Introduction

With the latest advancements in AI technologies, we have noticed a significant increase in the deployment of AI-based applications and services in recent years. More recently, with the booming IoT industry in particular, billions of mobiles and IoT devices are connected to the internet, generating tons of data. This arises the need to integrate AI into the network edge so as to fully capitalize on the potential of edge big data. To accomplish this task, Edge computing has been seen as a proper solution to which AI can be integrated. This led to the birth of a new technology – Edge Intelligence (EI).

Before getting started with EI, let us look at some of the existing technologies for a better understanding.

Existing Technologies

Edge Computing

Edge Computing is an existing technology that brings data processing closer to the site of data collection. The traditional approach of collecting data, sending it to the central data center for processing via the Internet, and sending it back to the source is a proven approach. However, with the increase in connected devices and the volume of data that needs processing, this approach is no longer capable of managing it efficiently. This is where Edge Computing comes into play.

The architecture of Edge Computing is set up close to the originating source. This is to process the client data at the periphery of the network. Only the result of this computing process at the edge, such as the insights obtained, equipment maintenance predictions, etc is sent to the data center for review. By doing so, we are able to remove the need for long-distance communication between the client and the server. This helps in reducing latency and bandwidth, making the process more efficient.

Cloud Computing

Cloud Computing, in simple words, is the delivery of computing services over the internet. These computing services include servers, storage, databases, networking, software, analytics, etc. It eliminates the expense of buying hardware and software for setting up data centers. It is cost-efficient as we won’t need to manage our own data centers. Another important feature is the speed of Cloud computing. It provides us with powerful computing resources with just a few clicks. This provides a lot of flexibility and ease for businesses.

The use of cloud computing makes the process more productive. This reduces the need to spend time on activities such as hardware setup, software patching, etc. So we can use this time for achieving better goals. Also, another notable feature of using the cloud is its security. It offers the latest protocols that secure our data against a variety of threats. This also makes it very reliable to its users.

How is Edge Intelligence different?

Edge Intelligence is a technology that is created by incorporating AI functionalities into Edge Computing. Even though Edge Computing has made data processing much more efficient, in order to keep up with the demand, there is a need to improve it. For this purpose, AI is essential due to its ability to quickly analyze huge volumes of data and provide valuable insights. This helps to improve the quality of the decision-making process.

There are 4 fundamental components for edge intelligence. They are edge caching, edge training, edge inference, and edge offloading.

Edge Caching: It basically refers to a distributed data system proximity to end users, which collects and stores data generated from edge devices as well as data received from the internet to support intelligent tasks for users at the edge. Data is distributed at the edge itself. In edge caching, this collected data is used as input for intelligent applications, and results are sent back to where data is cached.

Edge Training: It refers to a distributed learning procedure. This model is trained to learn the optimal values for all the weights and bias, or the hidden patterns based on the training set cached at the edge.

Edge Inference: This is the stage where a trained model is used to infer the testing instance on edge devices. So, this allows the edge device to provide actionable intelligence using AI technologies

Edge Offloading: It is a distributed computing paradigm that provides a computing interface for edge caching, edge training, and edge inference. Edge devices that do not have enough resources for specific applications can offload some tasks to edge servers or other edge devices.

Now, let us take a look at the architecture of EI

Edge Intelligence – Architecture

Here, we see a comparison of the traditional model vs edge intelligence. In the traditional model, all the edge devices first upload the data to a central server for performing intelligent tasks such as model training, inference, etc. The central server is usually located in the remote cloud. The results after processing are then sent back to the edge devices

In the EI model, the intelligent tasks are done by the edge servers/devices themselves. Only a very small/negligible amount of data needs to be uploaded to the cloud compared to the traditional models. The different functions are distributed among the different edge devices, which work together to complete the task.

Use cases of Edge Intelligence

1. Transportation and Logistics

EI is used for optimizing the costs associated with fleet management. It is used in asset tracking, finding efficient routes, monitoring temperature, predicting condition and maintenance needs, predicting delivery times, and improving safety. It optimizes the overall supply by connecting and sharing data with the various warehouses and also enables to identify inventory theft and other errors in transportation.

2. Industrial Manufacturing

Industrial manufacturers use EI to improve real-time data processing. They use automated real-time monitoring for their machines to gain insights about maintenance and improve manufacturing uptime. This results in improved operational efficiency and profits. It also improves the cost structure of operations and allows them to better understand the data collected.

3. Technology

Software companies use EI to develop personalized data-driven experiences to attract their customers. It enables them to meet the customer demands for instant access to data and tackle many issues related to performance and data-localization requirements associated with cloud-native, distributed application design. Also, EI highly increases the scalability of technology and assures the highest level of customer satisfaction.

4. Telecommunication

Multiple System Operators (MSOs) use EI to maximize the lifetime value of their subscribers. This enables them to make the most out of the existing network infrastructure and invest capital appropriately. So, this allows them to scale their architecture to millions of subscribers and get analytics per subscriber to provide personalized recommendations and experience. This also allows them to invest in other innovative services and thereby leverage the existing network.

Conclusion

So, in this article, we have discussed in detail about Edge Intelligence and its use cases, and how it differs from other existing technologies. Edge Intelligence is a technology that is still in its infancy. Based on our observations, we expect it to dominate in the coming years due to its huge potential.