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Recently, Jiangxing Intelligent announced the completion of 30 million A round financing, led by Songhe Capital, followed by Sequoia Capital, BV Baidu Venture Capital, and Qingtong Capital as financial advisor.Jiangxing Intelligent previously received tens of millions in angel round financing from Sequoia Capital inJuly 2018.
This is a provider of edge computing technology and services, which helps enterprises improve efficiency and reduce risks by managing and analyzing vast amounts of IoT devices and data. Currently, its core application scenarios are mainly in the fields of electricity, new energy, industrial assembly line monitoring, and telecommunications.
“Cloud + Edge Computing + End” Architecture
So what exactly is edge computing, and how does it differ from and relate to cloud computing? The terms Industrial 4.0, Industrial Internet, and Industrial IoT (Internet of Things) have completely transformed the manufacturing industry in less than ten years, propelling us towards intelligent manufacturing. Industrial 4.0 and the Industrial Internet are already well-known, while cloud computing and edge computing are shaping the future of IoT. This combination brings stability to connected devices in the IoT network and solves latency issues by processing data closer to the source.
By 2020, the number of global IoT devices is expected to reach 20-50 billion, with data volume growing rapidly; simultaneously, the demand for computing resources has also increased significantly due to the implementation of intelligent applications like AI.
The surge in data volume and computing power presents challenges: significant pressure on transmission bandwidth, cloud computing and storage, and low real-time performance. This makes the existing “cloud + end” model increasingly difficult to support numerous application scenarios.
The emerging computing model of “edge computing” has arisen in this context, equivalent to adding a data processing “intermediate station” on the side closer to the “device terminal,” integrating the core capabilities of “network, computing, storage, and application” to perform initial data processing, after which only a small amount of processed result data needs to be transmitted to the cloud.
Edge computing brings several benefits by performing intelligent processing at the network edge: it reduces pressure on the cloud’s core network nodes; local data processing greatly improves real-time performance; reduced data transmission lowers bandwidth costs; and local data storage can meet security and privacy requirements.
For example, in the AGV field, due to the real-time processing of maps and obstacle avoidance by the robot body, there may be slight delays in response speed. In such cases, edge computing at the proximal side may be insufficient, and some companies choose to upload data to the cloud for computation. The same applies to machine vision.
Intelligent devices installed and connected in an edge computing environment can process critical task data and respond in real-time, rather than sending all data over the network to the cloud and waiting for a response. The devices themselves act like mini data centers, with basic analysis occurring on the device, resulting in almost zero latency. With this new functionality, data processing becomes decentralized, significantly reducing network traffic. The cloud can later collect this data for secondary evaluation, processing, and in-depth analysis.
The Benefits of Edge Computing for IoT
Using edge computing brings many benefits to IoT devices, such as near-zero latency, reduced network load, increased resilience, reduced data exposure, and lower data management costs. Let’s look at these one by one:
Near-Zero Latency:
Near-zero latency is the greatest advantage of edge computing. The time interval between data collection, processing, and action is almost real-time. This is a crucial requirement for IoT devices in mission-critical situations. The best example is autonomous driving, and AGV is a simplified indoor version of autonomous driving.
Reduced Network Load:
A large amount of data on the Internet highway can lead to increased network congestion, especially in areas with weak connections. By using edge computing, most of the traffic load will be processed at the source rather than sending all data over the network, significantly improving network congestion.
Enhanced Resilience:
With the decentralized architecture provided by edge computing, other connected devices in the network become more resilient. Compare this with a single virtual machine failure in the cloud, which can affect thousands or even millions of connected IoT devices. Even if one device fails, it does not affect others, and they remain active and operational.
Reduced Data Exposure:
Edge computing reduces the amount of data transmitted over the network. This also helps to lessen data leakage during transmission. In some cases, sensitive and critical data collected by smart devices may not need to be transmitted at all. For instance, many large e-commerce and logistics companies prefer not to upload their data to public clouds, especially some large German and Japanese enterprises, which are very cautious about every step of their intelligence implementation.
Lower Data Management Costs:
Using edge computing can significantly reduce storage costs in the cloud since we do not store everything in the cloud. Due to the relatively small quantity, it also helps to manage data effectively. Only summarized data that requires deeper analysis will be sent to the cloud for subsequent analysis and inference.
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