Overview
A government-affiliated environmental agency implemented an IoT-based forest monitoring system to enhance early fire detection and ecosystem tracking. The project aimed to establish a secure, scalable, and serverless infrastructure capable of collecting, processing, and visualizing real-time environmental data from deep forest areas.
Problem Statement
The agency faced challenges in monitoring large forested areas for potential fire hazards and environmental changes. Their existing system lacked automation, real-time responsiveness, and integration capabilities with modern cloud technologies. There was a critical need to build a reliable backend for processing sensor data and supporting both internal analysts and field users with a user-friendly dashboard.
Solution
The solution used AWS IoT Core to ingest data from a network of distributed environmental sensors that measured metrics such as CO₂ levels, temperature, and humidity. AWS IoT Core pushed the data to Amazon MQ for managed MQTT message queuing. From there, AWS Lambda processed the messages and performed lightweight analytics before storing the results in Amazon DocumentDB within a secure private subnet.
On the front end, the application was hosted on AWS Amplify, accessed through an Application Load Balancer in a public subnet. This setup allowed field agents and administrative users to view dashboards, analytics, and alerts in real time. The entire solution was deployed inside an Amazon Virtual Private Cloud (VPC) for network isolation and security compliance.
Outcomes
The forest monitoring system significantly improved situational awareness across protected areas. Sensor data was now available in real-time, reducing fire response times and enabling data-driven resource planning. The agency reported improved operational efficiency and received positive feedback from both internal stakeholders and external partners.
Lessons Learned
One of the key takeaways was the importance of integrating serverless architecture and managed messaging services to reduce operational overhead. Additionally, placing the database in a private subnet enhanced security posture without compromising performance. The project also highlighted the need for automated alerting and visualization tools to improve response strategies during critical events.
