Announcements from AWS

By Chantal Polsonetti

Category:
Company and Product News

AWS IoT RoboRunner, Now Available in Preview

AWS IoT RoboRunner is a new robotics service that makes it easier for enterprises to build and deploy applications that help fleets of robots work together seamlessly. With AWS IoT RoboRunner, it can be easier to build applications that make it possible to interoperate and orchestrate robots from a single view by reducing the complex development work required to connect robots to each other and the rest of an industrial software systems.

AWS IoT RoboRunner collects and combines data from each type of robot in a fleet and standardizes data types, like facility, location, and robotic task data in a central repository. Developers can use AWS IoT RoboRunner's APIs and software libraries to build applications on top of the centralized repository for use cases, such as task orchestration, space management, and robot collaboration. With AWS IoT RoboRunner, enterprises can improve efficiency of robotics fleets and reduce costs of running robotic operations.

The AWS IoT RoboRunner service is available in preview in US East (N. Virginia) and Europe (Frankfurt) Regions.

 

 

AWS IoT SiteWise now supports hot and cold storage tiers for industrial data

AWS IoT SiteWise is a managed service to collect, store, organize, and monitor data from industrial equipment at scale. AWS IoT SiteWise now supports two storage tiers for equipment data: a hot tier optimized for real-time applications, and a cold tier optimized for analytical applications. The hot tier stores frequently accessed data with lower write-to-read latency. Users can store data in the hot tier for industrial applications that need fast access to the latest measurement values from equipment, such as applications that visualize real-time metrics with an interactive dashboard, or applications that monitor operations and trigger alarms to identify equipment performance issues. The cold tier stores less-frequently accessed data that can tolerate higher read latency. Use data from the cold tier to create applications that need access to historical data, such as business intelligence (BI) dashboards, artificial intelligence (AI) and machine learning (ML) training, historical reports, and backups.

By using the AWS IoT SiteWise cold tier, customers can lower their storage cost for less-frequently accessed data. AWS IoT SiteWise uses an Amazon S3 bucket in the customer account as the destination for cold tier data. Users can configure AWS IoT SiteWise storage tiers from the AWS IoT SiteWise console. All they need to do is provide the URL to an Amazon S3 bucket in their AWS account and define a hot data retention period after which data will be removed from hot tier. Once cold tier storage is enabled, AWS IoT SiteWise will export data from measurements, metrics, transforms, and aggregates to the S3 bucket every 6 hours. In addition, AWS IoT SiteWise will export to the user S3 bucket any changes to asset and asset model definitions within minutes, so users can have the most updated virtual representation of their factory floor in their industrial data lake at all times.

 

 

Securely manage AWS IoT Greengrass edge devices using AWS Systems Manager

AWS announced the ability to securely manage AWS IoT Greengrass edge devices using AWS Systems Manager (SSM).

AWS has integrated AWS IoT Greengrass and AWS Systems Manager to simplify the management and maintenance of system software for edge devices. When coupled with the AWS IoT Greengrass Client Software, edge device administrators now can remotely access and securely manage with the multitude of devices that they own – from OS patching, to application deployments. Additionally, regularly scheduled operations that maintain edge compute systems can be automated, without the need for creating additional custom processes. For IT administrators, this release gives a complete overview of all of their devices through a centralized interface, and a consistent set of tools and policies with the AWS Systems Manager.

For customers new to the AWS IoT Greengrass platform, the integration with AWS Systems Manager simplifies setup even further with a new on- boarding wizard that can reduce the time it takes to create operational management systems for edge devices from weeks to hours.

 

Announcing AWS IoT TwinMaker (Preview), a service that makes it easier to build digital twins

AWS announced AWS IoT TwinMaker, a new service that makes it easier for developers to create and use digital twins of real-world systems to monitor and optimize operations.

With AWS IoT TwinMaker, users can quickly get started with creating digital twins of equipment, processes, and facilities by connecting data from different data sources, like equipment sensors, video feeds, and business applications, without having to move the data into a single repository. Users can use built-in data connectors for the following AWS services: AWS IoT SiteWise for equipment and time-series sensor data; Amazon Kinesis Video Streams for video data; and Amazon Simple Storage Service (S3) for storage of visual resources (for example, CAD files) and data from business applications.

AWS IoT TwinMaker also provides a framework for users to create data connectors to use with other data sources (such as Snowflake and Siemens MindSphere). AWS IoT TwinMaker forms a digital twin graph that combines and understands the relationships between virtual representations of your physical systems and connected data sources, so users can accurately model the real-world environment.

Once the digital twin graph is built, customers want to visualize the data in context of the physical environment. Using AWS IoT TwinMaker, users can import existing 3D models (such as CAD files, and point cloud scans) to compose and arrange 3D scenes of a physical space and its contents (e.g. a factory and its equipment) using simple 3D tools. To create a spatially aware visualization of operations, users can then add interactive video and sensor data overlays from the connected data sources, insights from connected machine learning (ML) and simulation services, and equipment maintenance records and manuals.

To help developers create a web-based application for end users, AWS IoT TwinMaker comes with a plugin for Amazon Managed Grafana. End users, such as plant operators and maintenance engineers use Grafana applications to observe and interact with the digital twin to help them optimize factory operations, increase production output, and improve equipment performance. Amazon Managed Grafana is a fully managed service for the open source dashboard and visualization platform from Grafana Labs.

AWS IoT TwinMaker is available in preview in US East (N. Virginia), US West (Oregon), Europe (Ireland), and Asia Pacific (Singapore), with availability in additional AWS Regions to come.

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