After more than ten years of development, the traditional large-scale Internet of Things IoT architecture has formed a formula of "setting first, then calculating":
Let's look at the infrastructure of today's computing architecture: cloud computing has brought higher computing intensity, 5G and all-optical networks have introduced deterministic latency and ultra-large bandwidth, and CDN and cloud native have already supported the distribution of computing power to the town level. Now, new technologies such as autonomous driving, digital twins, and Immersive Technology have entered a period of rapid development. For these "new data generated outside the data center", new business forms have taken shape, and new computing architectures have entered a period of division— - Distinguish online real-time data computing and offline data processing, and focus on the continuous iteration capability of business systems:
This paper takes the practical application of "noise sensor" in the industrial Internet as an example, and introduces how to quickly realize real-time early warning monitoring of noise at low cost.
"Left-hand product, right-hand cost"
In the process of enterprise digital transformation, customers buy products, but in cost accounting, they focus on TCO (Total Cost of Ownership), which is defined on Wikipedia as: “Total cost of ownership (TCO) is a financial estimate intended to help buyers and owners determine the direct and indirect costs of a product or service. It is a management accounting concept that can be used in full cost accounting or even ecological economics where it includes social costs.”, simply come Say: TCO can be described as the sum of the cost of acquiring an asset and the costs incurred throughout its life service cycle. TCO is by no means equal to the purchase cost of the asset, it also includes the cost of operating and maintaining the asset after purchase.
The maturity of cloud computing has enabled the Internet of Things to "bounce" local computing to the cloud. Like the success of SaaS, this has reduced equipment acquisition costs and depreciation and amortization for enterprises. The industry is beginning to focus on three issues:
(1) Some businesses with sensitive data security still require some local computing (In-house)
(2) How can thousands of IoT devices ensure stable cloud latency?
(3) The complexity and scalability of the overall architecture determine the cost calculation for the entire life cycle
iKuai+YoMo , one hard and one soft, stable and safe
Aikuai router supports Docker container technology. When all IoT devices have completed network access through Aikuai, by deploying services developed based on YoMo on the router, low latency, high reliability and high degree of data transmission over long distances can be achieved. security protection.
Simple Tutorial Steps
Install the Docker plugin
Install the Docker plug-in directly in the plug-in market. For the specific tutorial on installing the plug-in, please click → "Plugin Management"
add mirror
The Docker plug-in supports local upload and mirror library download. Let's talk about how to add mirrors to the router through these two methods.
Local Upload
This upload method is the fastest. First, build and package the image file, and then upload it to the router's file system.
We can upload our image file by following these steps:
"Disk Management" --> "File Management" --> "Select Save Directory" --> "Click to Upload" --> "Select Local File" --> "Successful Upload" --> "View Path"- -> "Copy"
This will copy the path in the router after we uploaded the image file, such as: /cc/images/noise-source.tar
add mirror
"Plugin Management" --> "docker" --> "Image Management" --> "Add" --> "Select Reference Path" --> "Fill in the file path: /cc/images/noise-source.tar" --> "Confirm"
After waiting for a while, it will prompt that the upload is successful, and the following successful image will be displayed:
This method is the fastest way to upload, but for real deployment, it is more practical to use the mirror library.
Mirror library download
With the yomorun/noise-source:latest image built, the next steps are simpler, mainly uploading the image to hub.docker.com , and then downloading it in the router.
Back to the router, we can download the mirror library through the following steps:
"Plugin management" --> "docker" --> "Image management" --> "Add" --> "Select image library to download" --> "Search for yomorun to list the relevant container list" -- > "Select yomorun/noise-source" --> "Download"
After all is complete, you can view the downloaded image.
Create a container
Proceed as follows:
"Plugin Management" --> "docker" --> "Container List" --> "Add"
Fill in the following information
In the advanced settings, configure business-related environment variables:
Go back to the container list interface, you can see the running container
Effect video
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Based on the general trend of industrial Internet development, Aikuai will gather strength and forge ahead, adhere to the strategic layout of the three major centers of basic network center, edge computing and storage center, and IoT management center, and actively develop business needs for industrial customization. Optimize and improve, and accelerate the in-depth integration of informatization and industrialization.
YoMo
YoMo is an open source programming framework, built for low-latency streaming data processing in the field of edge computing. Its bottom layer is based on the IETF QUIC protocol communication, the core communication layer of HTTP 3.0, which is about to become the world standard. It uses Functional Reactive Programming as the programming paradigm to facilitate development. Developers build reliable and secure real-time computing applications for time series data, and optimize them for 5G and WiFi-6 scenarios to release real-time computing value.
Noise sensor acquisition monitoring system
Curious about the YoMo app deployed above and want to take a closer look at this full example? Friends, please click the link below to research, and your feedback is welcome!
Using YoMo to develop a noise sensor acquisition monitoring system
