Edge computing has become a viable option for businesses with sizable IoT deployments to handle sensor data near its source. Edge computing minimizes WAN bandwidth costs and tackles security, data privacy, and data autonomy issues, as well as the latency associated with transporting data from a remote place to a centralized data center or the cloud for analysis.
Edge computing is integrated into a multi-cloud architecture that enables new digital business opportunities on a more strategic level.
What is edge computing?
Edge computing is a concept that aims at providing computing services closer to various IoT devices in the data source end. This proximity to data at its source can deliver substantial business benefits, including faster insights, improved response times, and better bandwidth availability.
The concept of edge computing dates back to the 1990s. Even back then, there were plans to introduce nodes closer to the user to deliver cached content like images or videos. Edge computing is closely related to the Internet of Things and is based on the concept of data processing by end devices or micro-data centers located nearby.
There are numerous definitions of edge computing on the Internet. The simplest way to describe this technology is as a network of micro-data centers that process data locally before sending it to a central database operating in the cloud or a company’s data center.
What are the advantages of edge computing?
Low power consumption
Compared with cloud servers that aggregate massive data for centralized computing and analysis, edge computing gateways process data in real time at the site closest to the device layer. It can effectively reduce IoT applications by reducing the amount of data transmitted remotely and using energy-efficient computing chips. The overall energy consumption of the system.
The DUSUN edge computing gateway integrates multiple functions such as gateway, routing, switch, and device control. At the same time, the built-in complete industry protocol library facilitates device connection, supports the development of device collaborative application functions, and dramatically simplifies the IoT communication layer architecture.
The edge computing gateway adopts a highly integrated design, making the size smaller and more compact, and easier to install and deploy. And the application scenarios of the gateway terminal are more diverse, such as petrochemical, energy and power, rail transit, intelligent manufacturing, environmental protection industries, etc.
The intelligent gateway equipped with an edge computing function also has a powerful device linkage control capability, which supports the independent implementation of device management strategies, multi-device linkage strategies, event analysis strategies, and exception handling strategies. The gateway supports secondary development, which can further develop machine learning and computing optimization capabilities.
The gateway can also support networking methods such as Ethernet and 4G/5G/WIFI, which can be selected as needed. The installation, deployment, and use are more personalized and flexible. The edge computing gateway also has strong cloud software center support, which can install corresponding firmware and applications according to actual application scenarios.
What are the application scenarios of edge computing?
According to STL Partners’ research, edge computing can be used in various scenarios. Here are 9 critical application scenarios:
One of the first use cases for autonomous vehicles may be the automated platooning of truck fleets. Trucks can follow each other in convoy using edge computing technology. All but the lead truck will no longer require drivers, as trucks can communicate with each other with ultra-low latency. This saves fuel and reduces congestion.
Remote asset monitoring
For example, failure in the oil and gas industry can be disastrous. As a result, asset tracking is critical. However, oil and gas plants are frequently located in remote areas. Edge computing brings real-time analysis and processing closer to the asset, reducing reliance on high-quality connections to centralized clouds.
Edge computing will be a crucial technology for the widespread adoption of smart grids, assisting businesses in better managing their energy consumption. Sensors and IoT devices connected to edge platforms in factories, factories, and offices are used to monitor and analyze energy consumption in real-time.
Businesses and energy companies can make new deals with real-time visibility. For example, to run high-power machinery during off-peak hours of electricity demand. This may increase the company’s use of green energy sources such as wind.
Manufacturers want to be able to analyze and detect changes in production lines before they fail. Edge computing assists in bringing data processing and storage closer to the device. This allows IoT sensors to monitor machine health with low latency and perform real-time analytics.
Currently, monitoring devices such as blood glucose monitors, health tools, and other sensors are either unconnected or require large amounts of unprocessed data from the devices to be stored on a third-party cloud. This raises safety concerns for healthcare providers.
To ensure data privacy, the edge on a hospital website can process data locally. Edge computing can also alert practitioners to abnormal trends or behaviors in patients on time.
Cloud gaming is a new type of game that transmits the game’s real-time content directly to the device, which is highly dependent on latency.
Cloud gaming companies are looking for edge servers that are as close to players as possible to reduce latency and provide a fully responsive and immersive gaming experience.
Content delivery can be greatly improved by caching content such as music, video streaming, web pages, and so on at the edge. Latency can be significantly reduced. Content providers are looking to distribute CDNs more broadly, allowing network flexibility and customization based on user traffic needs.
Edge computing has the potential to improve the efficiency of urban traffic management. Examples include optimizing bus frequency in the face of fluctuating demand, opening and closing additional lanes, and managing autonomous vehicle traffic in the future.
Edge computing brings processing and storage closer to the smart home, reducing backhaul and round-trip times and processing sensitive information at the edge. For example, voice assistant devices such as Amazon’s Alexa will have much faster response times. Edge computing eliminates the need to transmit large amounts of data to a centralized cloud, lowering the cost of bandwidth and latency.
IoT devices are used in smart homes to collect and process data from all over the house. Typically, this data is sent to a central remote server where it is processed and stored. However, this existing architecture has backhaul costs, latency, and security issues.
Edge computing brings processing and storage closer to the smart home, reducing round-trip times and processing sensitive data at the edge.
The examples above are just a few of the numerous use cases supported by edge computing across multiple industries. More scenarios will emerge in the future as edge computing technology matures.