Overview
A national forestry authority launched an advanced field solution to monitor the health, safety, and real-time location of personnel deployed in wildfire-prone areas. The project aimed to establish a robust and secure system that could operate in challenging outdoor environments and enhance emergency response capabilities.
Problem Statement
During wildfire incidents, the organization needed a reliable way to track the condition and location of field personnel. Existing solutions lacked integrated health monitoring, real-time geolocation, and reliable alerting mechanisms. The need was to design a portable, fault-tolerant system capable of collecting biometric and GPS data from wearable devices and delivering it to a centralized command center.
Solution
The solution integrated wearable health sensors and panic buttons with a LoRaWAN-based communication backbone. These devices transmitted encrypted data to AWS Greengrass compute devices deployed in the field, running components such as MQTT servers, stream managers, and API gateways. Local data was aggregated, visualized, and forwarded to the cloud using secure mTLS connections. The system architecture included:
- AWS IoT Greengrass for edge processing and MQTT communication.
- Amazon Kinesis and Kinesis Firehose for real-time data streaming.
- Amazon S3, DynamoDB, and ElastiCache Redis for data storage and caching.
- AWS Lambda, API Gateway, and Amazon Cognito for backend operations.
- Amazon Location Service to track personnel via an integrated map dashboard.
- AWS WAF and Private Certificate Authority to ensure security and access control.
The user interface was accessible both at field and HQ levels via secured browsers, offering real-time health data, panic alerts, and positioning on a map.
Outcomes
The system was successfully tested under field conditions. Wearable devices reliably transmitted heart rate, body temperature, and emergency alerts. The monitoring panel accurately displayed worker states (e.g., normal, panic, fall detected), with alerts generated within seconds. During the test, devices simulated real emergencies (e.g., falls, panic button presses), and each event was correctly captured and displayed. The solution demonstrated high reliability, fast data transfer, and intuitive UI design that aided rapid decision-making.
Lessons Learned
The project underscored the importance of edge computing in latency-sensitive use cases. Operating without stable internet required offline-capable logic for on-site panels, which proved critical. Device pairing and management workflows were streamlined, and UI elements like colored alert statuses improved operational awareness. Future iterations will explore additional sensor integrations and automated drone responses to alerts.
