Real-World Examples of IOT edge computing

Real-World Examples of IOT Edge: IOT Edge computing is a framework that brings computing in the internet of things closer to the devices, which are the endpoint of the IOT network. By bringing the data processing closer to the Edge, it accelerates the processing, reduces the time taken for making decisions, and thus drives real-time action. IOT edge computing has gained enormous frenzy in recent years. Different versions of edge computing like fog computing have also gained momentum. Various applications, solutions, hardware, software have come into the market to enhance and propagate the adoption of edge computing among the IOT implementers. There are going be to be several advantages for the early adopters of the technology. Edge computing has also advanced to include AI and machine learning customized such that it is adaptable to edge computing. One of the significant benefits of Edge computing is the reduction of latency.

There are several early movers with IOT edge adoption. We will look at some of the real-world examples of where edge computing has been implemented and where it is bringing in operational efficiency.

Indoor Farming

Vertical Indoor farming has been an exciting field that benefits significantly from IOT. In indoor agriculture, the plants are mounted with sensors that track the water needed for the plants and the environmental conditions of the plants. The data from these sensors helps to adjust the operating conditions for these plants to achieve the maximum nutrients, flavor, and avoid wastages. These data also provide information when the vegetables can be picked and thus avoid wastage. Building an edge solution in this environment helps in the timelines of the decision and improve the growing environment.

Autonomous Vehicles 

Autonomous Vehicles is another interesting domain that can immensely benefit from edge IOT computing. Driverless vehicles need to analyze much information regarding their surroundings and communicate with the other cars on the road. An autonomous vehicle can send a large volume of data from all its sensors for it to perform optimally and efficiently. And as more and more self-driving cars are introduced, the data being sent by these sensors from all cars could put an enormous strain on the network bandwidth. If all these data needs to go to the cloud for processing, it will be killing the network. So these self-driving cars will benefit significantly if these have edge computing implemented. Some of the decisions by the vehicles can be local if the processing and AI are locally available.


The healthcare industry is another use case that can immensely benefit from edge implementation. It helps to localize the data processing for patients in the hospitals. Rural areas would be most beneficial in diagnosing and administering the action. 

Industrial Automation

Many factories are now adopting the internet of things, and edge computing will be one of the enablers for smart manufacturing. Sensors implemented in the factory gather data on the machine and factory conditions. Localized processing enable action on these data in real-time. For example, predicting a machine failure in real-time can help in taking corrective action to fix the machine in time and thus save a lot of time and cost. Similarly, data gathered can also help to determine the quality of the product in the factory and can drive any fine-tuning necessary to make changes in real-time.

Smart City

A smart city project includes traffic management, smart buildings, smart parking, smart streetlights. Data from a smart city could be voluminous. Sending this amount of data to the cloud could strain the bandwidth of the network, and getting a real-time response for the data could be not as quick as needed. Implementing edge computing can help some of these entities to make an autonomous decision and in real-time.

Real-World Examples of IOT Edge: When large amounts of data needed to process in a time-critical way, Edge computing would be the way to go. Edge computing can handle a large volume of data and improves the response to events by processing the sensor data locally. This process removes the need to send a large amount of data to the cloud and reduces the time to respond to the event. Also, lowering the cost involved in storing large volumes of data in the cloud. It also reduces the network bandwidth utilized in sending large amounts of data to the cloud. It also helps in securing the data by maintaining the data locally.


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