AIoT: Empowering IoT with AWS Greengrass and Edge Computing

Solution Overview

This solution offering leverages AWS services to provide an end-to-end AIoT platform designed to optimize industrial operations through real-time data ingestion, anomaly detection, and machine learning. The platform integrates edge computing, cloud processing, and digital twin technologies to offer comprehensive visibility and control over industrial assets, enhancing operational efficiency and reducing downtime.

Key Features

• Edge Telemetry Ingestion and Processing AWS IoT Greengrass is deployed on edge devices to ingest telemetry from industrial assets such as PLCs, sensors, and other IoT devices. Pre-fabricated connectors enable seamless data ingestion, facilitating hot anomaly detection directly at the edge.

• Edge-Based Anomaly Detection Perform real-time data stream analytics and machine learning inference at the edge for immediate identification of anomalies. This proactive approach ensures that potential issues are detected and addressed before impacting operations.

• Edge to Cloud Data Ingestion Real-time data ingestion into the cloud is enabled when network connectivity is available. AWS IoT Core serves as the interface for MQTT-based data ingestion, while AWS IoT SiteWise manages aggregation, asset tracking, and model management specifically tailored to IoT assemblies.

• Scheduled Edge Detection Jobs Utilize AWS IoT Jobs to schedule regular detection tasks at the edge, identifying drift in machine learning (ML) results. This feature ensures that the edge devices are consistently aligned with the latest ML models and anomaly detection parameters.

• Edge and Close-to-Edge Compute and Storage Store raw telemetry data and ML inference results locally at the edge or in close proximity. Edge storage also supports retraining ML models and performing data analysis, reducing latency and bandwidth usage.

• Data Lake Architecture AWS S3 forms the backbone of the data lake, storing terabytes of raw and processed telemetry data, as well as trained ML models and inference results. This scalable storage solution supports both hot and cold anomaly detection.

• Cloud-Based Anomaly Detection Leverage Amazon Kinesis Data Analytics to run queries and identify anomalistic behavior in datasets. This feature allows for deep analysis of telemetry data, enabling proactive maintenance and optimization of industrial processes.

• Time Series Data Management Time Stream database stores time series data, including customizable dimensions, allowing for detailed analysis and reporting of historical and real-time data.

• Kinesis Data Analytics Integration Utilize Kinesis Data Analytics to filter or process telemetric data before storing it in the Time Stream database. This preprocessing step ensures that only relevant data is stored, optimizing storage efficiency and query performance.

• Digital Twin Technology AWS TwinMaker enables the creation of digital twins, providing a virtual representation of physical assets. This feature allows for the optimization of building operations, increased production output, and improved equipment performance through enhanced visibility and control.

• Cold Anomaly Detection and Model Retraining Perform cold anomaly detection using data stored in the AWS S3 data lake. When drift is detected, either at the edge or in the cloud, AWS Step Functions and Lambda coordinate the retraining and redeployment of ML models, ensuring continuous improvement in anomaly detection capabilities.

• Operational Alerts and Notifications AWS SNS provides alerts and notifications to operational technology (OT) teams through emails, text messages, or integration into ticketing systems. This feature ensures timely responses to detected anomalies, minimizing downtime and operational disruptions.

• No-Code Dashboards for Performance Assessment Real-time and historical machine performance is assessed using no-code dashboards, enabling OT teams to make informed decisions quickly. These dashboards provide comprehensive visibility into the health and performance of industrial assets.