In the 5G era, don't you know about edge computing gateways? To know what an edge computing gateway is, you must first understand what edge computing is. After understanding the specific application fields of edge computing, you will understand the relevant use cases of edge computing gateways. Let's understand what is edge computing?
What is edge computing?
In simple terms, edge computing refers to processing information at the edge. What edge computing does is reduce the distance between devices and operations by decentralizing processes. Data is stored and computed closer to the devices that create it, rather than being transmitted to distant central locations. Storing and computing data on the device where it is collected is intended to address latency issues that can negatively impact application performance, especially in real-time data processing applications. By performing these processes locally, money can be saved because the amount of data that needs to be processed at the cloud-based location is greatly reduced.
IoT devices are growing so rapidly that they need to be connected to the internet in order to transmit data to and receive information from the cloud. A large number of IoT devices means generating a large amount of data, hence the need for edge computing.
In January 2020, Apple acquired Xnor.AI, an edge-focused artificial intelligence startup. Apple plans to run deep learning computing models on edge devices such as mobile phones, Internet of Things devices, cameras, drones and embedded CPUs.
The routers in the home are all gateway devices.
How does the edge computing gateway work?
The workflow of edge computing tools generally has the following modes:
Edge sensor devices collect data
In-device computing functions perform data processing at the edge
If the device needs to take action, it will take action based on the calculated result
Devices pre-stream filtered data from the edge to the cloud, so businesses can see the big picture by aggregating aggregated data from thousands of devices (within bandwidth constraints).
How is edge computing different from conventional computing?
Edge computing has similar capabilities to regular computing applications, except where the computation is performed. A key difference is that edge computing applications need to work on edge devices that are limited in memory, processing power, or communication. These applications are optimized to work within these constraints. Edge computing processes computing data at the edge, thus reducing the bandwidth cost of data transmission.
What are the advantages of an edge computing gateway?
Advantages of edge computing include:
Faster, autonomous decision-making because data processing is at the edge, reducing latency
Reduced storage and management costs for centralized data as less data is stored centrally
Since edge computing will do data filtering, the cost of data transmission is lower
Better security/privacy as granular data (e.g. video clips) are not stored or transmitted
Edge Computing Gateway Use Cases
Edge computing gateway for intelligent monitoring: Intelligent monitoring based on edge computing can improve security through behavior recognition and early warning. By using raw images from security cameras, edge computing can detect and track any suspicious activity.
Edge computing gateways are used for industrial equipment monitoring and maintenance: in industries such as energy and manufacturing, when any machine fails or requires maintenance, immediate response may be required. Data sifting through edge computing allows organizations to more quickly identify signs of failure and take action before any bottlenecks develop within the system.
Why is edge computing important?
One of the biggest advantages of edge computing is the faster speed at which data can be processed and stored. This is especially important for applications that require real-time processing. Significance can also be seen in the time (less than 400 milliseconds) it takes for an edge AI algorithm to generate a response to a query. This time compares to the latency you get with cloud architectures measured in seconds, which is much better.
Compared with cloud computing, bandwidth transmission costs are much less. Many businesses find that cost savings in this area is a good reason to take advantage of edge computing architectures.
With the deployment of edge computing, facial recognition algorithms will run on smartphones or edge networks. Some applications that require fast turnaround times include autonomous vehicles (self-driving cars), virtual reality, smart buildings, augmented reality, smart roads, and smart cities. There is no doubt that with the rapid development of technology, edge computing will be at the forefront of technological advancement in the coming decades.
In the field of intelligence such as drones and smart robots, most of the AI algorithms are run through cloud services because they require a lot of processing power. AI chipsets that allow data to be processed at the edge will also enable faster real-time responses, especially for applications that require near-instantaneous responses.
The edge computing gateway has always been our main product. The T600 series 5G edge computing smart gateway can be applied to autonomous machines such as robots, unmanned delivery vehicles, low-altitude defense, intelligent inspection, and smart buildings. Ideal carrier for deep learning.