Edge computing is a distributed computing system that uses data from the edge to process data without having to upload large amounts of data to a central warehouse. Edge computing, in other words, decentralizes data processing, application operation, and even the realization a few functional services from the network center to the nodes at each edge.
Monitoring and maintaining enterprise resources has always required a lot in terms of labor and materials. There is a strong demand for intelligent, real-time processing of large amounts of data to support digital transformations of power, manufacturing and other industries. The amount of data generated by the network’s nodes will exceed that of a traditional mode of Internet of Things construction.
These data can be handled using traditional cloud resources, but it is too difficult to guarantee low latency real-time operation of certain devices. Also, the data security risk for special information is significantly increased.
The theory behind edge computing is that it can meet many industries’ key requirements in terms of speed, real-time performance and data optimization. It also provides privacy and security protection.
We have ten important examples of edge computing use cases. There are too many to list.
Related post – What is Edge Computing?
1. Autonomous vehicles
The first autonomous vehicle use case will be the platooning and auto-driving of truck convoys. This allows a truck fleet to travel in close proximity, which reduces congestion and fuel costs. Edge computing will make it possible to eliminate the need to have drivers for all trucks, except the front, as the trucks can communicate with one another with extremely low latency.
2. Remote monitoring of assets within the oil and natural gas industry
Explosive oil and gas disasters can be devastating. It is important to monitor their assets.
Oil and gas plants can be found in remote areas. Edge computing allows real-time analytics and processing closer to the asset. This means that there is less dependence on a centralized cloud.
3. Smart grid
Edge computing will become a core technology for smart grids adoption and can be used to help enterprises better manage their energy consumption.
In factories, offices and plants, sensors and IoT devices are connected to an edge platform to track energy consumption and analyze it in real-time. Energy companies and enterprises can make new deals with real-time visibility. For example, high-powered machinery can be run when there is no electricity demand. This can help increase the amount of green energy an enterprise uses (e.g. wind power).
4. Predictive maintenance
Manufacturers need to be able analyze and detect any changes in production lines before they fail.
Edge computing allows data to be stored and processed closer to the device. This allows IoT sensors with low latency to monitor machine health and perform real-time analytics.
5. In-hospital patient monitoring
Healthcare has many edge opportunities. Monitoring devices, such as glucose monitors, health tools and other sensors are not connected at the moment. Monitoring devices (e.g. glucose monitors, health instruments, and other sensors) are not currently connected. If they are connected, large amounts of unprocessed data would have to be stored on a third-party cloud. Healthcare providers face security risks from this.
A hospital edge could store data on-site to protect data privacy. Edge allows for immediate notifications to healthcare professionals of unusual patient behaviors or trends (through AI/analytics) and the creation of 360-degree views of patient dashboards for complete visibility.
6. Virtualized radio networks and 5G (vRAN).
Operators are looking to virtualize parts of their mobile networks (vRAN) more often. This offers flexibility and cost savings. Complex processing and low latency are the requirements of virtualized RAN hardware. Edge servers will be required by operators to support virtualizing their RAN near the cell tower.
7. Cloud gaming
Cloud gaming is a new type of gaming that streams a live stream of the game directly to your devices. (The game itself is processed in data centers and hosted on servers) Latency is a major issue.
Cloud gaming companies want to place servers as close as gamers to lower latency and deliver an immersive and responsive gaming experience.
8. Content delivery
Caching content, e.g. Improved content delivery can be made possible by caching content – e.g. music, video streams and web pages. It is possible to reduce latency significantly. Content providers want to distribute CDNs further to the edge. This will guarantee flexibility and customization of the network based on user traffic requirements.
9. Traffic management
Edge computing is a way to improve city traffic management. This can be done by optimizing bus frequencies in response to fluctuations in demand, opening and closing extra lanes and managing autonomous car flows in the future.
Edge computing eliminates the need to transport large amounts of traffic data to the centralized Cloud, thereby reducing latency and cost.
10. Smart homes
Smart homes are dependent on IoT device collecting data and processing it from all around the house. This data is often sent to a remote server where it is stored and processed. This architecture is not secure and has high backhaul costs.
Edge compute reduces roundtrip and backhaul time and allows sensitive information to be processed at the edge. For example, voice-based assistants such as Amazon’s Alexa would respond much quicker.