Normal IoT systems collect data and send it to the cloud or central data center for it to be analyzed and distributed. With billions of connected devices this results in several problems
- The more connected devices the more bandwidth is needed. Many places just don’t have the data pipe to support the number of devices they are running.
- Latency can be another issue. Basic fact of network life is that the more data you put on a network the slower the network operates. This is caused by bandwidth issues and server load.
- Not all connected devices are located in areas that have good network connections. Sensors located in remote places or on moving vehicles may get intermittent and poor internet connections.
- Security of data can also be an issue. Sending data across the globe to a data center provides multiple opportunities for a bad actor to intercept the data and use it in a nefarious way.
- Time can also be a factor. Some data that is collected is time sensitive and needs to be acted on as quickly as possible. Having the data sent to the cloud where it is analyzed before it can be used could result in a delay that may create a problem that the system was supposed to prevent.
Edge computing is a way to overcome these problems. By placing small computers with limited processing capability at the edge of the IoT system (physically near the sensors) data can be collected, have a basic analysis and acted on in near real time. The analysis of the data can then be uploaded to the cloud for further analysis and distribution.
The edge computers can also be set up with very basic AI allowing them to control processes in near real time without human intervention. The system can also be designed so that the edge computers can be accessed locally or over the internet allowing for monitoring and adjustments.
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