Data in IOT should go to Edge or Cloud?

IoT has been bubbling for a long time, and everyone has been excited about the potential of IoT and the value it can produce . IoT can produce a large volume of data, and there has been a discussion on how the data can be used to produce meaningful intelligence. Irrespective of which vertical or market segment is using IoT the question has been how to unlock the potential of the data it is producing indeed. So the answer is Edge.

Cloud Vs. Edge


We know the importance of cloud in business. Its viewed as an extension of the business information. For IoT, the cloud has been a vital building block as its the storehouse of IoT data.

Another reason why cloud plays a vital role is because of the way the IoT systems are built – to house little resource and have lesser computing power. So the natural choice is to leverage the cloud to host all the data and do all the necessary computing. If you take the example of the sensors tracking weather or traffic, typically the hardware is chosen such that the device is small, power efficient and is capable of doing just the data collection. So these sensors have the power to gather the data. Any computation or analysis of this data has to be performed on the extension units. Typically it is the cloud. Cloud holds the data sent from Edge, and the analytics engine is run on the data on the cloud to interpret the data.

Moreover, if we have multiple sensors across doing the same work in the different region, there is going to trillions of data generated by all these sensors. Sometimes these sensors are so geographically distrubuted. This challenges distributed cloud computing.The question arises, do we have to push all these data to the cloud. Should we have a better filter, the solution is the Edge.

IOT powered by Edge

Edge computing pushes the data ingestion and data computation closer to where the data is collected. Edge helps to unlock the potential of the data better and faster. Edge also prevents sending irrelevant data to the cloud, thus reducing network latency and reducing the straining of centralized computing. Edge computing also enables real-time data analytics.

These tools need to be able to soak up the ongoing boost in information volume, speed, and range intensified by increasing geographical circulation. The edge analytics efforts need to make information actionable at a specific time where cloud computing would have taken longer to obtain worth.

Edge is the new Buzz word


Companies have started realizing the potential of the Edge in IOT. There have been many acquisitions by the big fishes in the IOT industry; Cisco acquired ParStream, a large information analytics business to push real-time analytics to edge.

Edge computing minimizes the quantity of volume business needs to move, the range the information should take a trip, and the expenses related to dispersing information throughout diverse places and saving it centrally. Edge computing alleviates pressure on central information shops and makes it possible for the business to carry out more reliable, localized real-time analysis of information. The business that can effectively carry out edge-computing architecture can lower latency, enhance facilities, and increase the worth of their information.

Edge computing looks to press the collection and processing of sensing unit information out of the central information shops and into edge places, permitting business to open more worth from their information. CDNs press popular material out to the Edge of a network, caching a subset of the aggregate offered information. Edge computing procedures sensing unit information as close to the sensing unit as is optimum, enabling business to prevent both network latency, and frustrating central facilities.

The way forward with Edge


Edge computing does not solve all the problem in IOT, but it is the logical next step. It does not mean it should be the only solution forward. The thought of using either of centralized cloud computing or near to the edge data processing is a wrong way forward. They are not mutually exclusive. Using a combination approach, probably the right way for best IOT solutions.

The difficulty is that a great deal of edge computing today is performed in an advertisement ad hoc style. Nodes like IoT sensing units and mobile phones produce information at the network edge. However, they do not always come from a thought-out method to edge computing. Organizations that wish to obtain long-lasting worth from their information must execute a method that combines existing facilities, both central and cloud, with brand-new tools purpose-built for IoT and edge analytics.

Footnotes:

  • Mobodexter, Inc based in Redmond, WA builds IOT Edge solutions for enterprises applications on Kubernetes & Dockers.
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