4/20/2023 0 Comments Filebeats nightlies hash![]() To build a UI solution based on the instructions shared in this blog, you don’t need specialized data analytics or IoT data processing experience. The following walk through contains detailed steps to develop this UI application at the edge with AWS IoT Greengrass V2. ![]() You can use this app to remotely monitor IoT job status in near-real time.įigure 1: UI application at the edge developed by AWS Professional Services and TensorIoT for remote IoT job monitoring in near-real time. The images below highlight the UI application developed by AWS Professional Services and TensorIoT. In part 2 of this blog, you will learn how to build a UI application at the edge to show streaming IoT job data. The data will help operators make the right decisions if it can be shown in an interactive UI application with low latency. Operators need to make judgements with streaming IoT data in a near-real time fashion. A variety of data analytics tools (Amazon Glue and Amazon Athena) /BI tools can be used to analyze this metadata with other IoT time series data in a flexible manner. In this use case, the unmapped data will land in Amazon S3. Sending data from the local edge device to AWS. This stream manager component will achieve two goals: You can also define bandwidth use, timeout behavior, and set priorities, so that the critical data can be exported first when internet connection resumes. You can define multiple data exportation configurations when the edge device is connected or disconnected. It is designed to work in environments with intermittent or limited connectivity. As a pre-built component, it offers flexible target destinations in the cloud, including Amazon Simple Storage Service (Amazon S3), Amazon Kinesis, AWS IoT SiteWise and AWS IoT Analytics. The stream manager component makes it easier and more reliable to transfer high-volumes IoT data from the edge to the cloud. Then the back-end component will send the data to an AWS IoT Greengrass V2 pre-built component: stream manager. This UI provides a file upload page, that uses a front-end AWS IoT Greengrass V2 component to ingest a JSON file, and sends the file data to a back-end component. Operators on factory floors may have some reports or IoT job metadata that cannot be ingested via a pre-defined IoT data model to the cloud. In this blog, we will showcase how to use this UI at the edge to address the following remote industrial job monitoring use cases: The application deployment and updates on multiple edge devices can be efficiently implemented via AWS IoT Greengrass runtime. This UI application at the edge contains multiple custom AWS IoT Greengrass components to achieve flexible data ingestion, and IoT data analytics and visualization at the edge. In this blog, we will showcase a UI application at the edge with AWS IoT Greengrass V2, a jointly developed solution by TensorIoT and AWS Professional Services. Such applications cover use cases such as: connection to industrial systems and IoT devices, low latency data processing and analytics to derive operational insights, and artificial intelligence (AI)/machine learning (ML) applications at the edge. Since AWS IoT Greengrass V2 launched, AWS is actively helping AWS Partner, TensorIoT, to develop various functional applications at the edge using this service. Moreover, the integration of AWS IoT Greengrass with AWS IoT Device Management enables you to monitor and manage your device fleet at scale. AWS IoT Greengrass also offers centralized security measures through a built-in log manager to facilitate edge component deployment. With AWS IoT Greengrass, you can access pre-built software components to improve the modularity of your application and accelerate your development efforts. AWS IoT Greengrass V2, the latest open source edge run time from AWS, offers a pathway to address the above-mentioned challenges. However, traditional IoT applications at the edge can experience some challenges, such as lack of centralized life-cycle management and a long development cycle. For industrial sites, such as remote power plants, oil rigs, and factory floors, an edge application with a user interface (UI) component offers great advantages to automate localized operations and improve workforce experience. Edge computing is a system of micro computing/storage devices that are installed at the edge of the network to efficiently process data locally. ![]() To reduce the unexpected network interruption and delay in IoT data processing, edge computing becomes a desirable option for real-time IoT data processing and monitoring. Many modern industrial operations require extensive monitoring and real-time decision making for efficiency and safety reasons.
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