Edge Computing
Millions of IoT devices generating trillions of transactions will simply overload the Public Cloud.
Latency and Cost will increase exponentially.
Edge computing processes and acts upon IoT data locally.
This improves latency and reduces cloud and back haul costs, attack surface and compliance concerns.
Edge AI enables devices to act in isolation if backhaul/Cloud services fail.
A core aggregation platform manages the edge estate and feeds into cloud based reporting and analytics.
Use Case : Railway Crossings
Visual and Audio IoT devices can, when combined detect failures at railway crossings.
Cloud processing of such feeds is dependent on the appropriate bandwidth and impacted by latency and security concerns.
AI based Edge computing offers a stand alone platform, with the potential for digital twinning for predictive maintenance.
Use Case : Manufacturing
An equipment manufacturer want to commuicate directly with devices located on a third party production site. Currently maintenence staff visit the site each month providing triage and software updates.
Extensible Edge is a ruggedised , DIN Rail powered(or standard 220 Volt) , physical device with LTE, Wifi and Ethernet backhaul.
It supporters Docker Containers orchestrated from the Cloud.
Each Edge, is configured with the appropriate protocol engine for the target device.
A secure overlay across the 3rd party LAN is established terminating in the manufactures operating centre, enabling real time triage and maintenence.