Moving legacy IoT systems to AWS IoT Core can be challenging, especially when devices rely solely on usernames and passwords for authentication. However, there’s a way to make this migration smooth and minimize the corresponding risk. Starting the Migration: Leverage Custom Authorizers: Utilize a custom Authorizer Lambda function of AWS IoT Core to authenticate devices using their existing credentials. This approach allows your devices to communicate with AWS without significant changes.

1️⃣ Devices: These are the “things” in IoT (for example, sensors, cameras, smartwatches, locks, and industrial robots). Those devices collect data and/or perform actions impacting their surroundings. They are the starting point of the IoT data pipeline. 2️⃣ Edge: At this layer, data processing starts closer to the devices. Gateways and edge servers filter, preprocess, and transmit relevant data to reduce latency and optimize bandwidth usage. 3️⃣ Backend: Here lies the IoT brainpower - cloud platforms and backend servers process, analyze, and store data.

AWS added propagating attributes - contextual metadata from thing attributes or connection details. What does it mean? Previously, we had to configure and deploy IoT Rule to extract the Client Id and include it into the MQTT Message Payload. I used it during various scenarios as it was a convenient way to enforce the tight security posture of IoT deployment. According to AWS documentation, propagating attributes deliver the same enrichment without executing the IoT Rule.

In the second video of our AWS IoT and AWS Timestream series, we focus on granting AWS IoT Core permissions to interact with the Timestream database created in our previous episode. This video guides you through creating an IAM Role with the necessary trust relations and IAM Policies using AWS CDK for Python. By the end of this tutorial, you’ll have a secure setup allowing AWS IoT Core to describe Timestream endpoints and write records to a specific table.

In this first episode of our mini-series, I walk through setting up the AWS Cloud Development Kit (CDK) on a local machine. In the future, we will deploy an Amazon TimeStream database to handle time series data from a simulated device. This video covers creating a virtual environment, installing necessary Python packages, and deploying a TimeStream database and table using AWS CDK for Python. The source code is available on GitHub: https://github.

Join me on the Internet of Things Equation podcast as I chat with Davy Demeyer about his remarkable journey from Belgium to China and back, paving the way for innovations in Design-Ops and automation. We delve into the nuances of digital transformation, the importance of roadmaps, and overcoming the pilot purgatory in industrial projects. Davy offers a wealth of knowledge on designing scalable systems and avoiding common pitfalls in digital transformation projects.