Advantech recently announced that its UNO series of IoT edge gateways has qualified for AWS IoT Greengrass, an IoT open source edge runtime and cloud service that supports device software development, deployment, and management. This means its UNO series of gateways (UNO-137, UNO-148, UNO-2271g V2, UNO-2372g, UNO-410 and UNO-430) are compatible with most mainstream cloud services and pre-built software components for cost-effective provides the ideal platform for local software development.
The gateway is a rugged, fanless system that can be easily deployed and maintained in industrial, outdoor/curbside, in-cabinet and hazardous/flammable installation environments. The gateways are also installed with the company's WISE-DeviceOn IoT management software, which supports data collection and visualization, fault prediction/prevention, logic control and remote management tasks. Thus, with AWS IoT Greengrass compatibility, the gateway provides a highly adaptable edge platform that facilitates low-cost on-premises development of edge-to-cloud IoT solutions.
The gateway is configured with multiple I/Os, integrates a secondary expansion stack, supports Wi-Fi, LTE, and 5G interconnect modules, and supports a variety of IoT and automation applications. They also feature edge container technology, support for third-party container-native applications, and support the deployment of cloud services as distributed computing resources. By leveraging the AWS Partner Network, the AWS IoT Greengrass Gateway enables OEMs and their customers to simplify, accelerate and optimize the IoT device development journey.
Integrated with AWS IoT Greengrass, the family of IoT edge gateways seamlessly extend AWS capabilities and cloud intelligence to the edge. For example, through AWS IoT Greengrass, AWS Lambda functions, and pre-built software modules, edge applications such as streaming analytics, machine learning, image recognition, and other high-value AI applications from cloud to edge can be produced for local execution. Similarly, the Amazon SageMaker Neo DLR and TensorFlow Lite frameworks enable machine learning inference at the edge by using cloud-trained models. This enables on-premises devices to process locally generated data while performing storage, analysis, visualization, and decision-making tasks through the cloud, simplifying data processing operations, and enabling the convergence of OT and IT.
