How Edge Signal Enabled thinkRF to Operate at Scale

thinkRF, a leader in software-defined spectrum analysis platforms, turned to Edge Signal to transform their perpetual business into a SaaS model by remotely managing their spectrum analyzers, associated edge infrastructure, and applications.

Providing ultimate visibility to wireless network operators and national regulatory agencies to empower them to optimize and utilize RF spectrum, thinkRF has a suite of spectrum analyzers, and they were using a PC application to access and process the spectrum data. thinkRF not only provides real-time data, but also historical insights to guide wireless network operators in making informed decisions about their spectrum policies. The use of a PC application was inefficient and prevented thinkRF from scaling their businesses quickly as demand increased.

By employing Edge Signal on AWS, thinkRF transformed their perpetual business into a SaaS model by remotely managing their spectrum analyzers, associated edge infrastructure, and applications. This enabled thinkRF to operate at scale while ensuring high quality with reduced device downtime.

Solution Architecture

Edge Signal allows thinkRF to smoothly register their edge nodes for wireless spectrum intelligence. Once the nodes are registered, the data is automatically onboarded to AWS IoT Greengrass and Edge Signal platform together. Edge Signal deploys all the edge applications and the analyzed spectrum data is directly sent to thinkRF cloud without traversing Edge Signal cloud. thinkRF benefits from the monitoring, deployment, and management capabilities of Edge Signal to operate remote nodes centrally.

Solution Benefits

Since thinkRF began using the edge computing infrastructure management capabilities provided by Edge Signal, they now focus on Cloud Analytics and AI Processing of the spectrum data. This quick transformation into a SaaS business has enriched their core functionalities by eliminating time spent on infrastructure delivery - the result being a 9-month improvement on go-to-market and double the productivity, 99,999% uptime, and a 100% reduction in operational costs, provisioning, and go-live processes.

The transformation from a hardware company to a SaaS model gave thinkRF the ability and confidence to onboard more customers worldwide without thinking of infrastructure management complexities. Currently, thinkRF is running their devices all across Canada, UK, and South America, with plans to expand further.

As a SaaS solution, there are no upfront costs to use the Edge Signal platform, which further supports thinkRF’s aggressive expansion plans.

Jim Roche, CEO at thinkRF explains: "At thinkRF, we pride ourselves on producing unparalleled spectrum intelligence systems that help our customers stay on top of their wireless networks. But to do that, we need the ability to manage our devices and applications at the edge, where the data is being generated. That's where Edge Signal comes in. Edge Signal allows us to remotely manage our devices and applications, so we can make sure they're running smoothly and efficiently. This has helped us reduce downtime and increase productivity. It's also helped us improve the overall performance of our spectrum management products, so our customers can get the most accurate and reliable data possible. Overall, Edge Signal has been a game changer for thinkRF. It's helped us to stay ahead of the competition and continue to provide our customers with the best possible products and services. We look forward to seeing what other future benefits it will bring!"

About Edge Signal

Powered by Wesley Clover International, Edge Signal provides a simple solution to integrating edge devices and data into the cloud as well as managing all aspects of edge applications. Customers choose Edge Signal to reduce time-to-market and to optimize their edge applications. Edge Signal also enables devices and gateways to be controlled, made more secure, and updated without costly on-site technicians or remote hands. Application developers can create solutions, including edge Artificial Intelligence (AI), Internet of Things (IoT), data aggregation, etc. from variety of sources without having in-depth knowledge of edge computing and its complexities.

Burak Cakmak, Chief Technology Officer at Edge Signal, explains what role AWS plays when it comes to the Edge Signal platform:

“When creating the Edge Signal platform, we decided on a pure serverless architecture. AWS Lambda gives us flexibility to run our workloads. First, we deploy AWS Lambda for our CRUD operations. We use Amazon API GW to trigger our functions which consume REST requests. Second, AWS enables our microservice-to-microservice event-driven architecture. We never invoke one function from another. Everything is event-driven, and with Amazon Simple Notification Service (SNS) and Amazon Simple Queue Service (SQS) we can trigger our functions in an async way. Third, AWS helps us with ingesting data from our device agents. Thanks to AWS IoT’s rules, you can run SQL-like queries on streams, and trigger the Lambda functions depending on your needs.

AWS IoT has a broad set of sub-services we also use in the Edge Signal platform. We employ AWS IoT Greengrass for containerized and native application deployment. And AWS IoT MQTT is integral to creating a bi-directional communication channel between devices and cloud services.

Amazon Timestream database helps us store and query timeseries data in an efficient way.

There are other AWS services Edge Signal employs, but AWS Cloud Development Kit (CDK) deserves special mention. Our system is immutable and easily deployed, and AWS CDK helps us spin up a new instance of frontend and backend easily. Since we develop our IaaC with Typescript, it’s aligned with our main programing language, and testable.”

Conclusion

As the thinkRF example shows, applying edge computing capabilities to spectrum intelligence can lead to more efficient, responsive, and secure management of wireless communication networks. Using Edge Signal empowers thinkRF and their and their global client base to make intelligent decisions in real-time, optimize spectrum usage, and enhance the overall performance of wireless communication technologies.

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