- Amazon Web Services announced the availability of AWS Greengrass to all of its cloud customers, including those using the healthcare cloud. AWS Greengrass is a software that allows organizations to securely run local compute, messaging, data caching, and synch capabilities for connected devices.
The tool provides connected devices with the means to communicate with one another even when they’re not connected to the internet. The connected devices run AWS Lamdba to leverage the organization’s full AWS Cloud processing, analytics, and storage power.
AWS is widely distributing Greengrass because of the increase in Internet of Things (IoT) devices across all major industries. The healthcare industry in particular is experiencing exponential growth in the number of connected medical devices incorporated into health IT infrastructure.
Many healthcare organizations use AWS Cloud to store data and run applications. Medical IoT deployed by entities already using AWS Cloud can leverage Greengrass for better communication among devices and to more easily use AWS Cloud tools and services.
IoT devices often have limited processing power and memory because they are not located in the data center. However, a total cloud-based health IT infrastructure deployment is unrealistic. There are times when certain tasks need to be done locally on the device at the edge of the network.
Programming and updating IoT devices also puts a heavy burden on IT departments because of physical device limitations, as well as IT staff shortages. AWS Greengrass gives medical IoT devices the flexibility to rely on the cloud or perform tasks on their own when necessary.
“By embedding AWS Lambda and AWS IoT capabilities in connected devices, AWS Greengrass gives customers the flexibility to have devices act locally on the data they generate while using the AWS Cloud for management, analytics, and storage – all using a single, familiar AWS programming model,” AWS Vice President of IoT Dirk Didascalou said in a statement.
“We are excited to make AWS Greengrass available to all AWS customers, and with AWS partners shipping AWS Greengrass-capable devices it is now incredibly easy to build and run IoT applications that seamlessly span devices on the edge and in the AWS Cloud.”
Many of AWS’s most prominent partners see the value in widespread adoption of AWS Greengrass. Partners including Intel, Lenovo, Qualcomm, Raspberry PI, and Samsung are currently working on integrating AWS Greengrass into platforms so their devices will be produced with the tool built in.
“The next generation of IoT devices and gateways depends on high-performance edge capabilities tightly integrated with the cloud,” Qualcomm Technologies Vice President of Business Development Jeffery Torrance said in a statement.
AWS Greengrass reflects the growth of edge computing and the need for computing architecture to exist close to where the data is generated.
The push from centralized cloud services comes from organizations migrating commuting tasks to the cloud. Centralized cloud computing is efficient because of the computing power offered by the cloud. However, data is being produced at the edge in quantities that are too large to be passed to the cloud due to bandwidth restrictions.
A clinician’s mobile device is the edge between the patient, who is the data source, and the cloud. A clinician treating a patient with a tablet will be able to enter patient data into the analytics platform at the edge where it is processed and displayed in near real-time. Patients no longer need to wait for analytics results, which may reduce their number of visits.
The volume of IoT devices, along with the volume of data they constantly produce, causes a bottleneck for cloud-based computing. Cloud-based computing is more efficient than edge computing, but bandwidth restrictions make edge computing the better choice for near real-time analytics.
Edge computing tools support the healthcare industry’s ambitions to further deploy big data analytics solutions supported by IoT devices. The ability to process data at the edge of the network results in near real-time analytics, helping clinicians make more accurate diagnoses at the point of care.