IoT Edge Computing – Use Cases & Applications

IoT Edge Computing: IOT devices have been growing at an explosive rate, and it is estimated that there will be approximately 31 million devices by 2020. The IOT is funneling large volume of data from the sensors, as the IOT devices have been growing, it also drives the growth in volume. The data from sensors are driven to storage in the cloud for analysis and inspiring action. Most of these storages are not on premises, and the large volume of data has been driving cost, the bandwidth of the network and also raising the security concerns. The last couple of years have seen the term edge computing been commonly used with IOT. The edge computing is bringing the storage and computing closer to the source of data generation.

IoT Edge Computing is bringing a decentralized approach to data processing. Cloud computing system was offering a centralized approach. With Edge Computing the resources are moved closer to the source of data generation. Several benefits are driving the increased interest in Edge computing. The most significant driving factor for edge computing is reducing the network latency by moving the Real-time data processing closer to the source and reducing the network bandwidth utilization and bring in security.

Here are some use cases where IoT Edge Computing is bringing principal value.

Edge computing for Smart Home devices

When the smart home started picking steam most of the Smart home devices were sending the data to the cloud, allowing the users to access the data from anywhere. This way was cool because it allowed a smart home user to turn on the coffee maker or light or set the temperature to the right one before he enters the homes. However, a smart home security camera uploading the data every second or the data from all devices in intelligent home uploaded to the cloud is just going to increases the bandwidth, and it may not be necessary as well. For example, NEXT has come up with the latest home security camera where the data is uploaded only when it detects faces, which is an outsider. This process could save bandwidth, cost of cloud usage, and helps to act faster since the processing is done at the edge.
Wearables: Another great example is wearables and fitness trackers. These devices need not have to be always cloud-connected and having edge computing setup can significantly drive the real-time actions for health data

Retail solutions

Similarly, IOT in retail solutions can leverage the benefits of edge computing. Retail advertising utilizes information which local to the community or store and does not require cloud processing. The edge devices equipped to analytics on the data gathered by instruments in the field can be fast and cost-effective

Smart Grids

Smart grids have been there for a while. They are a way of establishing two-way communication between the power distribution network and the end consumer. Most of the time the data is relevant to geography covered by the smart grid, and it does not warrant the need to drive all the data to the cloud if the smart grid can have local processing and learning and actions implemented

Smart Agriculture

Another potential usage vertical could be the smart agriculture industry leveraging IOT. The sensors in the fields are gathering data on the of the field and crop condition. This data is then transmitted to cloud and data processed, analyzed, and actions recommended. Edge could reduce a large amount of data transmitted up and down the network or halfway across the geography. If an edge computing is leveraged, this is going to save cost and bandwidth. All the data could be processed, and the local computing and analytics drive actions.

Smart Hospitals

Another potential opportunity for edge computing is in hospital. In Hospitals, patient data is highly confidential, and there is a large volume of data processing. In both scenarios it makes sense to follow an edge computing architecture instead of pushing all the data to the cloud can help in reducing the network latency

Autonomous cars

Autonomous cars is another excellent example where having edge computing setup can help automobiles to process the data in Realtime independent of a network connection

Edge computing is changing the way industries are optimizing and taking real benefits of the IOT. More and more implementations are seeing the benefits of having processing closer to the source of data generation. The application of edge computing can vary greatly. It could be a primary edge solution for event filtering and low data processing or a more complex gateway like implementation for processing a large batch of data with machine learning and analytics capabilities implemented.

Conclusion

There is a specific risk in the edge computing implementation like any new technology. One of the risks is the cost associated building computing resources near all the edges. For example, the cost of building edge devices, which has the power and applications needed for processing at every edge location vs. having it all on the cloud is something to be weighed on.

IoT Edge Computing has enormous potential, but that doesn’t mean that cloud computing for IOT is going away. A hybrid architecture which embraces both technologies and leverages its benefits will be a sustainable solution for the long term.

Footnotes:

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