Edge computing is an efficient, secure, private, and cost-effective computing method. It brings computing closer to the edge of the network, improving IoT devices and web applications. There are many edge computing applications in the real world. Here are 12 real-life use cases for edge computing.
What are the Applications of Edge Computing in the Digital Era?
Edge computing is an extended form of traditional cloud computing technology. Currently, Edge Computing is the most appropriate architecture when it is essential to treat data locally or to preserve the available and accessible bandwidth for data that must be consolidated in a centralized and aggregated way with others from other sources.
One of the edge computing goals is to provide the benefits of cloud computing, artificial intelligence, and big data processing while reducing IT resource usage and latency.
Edge computing covers a wide range of technologies, for example, artificial intelligence, augmented reality, remote sensor systems, circulated information stockpiling, etc. This technology can be used to push applications, data, and services away from central hubs to the logical extremes of the network.
Broadly speaking, this type of computing makes it possible to optimize the amount of data sent to the cloud system and its analysis in real-time. Edge computing often relies on the Internet of Things (IoT), collecting data from sensors and, instead of sending it to the cloud, it leaves it and analyzes data at the edge.
There are many applications of edge computing in the digital era. In this article, we will discuss the top 12 edge computing use case examples.
12 Real-Life Edge Computing Use Case Examples
There are many real-world applications of edge computing. Here are the top 12 edge computing use cases examples:
1. Edge Computing in Healthcare Systems
The healthcare informatics industry is experiencing a great deal of data warehousing and network sclerosis. Edge computing is being used in the medical field to analyze patient data and give a faster response.
Edge computing and artificial intelligence promise to make healthcare delivery cheaper, easier, and better for everyone.
Deploying Edge Computing in healthcare enables organizations to create new revenue streams, improve operational efficiency, and most importantly, improve patient experience and staff safety. In addition, Edge Computing technology can be deployed and used in specialized research centers around the world to fight deadly diseases.
Several hospitals in London and some in the United States are using Edge computing technology, specifically for the field of radiology, allowing the analysis of many more millions of data from all these diagnostic tests.
This proposal also includes an AI platform that hospitals can customize and integrate their data into. Now, hospitals are able to reduce the time required to identify abnormalities found in diagnostic tests from hours to minutes.
2. Edge Computing in Industry 4.0
Industry 4.0 (the Fourth Industrial Revolution) is a major step in digital transformation that brings together the worlds of Information Technology (IT) and Operational Technology (OT). Factories are now using edge computing and IoT, especially related to the concept of Industry 4.0.
Edge computing has the potential to significantly reduce latency. Low latency is necessary for an interconnected system to function as it should. Smart manufacturing without low latency cannot experience the full benefits of the Internet of Things.
Without going any further, we can see in the case of Microsoft a good example of how technology companies want to respond to this need: from its alliance with ARM to improve data transfer from IoT to new cloud platforms for smart factories.
One of the successful examples of the use of edge computing in this field is in the Japanese manufacturer of electronic products, DAIHEN Corporation. Before it was decided to use this technology, its production process required more than 200 manual inspections to ensure that everything was working properly. This represented 30% of the total production time. The company decided to equip its factory with additional sensors and deploy a real-time analytics platform, powered by edge computing. By doing so, The company says, they saved more than 5,000 hours of manual data entry per year.
3. Edge Computing in Autonomous Vehicles
Future autonomous vehicles will generate vast amounts of data that must be collected and analyzed in near-real time so that automatic systems can govern driving with guaranteed precision and safety. According to some estimates, each vehicle could generate about 4 Terabytes per hour, which represents a huge amount of information, which until now was said to be transmitted to data centers for processing and to manage automated driving services. The amount of data generated will far exceed the capacity of our imagination. The transfer of data from the cars themselves to the data centers would saturate the capacity of the most advanced communications networks. Therefore, a great solution would be to have computing capacity in the vehicle itself, something that gives complete sense to the adoption of Edge Computing for autonomous cars.
Edge Computing provides a great advantage to the perimeter systems themselves, and that any local processing significantly reduces the time between data generation, analysis, and obtaining a response. Therefore, the latency will be significantly reduced.
4. Edge Computing in Cloud Gaming
Input latency and visual lag can have a significant negative impact on the player experience, especially for new cloud gaming platforms, if servers are located in central cloud data centers, hundreds of miles away from the end user.
While centralized cloud service providers require businesses to keep content in a single location, edge computing enables the distribution of application processes at the edge of the network and as close to the user as possible.
Edge computing enables new cloud gaming platforms to terminate the need for dedicated hardware such as a console or high-end personal computer while helping to solve latency issues in transferring data from the cloud to the user.
The combination of cloud and edge computing also creates a more flexible platform that gives game developers and publishers the ability to scale. At the same time, Edge computing allows gamers to move seamlessly between different locations and different devices.’
5. Edge Computing in Telecom Industry
Edge computing has become a major area of interest and investment in the telecommunications industry.
The creation of new networks based on the edge infrastructure is revolutionizing the sector and driving innovation, which has a lot to do with the imminent arrival of 5G and various data networks, which are building their progress in the cloud, and that experts are convinced it will bring important benefits.
Edge computing is at the base of new data network technologies, ensures decentralization, allowing communication and computing services to be carried where they are most needed.
The idea is that all the infrastructures, networks, and perimeter services work in harmony and together. The new IT communications and cloud architecture companies provide telephony services, operational technologies, and finally, IoT environments.
Experts believe that the progress and success of these technologies depend on the use of microdata center networks, high capacity, and low latency.
6. Edge computing in the IoT
The Internet of Things (IoT) and edge computing are among the hottest buzzwords today. Although the IoT and edge computing together do not necessarily go hand in hand, combining the Internet of Things with edge computing is a common strategy to get the most value from these two types of technologies.
Edge computing is essential for many IoT applications, it can reduce latency and reduce bandwidth usage. However, when it comes to the Internet of Things, most people ignore one of the most important advantages of edge computing.
You don’t need IoT devices to build edge computing architecture. You can host edge applications on any type of infrastructure, provided that the infrastructure is closer to the end-user than the central data center.
You also don’t need an edge architecture to use the Internet of Things. You can deploy IoT devices and manage them from a traditional data center located in the center of the network.
The Internet of Things infrastructure is a possible way to build an edge computing environment. An increasing industry consensus is positioning the Internet of Things as one of many use cases for edge computing.
7. Edge Computing in Predictive Maintenance
This is the most important use of edge computing for the industrial sector. Analysis algorithms for predictive maintenance and quality assurance are already integrated into edge computing technology. In continuous production processes, edge computing solutions can optimize processes and create a real-time prediction model that can be updated and refined due to new process requirements.
When manufacturers want to analyze and detect potential changes in their production lines before the failure occurs, edge computing helps make this possible by processing and storing data from the device. This allows IoT sensors to monitor machine status with low latency and perform real-time analytics.
Edge computing solutions recognize anomalies in the state of the machine in real-time through the use of artificial intelligence (AI). Detailed status information is fed back to the automation level so that machine operators can proactively carry out setup or maintenance measures at an early stage.
8. Edge Computing Security
Digital transformation and economic growth are two key issues, closely related to the future of connectivity. One of the most important challenges that organizations of all kinds must face in relation to the digital transformation and its evolution towards the cloud, is cybersecurity.
Edge Computing is an innovative technology for data storage and processing, which emerges as an alternative to face some of the limitations currently presented by some of the cloud computing services.
Edge computing is considered a secure computing paradigm. Effective cybersecurity practices are in place throughout the network.
Security solutions such as botnet managers, web application firewalls, API protection, DDoS mitigation, and rapid defense against threats embedded in the network and edge data centers provide continuous monitoring to mitigate security threats of organizations.
9. Edge Computing for Businesses
The growing demand for edge computing has led to solutions from many vendors. However, these solutions often focus on a single layer of the IT stack, rather than on the embedded technologies needed for edge computing. The true value of edge computing comes from data and technologies that adapt to ever-evolving needs.
Edge computing can reduce latency on a reliable network, bringing workloads and applications closer to digital interactions, which in turn can lead to improved experiences overall.
Edge services can connect to thousands of data centers and on-net locations to run distributed IT workloads near the edge of the network.
Content Delivery Networks (CDNs) enable businesses to create personalized web application experiences that are highly responsive and secure. Edge computing solutions, for example, provide developers with a flexible, open, open-module architecture to design, configure, and deploy custom workloads for high-end network applications.
Edge computing makes it possible to achieve certain operational goals. Specifically, you not only add value to your products and services by improving user experiences, but you also help increase application performance, control operating costs, and mitigate security risks. In short, edge computing adapts to your needs and enables organizations to stay connected with key stakeholders while taking full advantage of their data and applications.
10. Edge Computing for Smart Grid
A smart grid is an electricity grid (an interconnected electricity supply network) that uses data and analytics to improve the way decisions are made to deliver electricity. With the increasing number of households and energy usage, there is also a growing interest in monitoring data related to energy production, use, or storage to maximize the customer experience.
Edge computing is a key technology in the widespread adoption of smart grids and can help allow organizations to better manage their energy consumption.
The edge computing ability to process data locally can help choose which data needs to go through the cloud and which data can stay local. Thus, data risks are reduced.
Edge computing can ensure low latency, which helps to monitor the frequency of the grid in real-time and take any proactive decisions to mitigate power factor penalties.
Edge computing can enable accurate predictions and models, taking into account the weather, location, or roof angle when using solar panels.
11. Edge Computing in Agriculture
Edge computing is also used in much more traditional sectors like agriculture to collect the right data and use it to optimize planning.
One of the biggest advantages is the ability to remotely monitor different aspects of agricultural operations: from the soil, climate, and humidity, and temperature conditions, as well as acidity and pH levels.
Within agriculture, the recent emergence of smart agriculture frameworks has promoted the use of state-of-the-art computing systems and Internet of Things (IoT) devices and has laid the groundwork to enable the development of smart agriculture systems. These systems are capable of being programmed to perform automated tasks such as harvesting vegetables, as well as spraying plants and crops, and more applications are currently being developed.
In vertical farms, for example, humidity levels are controlled with a network of sensors that constantly monitor the haze surrounding the plant. By using edge computing, much of the data processing involved in these operations can be performed on the edge devices themselves, without the need to send them to the cloud, further increasing the benefits of these systems in agricultural environments.
In indoor vertical farming, sensors and cameras in the aeroponic growing system collect data on everything from nutrients and moisture to light and oxygen. Its sensors capture data about the growing and operating environment and send it to edge computing for analysis.
12. Edge Computing for Smart Cities
Edge computing plays an important role in the next stages of development in the smart city sector.
Most cities have surveillance cameras installed on public roads. These facilities generate huge amounts of data and their real-time analysis requires significant computing power.
Edge computing can bring the processing and storage closer to the smart home, and reduce backhaul and roundtrip time and sensitive information can be processed at the edge.
Edge computing technology can enable a more effective city traffic management system. With edge computing, there is no need to transfer large amounts of traffic data to the central cloud. This reduces the cost of bandwidth and latency. Edge computing solutions can optimize bus frequencies under uncertain demand, manage the opening and closing of extra lanes, and, in the future, can manage autonomous car flows.
Some cities such as Atlanta or Zurich are betting on using IP cameras and combining this information with that collected by other sensors. All this data is analyzed with edge computing to allow law enforcement to identify possible suspects.
In the case of the American city, edge computing is analyzing large data sets related to traffic, pedestrians, bicycles, parking, etc. With all this information, the city also tries to improve mobility in addition to reducing crime.