Edge Intelligence: Definition, Applications, Components and Benefits

Edge intelligence is a further development of edge computing. It allows smart sensor nodes to make decisions locally, with the option to send data to a gateway for further filtering and finally to the cloud or other storage system. Edge intelligence combines artificial intelligence and edge computing so that it no longer relies on the cloud, but directly senses information locally and responds quickly to changes in the environment through real-time decision-making applications. The emergence of this technology will bring more efficient data processing and faster decision-making capabilities to various industries.

Applications based on edge intelligence

Machine learning aspect

Machine learning (ML) is The core of edge intelligence, it detects patterns and anomalies in data streams to trigger appropriate responses. ML has a wide range of applications, including factories, smart cities, smart grids, augmented and virtual reality, connected vehicles, and healthcare systems. ML models are trained in the cloud and then used to empower edge devices with intelligence.

Machine learning is an effective way to create functional AI. ML techniques such as decision trees, Bayesian networks, and K-means clustering are used for classification and prediction of AI entities. Deep learning uses artificial neural networks to perform tasks such as image classification and face recognition.

Artificial intelligence aspect

Machine learning has become very popular in manufacturing, while artificial intelligence is more used to extract content from social media , business informatics, and big data collected in things like online shopping records. Transmitting large amounts of data from IoT devices to the cloud for analysis is expensive and inefficient. Edge intelligence pushes cloud services from the core of the network to the edge of the network for processing. The edge node sensor can be a smartphone or other mobile device.

Among them, real-time video analysis is expected to become a very popular application for edge computing. As an application built on computer vision, real-time video analytics will continuously collect high-definition video captured from surveillance cameras. These applications require high computation, high bandwidth, and low latency to analyze video. This can be achieved by extending the cloud’s AI to gateways covering the edge.

Benefits of edge intelligence

1. Low latency intelligence

Cloud computing or centralized The system has been having latency issues. Capturing data and sending it to a central location, processing and responding takes time and does not facilitate real-time decision making.

The core benefit of edge intelligence is to reduce latency, thereby executing actionable events in near real-time, thereby improving overall system performance. This also frees cloud computing and centralized systems from having to deal with raw and irrelevant data; they can instead process highly structured, context-rich, actionable data. In this way, latency is improved not only at the edge, but also across the entire system.

2. Low-bandwidth data storage

In any IoT model, transmitting all the data collected by thousands of edge devices has a significant impact on bandwidth. The requirements are very high. As the number of these devices increases, it will grow exponentially. The remote site location may not even have the bandwidth to transfer data and analytics back and forth from the cloud server. Edge intelligence helps perform analysis and take required actions. It can store data, metadata, and operational reports that can be collected later.

3. Linear Scalability

Edge intelligence architecture can scale linearly as IoT deployments grow. Edge intelligence architecture leverages the computing power of deployed devices. It can take on the heavy lifting of executing deep learning and machine learning models. This relieves the pressure on centralized cloud systems as edge devices share the main burden of performing intelligent functions.

4. Reduce operating costs

#Since edge intelligence operates on time-sensitive data locally, it can save a lot of cloud space, Because they provide rich data to central systems. This also reduces operating costs. Edge intelligence drives operations in real time for all connected IoT devices, which enables professionals to deploy and maintain devices more efficiently.

Edge intelligence architecture components

The 4 main components in the edge intelligence architecture: edge caching, edge training, edge inference and edge offloading.

1. Edge caching

Under edge intelligence, edge caching mainly handles the traffic from end users and their surrounding environment to edge devices. Enter distributed data. In addition to this data, data generated by the edge device itself also belongs to the edge cache. In addition, mobile sensors collect environmental data, process and store it in reasonable places for use by artificial intelligence algorithms to provide services to users.

This module is responsible for complete edge data storage management.

2. Edge training

To date, the training of AI models deployed on the intelligent edge has mostly been centralized. We train deep learning models on powerful central servers equipped with powerful GPUs and port them to edge devices using their compatible edge SDKs and runtime environments. This remains the best way to control the retraining and deployment of models to edge devices using cloud connectivity or other IO interfaces. But for a true edge intelligence architecture, we need to implement edge training.

Edge training refers to the process of learning optimal values ​​of weights and biases for a model deployed on data or identifying hidden patterns on training data captured on the edge.

3. Edge reasoning

Edge reasoning is to evaluate the training model or algorithm on the test data set by calculating the output on the edge device. performance process.

4. Edge offloading

Edge offloading is another important feature of edge intelligence, where edge devices can offload certain tasks, such as Offload training, edge caching, or edge inference to other edge devices in the network. It is similar to the distributed computing paradigm where edge devices create intelligent ecosystems. Edge offload is an abstract service layer on top of the other three components, so it is very important that it also provides a fail-safe strategy to overcome the shortcomings of standalone edge devices. Structured and effective implementation will enable edge intelligence to make the most of available resources in the edge environment.

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