Get ready to start hearing a lot more about edge computing. Increasingly, it’s a critical part of the Internet of Things (IoT) ecosystem and how such devices work. Companies that want to provide IoT services must understand it well to serve their customer needs best.
With edge computing, data is processed at the periphery of the network instead of back at the data center. This gets it as close to the original source as possible, which is very important for devices that must make instant computing decisions. It allows for more rapid processing of the data, which is essential for the use of distributed parts sprung across a network. This trend is going to accelerate very quickly, especially when you consider the standardization that is happening with IoT. Cars, routers, home appliances, wearables, and many other device types are going to leverage this technology to improve their service and performance.
Of course, this means that what edge computing produces must be managed and secured. There are sensors, actuators, and other components that are connecting to cloud services and talking to one another in real-time. All this data being generated from the “edge” must be processed, shared, and interpreted by the devices for them to perform the required functions. And it must be done securely - edge computing doesn’t mean that no data is going off to the cloud, instead it means that much of the actual computing is happening on the device thanks to a connected swarm of sensors and other input devices.
At the edge, IoT devices are equipped with sensors. They’re found in everything from a car to a smart television or security device. They are constantly gathering information like the temperature, GPS coordinates, movement, and other data points. There’s a complex architecture that sees event data and act upon the event stream, eventually through a distributed set of rules. A relevant example of this has been showcased in the last Mobile World Congress by Qualcomm, through the adoption on its flagship silicon platform Snapdragon of pretty complex neural networks based algorithms for visual recognition – directly on the edge devices.
This transition to edge computing presents an incredible market opportunity. That’s because edge computing is projected to be a multibillion dollar tech market. Consider solar panels in the middle of the desert or water pressure indicators in a reservoir. Such endpoints must make immediate decisions based upon readings. As nearly every device and accessory around gets “smart,” these devices are going to need to be able to perform rapid decisions. Any business that wants to succeed in IoT must do so with an understanding of edge computing.
Real-time analytics are, therefore, a critical piece of this infrastructure. Any IoT device must be able to take stock of its data securely and quickly and make decisions about how it is best going to assist the user.
This makes the issue of security more paramount. In 2016 a Mirai botnet – ShadowKill – almost teared down the MTN infrastructure (see more on it on Wired) To protect the flow of data to the different hubs in the edge and for anything that connects to a cloud service, good practices must be followed. Security constraints like device authentication must be used to protect yourself from hackers. There are several good strategies, as outlined by developer Rafael Rocha:
● Data routing or send commands to the things using field protocols
● Persisting data to storage system
● Routing aggregated data to the cloud using cloud protocols
We’re not only ready for the increasing challenge of building for the world of edge computing, but our past work illustrates how we’ve been a leader in this area. We believe in the Axiros AXvantage: any protocol, any device, any service - any time. We’re a recognized leader in TR-069 and device management. Our company successfully transitioned into the Internet of Things, recognizing the almost limitless possibility for how this would transform personal and enterprise technology.
Check out our offerings with regards to analytics, self care, and open device and service management. We understand the customer experience and have the hardware and software experience to guide customers through transformative changes like edge computing.