Blog Energyly - Energy Monitoring Devices

Using Edge Analytics

Apart from rampant conception of sensors, the more of industrial internet of things data collected is never analyzed. Many existing iOT platform solutions are slow, expensive and a drain on resources which makes analyzing the rest tangled. The only benefit of this data will come from analysis.

A rather new coming namely Edge Analytics is in use to address these issues that attempt to collect data in decentralized environments. It’s about analyzing in real-time on site.

Edge Analytics refers to analysis and collection of data from some non-central point system at the edge of the network, near the source of data that can be anything from a car, or wristwatch, sensor, switch, to an industrial component which gathers data from several machines in a factory.

edge analytics

WHY Edge Analytics?

It is very important to understand that where edge analytics makes sense. For example, sensors in train or at stop lights that provide intelligent monitoring and management of traffic should be powerful enough to raise an alarm to nearby fire or police departments based on their analysis of the local surroundings. So these kinds of event make more sense to be processed locally rather than sending them over the network for analysis.

Edge architecture

In Edge architecture, devices can be of three types depending on their role:

  1. Edge Sensors and Actuators are special purpose devices connected to Edge Devices or Gateways directly or via low power radio technologies.
  2. Edge Devices are general purpose devices that run the Edge intelligence, i.e., they run computation on data they receive from sensors and they send commands to actuators and  are connected to the Cloud either directly or through the mediation of an Edge Gateway.
  3. Edge Gateways act as arbitrators between the Cloud and Edge Devices, possibly offering additional location management services

Exciting factors for using Edge Analytics

  • Preserve privacy
  • Reduce latency
  • Be robust to connectivity issues
  • Fast application performance
  • Support larger data bytes in smaller footprint
  • Full range deployment models anywhere