Building event-driven architectures with IoT sensor data

Post Syndicated from Raghavarao Sodabathina original https://aws.amazon.com/blogs/architecture/building-event-driven-architectures-with-iot-sensor-data/

The Internet of Things (IoT) brings sensors, cloud computing, analytics, and people together to improve productivity and efficiency. It empowers customers with the intelligence they need to build new services and business models, improve products and services over time, understand their customers’ needs to provide better services, and improve customer experiences. Business operations become more efficient by making intelligent decisions more quickly and over time develop a data-driven discipline leading to revenue growth and greater operational efficiency.

In this post, we showcase how to build an event-driven architecture by using AWS IoT services and AWS purpose-built data services. We also discuss key considerations and best practices while building event-driven application architectures with IoT sensor data.

Deriving insights from IoT sensor data

Organizations create value by making decisions from their IoT sensor data in near real time. Some common use cases and solutions that fit under event-driven architecture using IoT sensor data include:

Medical device data collection for personalized patient health monitoring, adverse event prediction, and avoidance.
Industrial IoT use cases to monitor equipment quality and determine actions like adjusting machine settings, using different sources of raw materials, or performing additional worker training to improve the quality of the factory output.
Connected vehicle use cases, such as voice interaction, navigation, location-based services, remote vehicle diagnostics, predictive maintenance, media streaming, and vehicle safety, that are based on in-vehicle computing and near real-time predictive analytics in the cloud.
Sustainability and waste reduction solutions, which provide access to dashboards, monitoring systems, data collection, and summarization tools that use machine learning (ML) algorithms to meet sustainability goals. Meeting sustainability goals is paramount for customers in the travel and hospitality industries.

Event-driven reference architecture with IoT sensor data

Figure 1 illustrates how to architect an event-driven architecture with IoT sensor data for near real-time predictive analytics and recommendations.

Figure 1. Building event-driven architecture with IoT sensor data

Architecture flow:

Data originates in IoT devices such as medical devices, car sensors, industrial IoT sensors.This telemetry data is collected using AWS IoT Greengrass, an open-source IoT edge runtime and cloud service that helps your devices collect and analyze data closer to where the data is generated.When an event arrives, AWS IoT Greengrass reacts autonomously to local events, filters and aggregates device data, then communicates securely with the cloud and other local devices in your network to send the data.
Event data is ingested into the cloud using edge-to-cloud interface services such as AWS IoT Core, a managed cloud platform that connects, manages, and scales devices easily and securely.AWS IoT Core interacts with cloud applications and other devices. You can also use AWS IoT SiteWise, a managed service that helps you collect, model, analyze, and visualize data from industrial equipment at scale.
AWS IoT Core can directly stream ingested data into Amazon Kinesis Data Streams. The ingested data gets transformed and analyzed in near real time using Amazon Kinesis Data Analytics with Apache Flink and Apache Beam frameworks.Stream data can further be enriched using lookup data hosted in a data warehouse such as Amazon Redshift. Amazon Kinesis Data Analytics can persist SQL results to Amazon Redshift after the customer’s integration and stream aggregation (for example, one minute or five minutes).The results in Amazon Redshift can be used for further downstream business intelligence (BI) reporting services, such as Amazon QuickSight.
Amazon Kinesis Data Analytics can also write to an AWS Lambda function, which can invoke Amazon SageMaker models. Amazon SageMaker is a the most complete, end-to-end service for machine learning.
Once the ML model is trained and deployed in SageMaker, inferences are invoked in a micro batch using AWS Lambda. Inferenced data is sent to Amazon OpenSearch Service to create personalized monitoring dashboards using Amazon OpenSearch Service dashboards.The transformed IoT sensor data can be stored in Amazon DynamoDB. Customers can use AWS AppSync to provide near real-time data queries to API services for downstream applications. These enterprise applications can be mobile apps or business applications to track and monitor the IoT sensor data in near real-time.Amazon Kinesis Data Analytics can write to an Amazon Kinesis Data Firehose stream, which is a fully managed service for delivering near real-time streaming data to destinations like Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon OpenSearch Service, Splunk, and any custom HTTP endpoints or endpoints owned by supported third-party service providers, including Datadog, Dynatrace, LogicMonitor, MongoDB, New Relic, and Sumo Logic.
In this example, data from Amazon Kinesis Data Analytics is written to Amazon Kinesis Data Firehose, which micro-batch streams data into an Amazon S3 data lake. The Amazon S3 data lake stores telemetry data for future batch analytics.

Key considerations and best practices

Keep the following best practices in mind:

Define the business value from IoT sensor data through interactive discovery sessions with various stakeholders within your organization.
Identify the type of IoT sensor data you want to collect and analyze for predictive analytics.
Choose the right tools for the job, depending upon your business use case and your data consumers. Please refer to step 5 earlier in this post, where different purpose-built data services were used based on user personas.
Consider the event-driven architecture as three key components: event producers, event routers, and event consumers. A producer publishes an event to the router, which filters and pushes the events to consumers. Producer and consumer services are decoupled, which allows them to be scaled, updated, and deployed independently.
In this architecture, IoT sensors are event producers. Amazon IoT Greengrass, Amazon IoT Core, Amazon Kinesis Data Streams, and Amazon Kinesis Data Analytics work together as the router from which multiple consumers can consume IoT sensor-generated data. These consumers include Amazon S3 data lakes for telemetry data analysis, Amazon OpenSearch Service for personalized dashboards, and Amazon DynamoDB or AWS AppSync for the downstream enterprise application’s consumption.

Conclusion

In this post, we demonstrated how to build an event-driven architecture with IoT sensor data using AWS IoT services and AWS purpose-built data services. You can now build your own event-driven applications using this post with your IoT sensor data and integrate with your business applications as needed.

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