Amazon Web Services Announces AWS IoT Edge Edge’s General Access. The IoT Edge feature of AWS IoT siteWise managed services, which was revealed in preview last December is now generally available. IoT SiteWise automates collecting and organizing industrial equipment data using software running on a local gateway. The gateway connects to the facility’s on-premises servers and collects, processes, and sends the data over to AWS. It can be used for modeling physical assets and processes as well as to calculate common industrial performance metrics. IoT SiteWise Edge allows users to collect and process equipment data locally for low-latency applications that need to continue working even when the internet is down. The “Edge” feature brings AWS IoT SiteWise’s cloud-based capabilities to users’ homes. In a blog post, Channy Yun, AWS’ principal developer advocate, explained that the new feature makes it simple to collect, process, and monitor equipment data locally, before sending it to AWS Cloud destinations. The software can be installed locally on hardware such as computers and industrial gateways, or on AWS Outposts or AWS Snow Family compute devices. It uses AWS IoT Greengrass as an edge runtime that helps to build, deploy, and manage applications. Yun explained that AWS IoT SiteWise edge allows you to organize and process your equipment data using AWS IoT SiteWise assets models. You can then access the equipment data locally using the same APIs that you use in AWS IoT SiteWise cloud. You can, for example, compute metrics such as Overall Equipment Efficiency (OEE) locally to use in a factory-floor monitoring dashboard. Yun cites three uses cases for IoT SiteWise Edge.

Amazon Web Services Announces AWS IoT Edge Edge’s General Access. The IoT Edge feature of AWS IoT siteWise managed services, which was revealed in preview last December is now generally available. IoT SiteWise automates collecting and organizing industrial equipment data using software running on a local gateway. The gateway connects to the facility’s on-premises servers and collects, processes, and sends the data over to AWS. It can be used for modeling physical assets and processes as well as to calculate common industrial performance metrics. IoT SiteWise Edge allows users to collect and process equipment data locally for low-latency applications that need to continue working even when the internet is down. The “Edge” feature brings AWS IoT SiteWise’s cloud-based capabilities to users’ homes. In a blog post, Channy Yun, AWS’ principal developer advocate, explained that the new feature makes it simple to collect, process, and monitor equipment data locally, before sending it to AWS Cloud destinations. The software can be installed locally on hardware such as computers and industrial gateways, or on AWS Outposts or AWS Snow Family compute devices. It uses AWS IoT Greengrass as an edge runtime that helps to build, deploy, and manage applications. Yun explained that AWS IoT SiteWise edge allows you to organize and process your equipment data using AWS IoT SiteWise assets models. You can then access the equipment data locally using the same APIs that you use in AWS IoT SiteWise cloud. You can, for example, compute metrics such as Overall Equipment Efficiency (OEE) locally to use in a factory-floor monitoring dashboard. Yun cites three uses cases for IoT SiteWise Edge.

October 28, 2022 Off By Nick
  • Localized testing of products: Multiple sensors embedded in products and testing equipment can generate thousands of data points per second for testing automotive, electronics, and aerospace products. To optimize bandwidth and storage costs, users can process data locally in a gateway to create near-real-time dashboards.
  • Lean manufacturing in the smart plant: Users can calculate key performance metrics like OEE, Mean Time Between FAILURES (MTBF), or Mean Time to Resolution(MTTR) in the gateway. They can also monitor local dashboards that need to continue working even if the factory’s connection to the cloud is interrupted temporarily. This allows factory staff to quickly identify the root cause of any bottleneck that arises.
  • Product quality can be improved by local applications that read sensor data and equipment from AWS IoT SiteWise Edge as it is collected. They can then combine this data with data from other sources, such as enterprise resource planning (ERP), manufacturing execution systems, and help to identify defect-causing conditions. Machine learning models can further process the data to identify anomalies and trigger alerts for factory workers.

In his blog post, Yun provides details on how to get started with this feature. According to the company, AWS IoT SiteWise edge is available in all AWS regions where AWS IoT SiteWise can be found.

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