What is Edge Analytics
The concept of Edge Computing, first created in the 1990s was aimed to bring computation and storage closer to the target location in order to improve connection speed, response time and save bandwidth.
Edge analytics on the other hand emerged after the Internet of Things (IoT) and described a particular branch of edge computing, that is collecting, analyzing and creating insights from the IoT devices at target location and sometimes inferring a result.
How does Edge Analytics Work?
Edge analytics tools generally follow this workflow
- Devices or Sensors gather data at the edge.
- Analytics abilities within the devices to allow data processing at the edge.
- If there is a need to take action at the end device, it takes actions so depending on the outcomes of the analysis. For instance, Rulex is a seller that delivers autonomous functioning judgments with actual analytics at the edge.
- Related data is transferred from the edge to the cloud so companies are able to see the big image by summing up precise data from thousands of devices.
Examples of Edge Analytics
One example of Edge Analytics may happen at the control center of an autonomous truck fleet. The truck conveys travel at a certain distance between one from another, and accelerates or decelerates at an optimized level to save fuel and time. With edge analytics, the control center will holistically gather data from the entire fleet and share it with each individual truck so the on-vehicle edge computing processor may respond to road conditions and make decisions better.
In telemedicine, monitoring devices (chronic disease monitors and sensors) are not currently connected. There is a large amount of unprocessed data at devices that can be shared to a cloud data hub. Hospitals or the telemedicine provider can enable real-time notification, suggestions, and health practices by gathering data at their analytics center and improving their devices.
Centralized data and decentralized computing
As the numbers of sensors and network devices grow and generate more data, data analytic resources will experience more strain and shortages. By enabling devices to scale up their processing and analytical capabilities, data can be processed more efficiently. That being said, edge analytics is not a replacement for centralized data analytics. Local and central computing should work together to improve response time and the quality of decision making. The main goal for edge computing is to reduce latency and produce faster insights, though it requires a central data hub where the data can be aggregated and shared to.