Edge Computing- The way forward for IoT
The amount of data gathered at the network edge has increased in tandem with the Internet of Things (IoT) expansion. In turn, the necessity to handle that data volume has sped up the development of edge computing use cases.
A distributed open IT architecture called edge computing allows devices to process data close to or at the information source rather than sending it to the Cloud. Real-time, latency-free data processing is made possible via edge computing.
Motivators of Growth
The following are the main elements promoting the expansion of edge computing infrastructure:
- Amount of data being generated
- Shorter response time
- Heightened security risks
- Convergence of IT/OT
To describe it succinctly, edge computing is the processing that happens as near as feasible to the activity or thing an IoT device is monitoring. Devices directly connected to sensors, routers, or gateways that transport data or tiny installations of servers placed on-site in a closet or enclosure can all be used for edge computing.
“Edge Computing are often pushing the boundaries of what is possible with technology today.”
Edge Computing Architecture
Edge computing dramatically closes the distance between computing, storage, and networking resources and the devices, users, and applications. As a result, it is regarded as one of the critical technologies in the future generation of networks that is connected to the Internet of Things and artificial intelligence.
It makes it possible for data to move incredibly quickly without transmitting it to a cloud or data center. Edge computing is a multilayered distributed architecture that distributes workload among the enterprise layer, edge layer, edge cloud, and edge network. Three nodes are present in edge computing: Device edge, Local edge, and Cloud.
Thanks to edge computing, processing, and storage capabilities are brought closer to the point of demand. Now let’s look at a few edge computing examples.
Smart Grid Edge Control
The foundation of how smart grids function today is the creation of two-way communication channels between the infrastructure for electricity distribution, the receiving customers (households, businesses, etc.), and the utility head-end. The tried and proven wide-area network (WAN) internet protocols are used for this.
The industrial internet of things (IIoT) has progressively absorbed the internet of things’ phenomenal development pace, bringing with it a wide range of technologies that can easily monitor, manage, and control the different operations inside the distribution infrastructure of the electric grid.
Remote asset monitoring for the oil and gas sector
Failures related to oil and gas can be pretty bad. Therefore, it is crucial to keep a close eye on their assets. Oil and gas facilities, however, are frequently found in outlying areas.
Edge computing makes real-time analytics possible, bringing processing considerably closer to the asset and reducing the need for reliable access to a centralized cloud.
Defense and Military
Military applications frequently employ robust industrial computers on land, at sea, and in the air. As a result, the military utilizes a broad range of industrial PCs, including wall-mount computers, embedded PCs, industrial touchscreen PCs, and rack-mount military computers.
Industrial computers that are constructed to withstand harsh conditions are known as “rugged” computers. The military uses industrial PCs for in-vehicle applications, data collecting, and video processing.
Visualization of IoT Data
Connecting cameras, temperature/humidity sensors, and other vast amounts of equipment without communication network access in warehouses and factories to edge terminals will enable visualization of the data stored there. These need to be connected to the cloud system and compatible with various wired and wireless interfaces, including edge gateways.
The cloud system’s visualization of previously inaccessible data, such as monitoring camera pictures and machine tool functioning status, enables in-the-moment situational awareness and analysis. Results can be applied to failure prediction and remote management tasks, among other client services.
An excellent example of edge computing data is from autonomous or self-driving automobiles. They require a large volume of processed data quickly.
For example, to choose which lane to go in, the autonomous car must immediately process the data at the cloud data center. For the sake of the passengers inside the car and those in the lane, these calls must be made immediately within seconds.
Due to its minimal latency, edge computing is intended to help with this real-time decision-making. Point-of-origin processing refers to the integrated computing used to process this real-time input to make a decision.
IoT sensors make it feasible to monitor machines. It keeps track of machine metrics. These IoT sensors use the stability of edge computing and perform local edge analysis on the acquired data. From the edge, it is transmitted to the Cloud.
Before any disruptions happen, edge computing conducts precise measurements and keeps the process running. In addition, a robust network for monitoring systems is attached to linked parking meters.
In the case of a malfunction, this makes it possible for the data to be transmitted to the hub. Since all smart equipment in the environment, including traffic lights, have IoT sensors built in, edge computing keeps an eye on this data locally. Only defect reports are communicated and transmitted centrally.
Edge computing technology can help security surveillance systems as it’s crucial to react to attacks quickly. As a result, security systems can spot possible dangers and immediately notify users of any unexpected activities.
Advertising for Retail Businesses
Targeted adverts and information for retail businesses are based on important criteria, such as demographic data, established on field devices. Edge computing can assist in preserving user privacy in this use scenario. Instead of transmitting unprotected data to the Cloud, it may encrypt and retain the source.
IoT gadgets are crucial in offering treatments for the patient’s health condition. It collects patient data in real-time from the Cloud and provides thorough analyses instantly. Therefore, it offers a better option when the patient is in a difficult situation.
Edge computing can process and analyze data, assisting doctors in making the proper diagnosis. In addition, with the help of edge computing, we can control connections and analyze data much closer to the location where it is collected.
An authenticated electronic health record (EHR) using proxy cards, for example, is where the patient’s data is uploaded after being acquired by IoT devices that monitor the patient. During diagnosis and therapy, medical devices may independently assemble and process information.
Virtual and Augmented reality (VR/AR)
Low latency and fast reactions are necessary for virtual and augmented reality work.AR projects visuals into the real world, whereas VR offers an immersive experience of digital graphics.
It takes a lot of rendering to synchronize a user’s movements and the actual environment with the virtual one.
Edge computing, in this situation, assists in enhancing on-device operations by dividing tasks between the AR/VR device and the edge cloud.
How can VTG Help You?
The examples of edge computing we’ve given illustrate the range of this developing trend and the various vertical industries it supports.
Organizations, industrial businesses, and smart cities benefit from improved performance and efficiency from their IoT installations thanks to edge computing solutions.
VTG is ready to help you with every step of your edge computing planning, from formulating a plan to developing edge intelligence in building your final product.
Get in touch with us to find out how VTG can help your edge computing application.