttps://stackoverflow.com Fritzing–dashtopapphttps://getbootstrap.com

Copyright

Copyright © 2025 Bhisham Kumar, Pradhuman Singh, Amit Gaurav Kumar Wakre, Astha Sahu, Vivek Singh Rathore. 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.