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AWS IoT Core for LoRaWAN comes to Sydney cloud region

AWS IoT Core for LoRaWAN comes to Sydney cloud region

Now available in five AWS regions globally.

Credit: Dreamstime

Amazon Web Service’s (AWS) IoT Core for LoRaWAN is now available in the Asia Pacific (Sydney) and Asia Pacific (Tokyo) AWS regions, in addition to US West (Oregon), extending the footprint of the offering to five AWS regions globally. 

AWS IoT Core for LoRaWAN is a fully managed capability that is designed to give AWS IoT Core users the ability to connect and manage wireless devices that use low-power long-range wide area network (LoRaWAN) connectivity with the AWS cloud.  

“Using AWS IoT Core, enterprises can now setup a private LoRaWAN network by connecting their own LoRaWAN devices and gateways to the AWS cloud – without developing or operating a LoRaWAN Network Server (LNS),” the cloud vendor said in a blog post. 

“This allows customers to eliminate the undifferentiated work and operational overhead of managing an LNS, and enables them to quickly connect and secure LoRaWAN device fleets at scale,” it added. 

According to AWS, the IoT Core for LoRaWAN offer can also make it easy for users to act on the data from connected devices using AWS services for processing, storage, analytics or machine learning.  

“With built-in integration with AWS IoT Core Rules Engine, the device data (uplink) is automatically routed and transformed according to customer defined rules,” the vendor said. “Developers can also send application messages to the connected devices (downlink) using APIs.  

“With transparent, pay-as-you-go pricing with no monthly commitments, you can cost-effectively and reliably scale your LoRaWAN device fleets,” it added. 

The AWS IoT Core for LoRaWAN offering was already availability in US East (N. Virginia) and EU (Ireland) AWS regions. 

In May, the cloud vendor made its data warehouse product Redshift ML generally available, including in Asia Pacific.  

According to AWS, Amazon Redshift ML enables developers to create, train and deploy machine learning (ML) models using SQL commands. The move allows users to “leverage” Amazon SageMaker, a managed ML service, without moving data or learning a new skill, AWS claimed. 

Writing in a blog post during its general availability release, the cloud giant said Amazon Redshift ML automatically discovers the best model and tunes it based on training data using Amazon SageMaker Autopilot. This chooses between regression, binary or multi-class classification models. 

“Amazon Redshift ML uses your parameters to build, train and deploy the model in the Amazon Redshift data warehouse,” AWS said at the time. 


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