Is it possible to bring cloud capabilities closer to local devices? Using Edge computing, data can be processed close to the source where data is generated.
Edge Computing addresses two critical scenarios, one where the devices are in remote locations with limited connectivity and second when the customers do not want to send their data to cloud for security reasons. In both the scenarios customer would like to have all the cloud capabilities implemented locally close to the devices.
With AWS IoT Greengrass all cloud capabilities can be implemented on local Edge devices. Multiple Edge devices can be connected to each other within the local network to ensure that device to device, device to sensor and sensor to device communication is possible. AWS IoT Greengrass core helps Edge devices to collect, manage and analyse data closer to the source of information (sensors and actuators), react automatically to local events and communicate securely with each other on local networks. AWS IoT Greengrass developers can use AWS Lambda SDK to work with Lambda functions and prebuilt connectors to create event-driven serverless applications and their logics that are deployed to Edge devices for local operations.
Sensor data (temperature, humidity and velocity) is collected and published to local Greengrass Core Device using MQTT protocol. A Lambda function which is running on Greengrass Core subscribes to that topic payload (Sensors Data) and stores it on local file storage. Sensor data is monitored continuously, and alerts are triggered based on various rules. In critical situations, device shutdown command will be transmitted to the device. As data is being collected, it is analysed for future Predictive Maintenance, so complete breakdown of the Windmill breakdown completely.
With the help of AWS Greengrass, we were able to build a resilient system for predictive and prescriptive maintenance of Wind Mill and Ore Mines. AWS Greengrass can really help you to build a complete eco system on the top of Edge Devices where you can perform different event-driven serverless operations and also you can write Machine Learning inference to prepare Local ML models to make the Edge device more powerful for continuous monitoring of basic equipment’s required for workers to work on such hazardous environment.
These services grant us the super power to act upon anomalies and critical situations instantly with a fast response time thereby avoiding casualties or further damage in the system while reducing the maintenance cost with almost zero downtime.