Decoding Embedded Intelligence Over the Edge Cloud – Analytics Insight

Edge Cloud

Intelligent edge systems are improving outcomes for organizations and consumers by making instantaneous, autonomous, or semi-autonomous decisions independent of the datacentre and private or public clouds. Compared to embedded systems, intelligent edge systems are designed with more compute and sensors to enable analytics, artificial intelligence inferencing, and natural user interfaces. Also, unlike fixed-function embedded systems, intelligent edge systems can change their behaviour and provide new functionality over time based on programming instructions sent to them from the edge cloud.

The Integration of Edge Cloud and Embedded Intelligence-

Embedded intelligence is solving problems in revolutionary ways, many beyond line of sight. With the massive growth of Edge intelligence, many organizations will find that a consumption-based acquisition model will fit their needs best, given the uncertainty of future needs. Here are the revolutionary applications of Embedded Intelligence over the Edge Cloud-


• Loss prevention solutions

• Inventory management solutions

• Customer experience solutions


• Line detection and ranging (LiDAR)

• Central driver assist

• Visual systems


• Semiconductor manufacturing equipment

• Industrial robots

• Inventory management


• Computing tomography (CT) scan

• Picture archiving and communications system (PACS)

• Magnetic resonance imaging (MRI)

Initial implementations of embedded intelligence may not account for the volume, distribution, and value of big data that comes from an edge implementation. That data needs to be secured from theft, tampering, and accidental loss.

Communicating with IoT

The number of intelligent devices and smart sensors is constantly increasing maintaining such device infrastructures is becoming more and more expensive, since huge amount of data must be transferred to central data centre via the Internet. Edge computing assimilates secure computing and storage capabilities at the behest of data analytics, routing, filtering, aggregation, and device management. Edge computing facilitates advanced management functionalities to create big data warehouses thus reducing latency, connection issues and infrastructure costs. Integration with cloud platforms is thus an additional feature for a more complete, end-to-end infrastructure management.

Technological advancements have made it possible for embedded devices to communicate with IoT sensors in an effective cost-effective and simple way. Thanks to embedded software platforms, these devices can collect data and transfer them via the Internet: which are the typical functionalities of IoT gateways. The role of cloud-based data centres and cloud computing is crucial. They assimilate remote connectivity to and remote management of OT infrastructures and a place for data storage and analytics to trigger important business strategies.

Edge computing as a concept has been around the business for many years, however, today’s businesses need require huge amounts of data for quick insights. This means data needs to be synergised for quick decisions, arising the need for more computing, storage and analytics capabilities hosted at the edge. More powerful and intelligent devices mean more value extrapolated from data, cost-effective business decisions and more efficient operations for the organizations.

Share This Article

Do the sharing thingy

About Author

More info about author

Kamalika Some

Kamalika Some is an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank. An MBA (Finance) and PGP Analytics by Education, Kamalika is passionate to write about Analytics driving technological change.

More by Kamalika Some

This UrIoTNews article is syndicated fromGoogle News