Category: AWS IoT Greengrass

Introduction Organizations install hundreds of Internet Protocol (IP) cameras to increase security by surveilling indoor and outdoor spaces. This is a common need for manufacturing plant floors across industries such as automotive, commercial, Oil & Gas, public safety, and agri-tech. Companies connect cameras to the cloud to create a centralized view of their siloed data […]

Post by Angela Wang and Tanner McRae, Senior Engineers on the AWS Solutions Architecture R&D and Innovation team This post is the third in a series on how to build and deploy a custom object detection model to the edge using Amazon SageMaker and AWS IoT Greengrass. In the previous 2 parts of the series, we walked […]

Introduction In this post, we will be discussing how to manage Docker container lifecycle using an AWS IoT Greengrass custom component. There are five phases in a Docker container lifecycle: create, run, pause/unpause, stop, and kill. The custom component interacts with the Docker Engine via the Docker SDK for Python to manage processes based on […]

Introduction We are excited to announce that AWS IoT Core now supports MQTTv5 features that help enhance communications of large-scale device deployments and innovate device messaging patterns. Customers who already have MQTTv3.1.1 deployments can make use of the new MQTTv5 features as AWS IoT Core provides seamless integration between both versions and supports heterogeneous deployments […]

Introduction When devices are deployed behind restricted firewalls at remote sites, you need a way to gain access to those devices for troubleshooting, configuration updates, and other operational tasks. This is where, secure tunneling, a feature of AWS IoT Device Management has been helping customers to do remote tasks. To help elevate customers even further, AWS has […]

Introduction Machine learning (ML) at the edge requires powerful edge requires powerful edge devices with a unique set of requirements. The availability, safety, and security requirements for the edge differ from cloud since they are located at the customer site, outside the data center, and interface directly with operational technology (OT) and the internet. Since […]

Introduction Autonomous mobile robots (AMRs) are widely used in many industries like logistics and manufacturing. However, there are various challenges in developing and operating autonomous robots. To develop autonomous robots, a wide range of technologies are required, and the process is complex and time-consuming. Integration with the cloud is also required to develop and operate the robots […]

Introduction Many modern industrial operations require extensive monitoring and real-time decision making for efficiency and safety reasons. 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. Edge computing is a system of micro computing/storage devices that are installed at […]

Introduction In this second-series blog post, we will continue to showcase a user interface (UI) application at the edge with AWS IoT Greengrass V2, a jointly developed solution by AWS Partner, TensorIoT, and AWS Professional Services. This UI application at the edge contains multiple custom AWS IoT Greengrass V2 components to achieve flexible IoT data […]

Introduction This post shows you how to set up a clean and efficient development environment for AWS IoT Greengrass. With this environment you can rapidly iterate on your ideas and automate your process to build edge compute systems from scratch. Building a repeatable development environment for edge systems can take a long time. These tools […]