Deploy a secure Cloud- connected IoT device network complete with Edge computing capabilities
endpoint systems, which are the sensor and actuator devices that make up the farthest reaches of the IoT application periphery. Although these endpoint systems connect directly with the Cloud in many IoT applications, more complex applications often require deployment of so-called Edge systems, which lie functionally positioned between endpoints and the IoT Cloud. By providing local processing capabilities inloops oroximity to a set of IoT endpoints, Edge systems can reduce latency in tight feedback loops, or meet timing requirements for industrial process controls. Edge systems provide local resources needed to process more complex algorithms such as machine learning inference, or sophisticated preprocessing routines used to clean data and reduce the volume and velocity of data driven to the Cloud. This local processing capability proves critical in supporting advanced security policies and privacy requirements such as data minimization prior to transfer across the public Internet. Enhancing IoT applications with AWS IoT Greengrass Amazon Web Services (AWS) formalises Edge processing capabilities with its AWS IoT Greengrass service, which provides a portion of its Cloud services running as Greengrass Core on the Edge device. Designed to
work closely with Cloud services running on scalable AWS Cloud resources, Greengrass provides a relatively straightforward path for deploying and updating machine learning inference models built with tools such as the AWS SageMaker fully managed machine learning platform (Figure 1).
Local processing is only one of the benefits of an Edge service such as AWS Greengrass. In providing a sort of interface buffer between endpoint systems and Cloud resources, Edge systems also play a key role in meeting IoT application requirements for reduced latency, for enhanced privacy and security,
Though much in demand, the deployment of an Internet of Things (IoT) network with Edge computing resources can be a daunting undertaking with multiple requirements for endpoint devices, Edge computing systems, and secure Cloud connectivity. Although discrete elements of the required solution are readily available, integrating them all into a seamless, efficient IoT application requires immersion in the complex tasks of implementing not only the endpoint and Edge hardware platforms, but also the service interfaces, communications methods, and security protocols required by IoT Cloud providers. Recently, a steady stream of more highly integrated IoT solutions has emerged to help developers get to market more quickly. For example, a set of Cloud-ready endpoint and Edge computing products from Microchip Technology provides
an off-the-shelf solution designed to connect easily with Amazon Web Services (AWS) IoT services and the AWS IoT Greengrass Edge computing service. This article will briefly discuss why intelligence should be deployed at the Edge. It will then introduce Microchip’s AWS-qualified boards that serve as Cloud-ready sensor endpoint systems. The article will then show how those endpoints can be combined with an Edge computing platform based on a wireless system-on-module (SOM) preloaded with AWS credentials and service software to provide near transparent connectivity to the AWS Cloud. Combining endpoint and Edge system Ready availability of low-cost, low-power systems has simplified implementation of so-called
Written by: Stephen Evanczuk Contributing Author at DigiKey
Figure 1: AWS IoT Greengrass simplifies local processing and Edge deployment of advanced functionality including machine learning models trained in the AWS SageMaker machine learning environment. Image source: Amazon Web Services
Figure 2: Within an AWS IoT Greengrass group, endpoint devices can communicate with each other and the Cloud using MQTT messaging managed by a Greengrass Core device. Image source: Amazon Web Services
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