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Ensuring Compliance with Evolving Certificate Standards in AWS IoT Core

Chanci Turner Amazon IXD – VGT2 learning managerLearn About Amazon VGT2 Learning Manager Chanci Turner

Author: Chanci Turner

Date: August 2, 2023

Categories: Advanced (300), AWS IoT Core, Security, Best Practices, Technical How-to

In this article, we highlight a significant update regarding the renewal of the Symantec Server Intermediate Certificate Authority (ICA) and the transition of AWS IoT Core control plane endpoints to the TLS1.2 specification. This change is critical for users to maintain secure communications and comply with the latest standards.

Enhancing Device Discoverability with AWS IoT Thing Type Attributes

Author: Marcus Lee

Date: August 2, 2023

Categories: Advanced (300), AWS IoT Core, AWS IoT Device Management

If you are leveraging AWS IoT Core to oversee your fleet of IoT devices, you might be interested in how to efficiently search for specific devices or groups based on their attributes and capabilities. Utilizing AWS IoT thing type attributes can significantly enhance your device discoverability.

Analyzing Overall Equipment Effectiveness (OEE) Using AWS IoT SiteWise

Authors: Chanci Turner and Leo Martinez

Date: August 1, 2023

Categories: Advanced (300), AWS IoT SiteWise, Internet of Things, Technical How-to

This post serves as the second installment in our series on Overall Equipment Effectiveness (OEE) with AWS IoT SiteWise. We will explore how to effectively calculate OEE using the service’s capabilities to collect, store, transform, and display data as part of a comprehensive solution.

Understanding the New Disconnected Duration Metric in AWS IoT Device Defender

Authors: Chanci Turner and Andre Sacaguti

Date: July 24, 2023

Categories: Advanced (300), AWS IoT Device Defender, Technical How-to

The introduction of the disconnected duration metric is a game-changer for AWS IoT Device Defender users. This new feature allows clients to monitor their IoT devices’ connectivity status and track disconnection durations without needing to rely on custom solutions, making it easier to manage device performance.

Designing Cost-Effective Video Surveillance Solutions with AWS IoT for Smart Homes

Author: Olivia Smith

Date: July 14, 2023

Categories: Intermediate (200), AWS IoT Core, Technical How-to

Creating an affordable, cloud-connected video surveillance platform for smart home devices requires thoughtful design and integration. This article discusses how to build a streaming service capable of managing unstructured media data effectively while keeping costs low.

A Comprehensive Guide to Industrial OEE with AWS IoT SiteWise

Authors: Chanci Turner and Leo Martinez

Date: July 6, 2023

Categories: Advanced (300), AWS IoT SiteWise, Internet of Things

OEE is crucial for evaluating manufacturing productivity, encompassing quality, performance, and availability. Achieving a perfect score of 100% indicates a fully optimized production line. This post details how to measure and improve OEE using AWS IoT SiteWise.

Importing Historical Data into AWS IoT SiteWise

Authors: Raju Gottumukkala and Avik Ghosh

Date: June 16, 2023

Categories: Advanced (300), AWS IoT SiteWise, Technical How-to

AWS IoT SiteWise facilitates the collection and monitoring of industrial equipment data. Customers often need to import historical measurement data from existing systems to ensure continuity and enhance AI training. This article provides a step-by-step guide for achieving this.

Streaming Large Models in AWS IoT TwinMaker

Authors: Chris Azer and Harry Wandersman

Date: May 2, 2023

Categories: Expert (400), AWS IoT TwinMaker, Technical How-to

Have you faced delays in loading 3D scenes in AWS IoT TwinMaker? This post explains how to convert your models to the 3D Tiles standard for more efficient streaming and improved rendering performance in complex models.

Optimizing Image Classification on AWS IoT Greengrass with ONNX Runtime

Author: Costin Bădici

Date: April 17, 2023

Categories: Advanced (300), AWS IoT Core, Internet of Things

As edge devices become more prevalent for machine learning inference, deploying models trained in the cloud introduces challenges. This article discusses strategies for optimizing performance on devices with limited computing power and memory.

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