Edge computing technology is an extension of cloud computing. It can improve the analysis technology of cloud data. It is a concept of nearby computing. Its technology is gradually applied to various industries so that each industry can break through the existing restrictions and increase its productivity and efficiency.
What is edge computing?
Edge computing is a concept of nearby computing (decentralized computing architecture). Computing is performed in the local area network where the resources are located closer to the cloud. As far as possible, data is not returned to the cloud, reducing the cost of data traveling to and from the cloud. The difference from the cloud is that the network center node is moved to the edge node position on the network logic for processing.
As sensor prices and computing costs continue to fall, more "things" will be connected to the Internet. As more networked devices become available, edge computing will find more and more applications in all walks of life, especially in areas where cloud computing is inefficient. We have begun to see the impact of edge computing technology in many different industry sectors.
"When we sink the power of the cloud to the device (that is, the edge), we can bring the ability to respond, analyze and act in real-time, especially in areas with limited network conditions or lack of network ... it is still in the early development stage, But we are beginning to see that these new features can be applied to solve some of the major challenges worldwide. ”-Microsoft Chief Technology Officer
Which industries are edge computing used in?
From self-driving cars to agriculture, the following separately analyzes how several industries combine edge computing technology to achieve greater benefits.
One of the most obvious potential applications of edge computing technology is transportation, more specifically driverless cars. Self-driving cars are equipped with a variety of sensors, from cameras to radars to laser systems, to help vehicles run.
As mentioned earlier, these self-driving cars can use edge computing to process data closer to the vehicle through these sensors, thereby reducing the response time of the system during driving as much as possible. Although driverless cars are not the mainstream trend, companies are planning. Earlier this year, the Automotive Edge Computing Consortium (AECC) announced that it will start projects focused on connected car solutions. "Connected cars are rapidly expanding from luxury models and high-end brands to high-volume mid-range models. The automotive industry will soon reach a tipping point when the amount of data generated by cars will exceed existing cloud, computing, and communications infrastructure resources "-Chairman and President of AECC. Members of the alliance include DENSO Corporation, Toyota Motor Corporation, AT & T, Ericsson, Intel, and other companies.
However, it's not just self-driving cars that generate a lot of data and need to process it in real-time. The same is true of planes, trains, and other means of transportation-whether or not they are driven by humans. For example, the aircraft manufacturer Bombardier (Bombardier) C series aircraft is equipped with a large number of sensors to quickly detect engine performance problems. During the 12-hour flight, the aircraft generated up to 844 TB of data. Edge computing supports real-time processing of data, so the company can proactively deal with engine problems.
Today, people increasingly like to wear fitness tracking devices, blood glucose monitors, smartwatches, and other wearable devices that monitor their health. However, to truly benefit from the massive data collected, the real-time analysis may be essential-many wearable devices are directly connected to the cloud, but other devices support the offline operation.
Some wearable health monitors can analyze pulse data or sleep patterns locally without connecting to the cloud. The doctor can then assess the patient on the spot and provide immediate feedback on the patient's health. But in the healthcare field, the potential of edge computing is far from being limited to wearable devices. Think about how fast data processing can bring benefits to remote patient monitoring, inpatient care, and medical management in hospitals and clinics.
Doctors and clinicians will be able to provide patients with faster and better care, while the health data generated by patients also have an additional layer of security. Hospital beds have an average of more than 20 networked devices, which generates a lot of data. The processing of these data will happen directly closer to the edge, rather than sending confidential data to the cloud, thus avoiding the risk of improper access to the data. As mentioned earlier, the localized data processing means that large-scale cloud or network failures will not affect business operations. Even if the cloud operation is interrupted, the sensors of these hospitals can operate independently.
Smart manufacturing is expected to gain insights from the sensors deployed in modern factories.
Because it can reduce lag, edge computing may enable the manufacturing process to respond and change more quickly, and it can apply insights and actions in real-time from data analysis. This may include shutting down the machine before it overheats. A factory can use two robots to accomplish the same task. The two robots are equipped with sensors and connected to an edge device. Edge devices can predict whether one of the robots will fail by running a machine learning model.
If the edge device determines that the robot is likely to malfunction, it will trigger actions to prevent or slow down the robot's operation. This will enable the plant to assess potential failures in real-time. If robots can process the data themselves, they may also become more self-sufficient and responsive. Edge computing should support more insights from big data faster, and support the application of more machine learning technologies to business operations. The ultimate goal is to tap the huge value of the massive amounts of data generated in real-time, prevent potential safety hazards, and reduce machine outages on the factory floor.
Agriculture and smart farm industry
Edge computing is very suitable for agriculture, because farms are often in remote locations and harsh environments, and there may be problems with bandwidth and network connectivity.
Now, smart farms that want to improve network connectivity need to invest in expensive fiber optics, microwave connections, or have a satellite that operates 24/7; and edge computing is a suitable, cost-effective alternative. Smart farms can use edge computing to monitor temperature and equipment performance, and automatically slow down or shut down various equipment (such as overheated pumps).
Energy and grid control industry
Edge computing may be particularly effective in the entire energy industry, especially in the safety monitoring of oil and gas facilities.
For example, pressure and humidity sensors should be closely monitored and cannot make mistakes in connectivity, especially considering that most of these sensors are located in remote areas. If an abnormal situation occurs-such as an overheated tubing-but it is not noticed in time, then a catastrophic explosion may occur. Another benefit of edge computing is the ability to detect equipment failures in real-time. Through grid control, sensors can monitor the energy generated by everything from electric vehicles to wind power plants, helping to make corresponding decisions to reduce costs and improve energy production efficiency.
Application in other industries
Other industries that can take advantage of edge computing technologies include finance and retail. Both industries use large customer and back-end data sets to provide everything from stock-picking information to in-store clothing placement, which can benefit from reducing dependence on cloud computing.
Retail can use edge computing applications to enhance the customer experience. Today, many retailers are working to improve the in-store experience, and optimizing the way data is collected and analyzed is meaningful to them-especially considering that many retailers are already experimenting with connected smart displays.
Also, many people use point-of-sale data generated by in-store tablet computers, which will be transferred to the cloud or data center. With edge computing, data can be analyzed locally, reducing the risk of sensitive data leakage.
To conclude: From wearable devices to cars to robots, IoT devices are showing an increasingly strong development momentum.
As we move towards a more interconnected ecosystem, data generation will continue to increase rapidly, especially after 5G technology has taken off and further accelerated network connectivity. Although a central cloud or data center has traditionally been the first choice for data management, processing, and storage, these two solutions have limitations. Edge computing can serve as an alternative solution, but because the technology is still in its infancy, it is difficult to predict its future development.
Equipment capabilities challenges-including the ability to develop software and hardware that can handle off-load computing tasks in the cloud-may emerge. It is also a challenge to teach the machine to switch between computing tasks that can be performed at the edge and those that need to be performed in the cloud.
Even so, as edge computing becomes more adopted, companies will have more opportunities to test and deploy this technology in various areas. Some use cases may prove the value of edge computing better than others, but overall, the potential impact of edge computing technology on our entire interconnected ecosystem will be unpredictable potential, and we look forward to the future development of edge computing.