Edge Computing Explained
Edge Computing allows data produced by sensor-rich assets like machines, equipment, vehicles and devices to be processed closer to where it is created.
Real-time Edge Processing
Real-time local processing and analysis of the data enable advanced filtering, aggregation, and compression of data resulting in significant savings in bandwidth and data storage costs.
Trigger Local Actions
If an anomaly is detected then real-time local actions can be triggered. Use-cases that involve moving objects, people, goods or assets that contain oil, gas, water in movement can benefit hugely from ultra-low latency.
It also opens up for ultra-low latency communications at the edge as the data doesn´t need to be sent across long routes to data centers or cloud services.
Smart Data Collection
Data collection for advanced Support Programs or Predictive Maintenance often requires Edge Computing.
Edge Computing Architecture
There are two main deployment scenarios in Edge Computing. For sensor-rich assets that typically are remote or mobile the Edge Computing software is deployed embedded, on-asset. This is the true edge.
The other scenario is where the Edge Computing software is deployed in a middle-layer between the device and the cloud. This scenario is also called Fog Computing.
Benefits with Edge Computing
There are several business and technical benefits with an Edge Computing architecture compared with a centralized/cloud computing model.
- Significantly reduced Cloud data volumes and cost
- Less bandwidth and network cost
- Enables local intelligence for off-grid scenarios or where connectivity is scarce or unreliable
- Ultra-low latency for M2M actions
- Increased security
- Independence from Cloud Services