Industrial Applications of Edge AI

Introduction to Edge AI

Edge AI is a combination of artificial intelligence and edge computing. Locally, algorithms are processed on the machines on the manufacturing line or a nearby server. The algorithms use data collected by various sensors and created by the machines themselves. Machines can make autonomous decisions in milliseconds without connecting to the Internet or the cloud in this way.

With developing technologies such as deep learning and neural networks, which have revolutionary promise but are dependent on cloud computing, their runtimes are hampered, and large power requirements are incurred.

"Latency is becoming a concern as organizations strive to push more data to software that operates in the cloud or in the data center," according to Forrester Research, an international information technology consultancy. As the volume of data grows, it becomes less cost-effective to handle everything centrally.

Edge AI have opened up opportunities to take a fresh and practical approach to data processing and fuel a range of technology-driven solutions. Click to explore our, Edge AI Architecture and its Applications

The pandemic has increased the use of edge computing, which refers to computation and data storage close to the point of use.

Edge Computing Expansion

By 2028, sectors such as digital health care, manufacturing, and retail will increasingly adopt edge computing to reduce latency, optimize bandwidth, and improve data analysis flexibility.

Surge in Data from IoT Devices

IoT devices are anticipated to produce more than 175 zettabytes of data by 2025, underscoring the rapid growth in data creation driven by interconnected technologies.

How Edge AI Works?

Edge AI involves deploying artificial intelligence (AI) algorithms and models on edge devices, such as smartphones, IoT devices, and other embedded systems, to enable faster and more efficient data processing. Here are the general steps of how Edge AI works:

• Data Acquisition: Edge devices capture data from various sensors or sources, such as cameras, microphones, and environmental sensors.

• Data Pre-processing: The acquired data is pre-processed to clean, filter, and format it to be used by AI algorithms and models.

• Local Inference: AI algorithms and models are deployed on the edge device, enabling real-time data processing and decision-making. The processed data can trigger actions, make decisions, or provide insights.

• Data Transmission: The processed data is sometimes transmitted to remote servers or the cloud for further analysis or storage.

• Model Updates: AI models deployed on edge devices may need to be updated regularly to improve accuracy or to adapt to changing conditions. These updates can be done remotely or through manual intervention.

Overall, Edge AI enables data to be processed and analyzed in real-time on the edge device, reducing the need for data transmission to remote servers or the cloud. This approach enables faster response times, improved data security and privacy, and more efficient use of network resources.

It is necessary to implement data and AI Ethics. AI must be developed and deployed ethically. Taken From Article, Ethical AI Challenges and it's Solutions

Edge AI vs. Cloud AI

When we talk about Edge AI, it is somewhat different from AI. Edge AI is a subset of AI. Apart from Edge AI, AI does have other subsets, too. There is also Cloud AI, which involves a centralized server for data processing, and the ML algorithms are applied there. Cloud AI and Edge AI have advantages and disadvantages, which are applicable based on the requirements and needs.

Edge AI emerges as a better alternative whenever there is a need for real-time prediction and data processing. Since there is latency in the case of Cloud AI, it is not a better model for that. Edge AI and Cloud AI are two approaches to deploying artificial intelligence (AI) algorithms and models, with advantages and disadvantages. Edge AI refers to deploying AI algorithms and models on edge devices, such as smartphones, IoT devices, and other embedded systems. This approach enables faster response times and more efficient use of network resources, as data is processed locally on the device.

Cloud AI, on the other hand, refers to deploying AI algorithms and models on cloud servers. This approach provides more processing power and storage capacity, enabling more complex and sophisticated AI models to be trained and deployed.

Here are some critical differences between Edge AI and Cloud AI:

• Latency: Edge AI offers lower latency as data is processed locally on the device, while Cloud AI sends data to remote servers, resulting in higher latency.

• Data Privacy: Edge AI offers better privacy as data is processed locally on the device. At the same time, Cloud AI involves sending data to remote servers, potentially exposing sensitive data to third-party servers.

• Processing Power: Cloud AI offers more processing power and storage capacity than Edge AI, enabling more complex and sophisticated AI models to be trained and deployed.

• Network Bandwidth: Edge AI requires less bandwidth as data is processed locally on the device, while Cloud AI involves sending data to remote servers, requiring more network bandwidth.

Edge AI in Manufacturing

Manufacturers worldwide have begun to alter their manufacturing processes with AI at the edge. The following use cases of edge computing are helping manufacturers improve their efficiency and productivity.

Predictive Maintenance

Sensor data may be utilized to discover anomalies early and anticipate when a machine will break, referred to as predictive maintenance. Sensors on equipment scan for defects and notify management if a machine needs to be repaired, allowing the problem to be treated quickly and with minimal downtime. The manufacturer can precisely analyze equipment conditions using sensor data, artificial intelligence, and edge computing, avoiding costly, unexpected downtime. Sensor-equipped video cameras, for example, are used in chemical facilities to detect corrosion in pipelines and inform workers before any damage is done.

Quality Control

Detecting flaws is an essential aspect of the manufacturing process. Defects must be caught in real time when running an assembly line that produces millions of units. Edge computing devices can make microsecond choices, detect faults immediately, and warn workers. This feature gives firms a significant edge by reducing waste and increasing manufacturing efficiency.

Equipment Efficiency

Manufacturers are constantly seeking ways to optimize their operations. When combined with sensor data, Edge computing can evaluate overall equipment effectiveness. For example, in the automobile welding process, producers must follow several regulations to ensure that their welding is of the highest quality. Companies can monitor products in real-time using sensor data and edge computing, catching flaws or safety issues before they leave the facility.

Yield Optimization

Knowing the exact quantity and quality of the components used in the manufacturing process is crucial in food processing plants. Machines can rapidly recalibrate if any parameters need to be modified to generate better-quality products using sensor data, AI, and edge computing. There is no requirement for manual oversight or data to be sent to a central location for analysis. The sensors on-site are capable of making real-time decisions to boost yields.

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Edge AI in Healthcare

The use cases of Edge Artificial Intelligence in Healthcare are described below:

Monitoring of Hospital Rooms

In general, the ability to automate processes is one of AI's distinctive selling points. AI algorithms may collect data from various sensors and analyze it to choose the best course of action. This is taken to the next level with Edge AI. It provides autonomous monitoring of hospital rooms and patients using computer vision and data from other sensors.

Fall Detection

Many wearables now include the capacity to detect if a person falls suddenly by utilizing specific technology. These gadgets can be trained to detect falls in real time and even notify caretakers. In most circumstances, this can save a person's life. The fall detection feature on the Apple Watch is an example of this.

Monitoring vital indicators is another area where it can be quite useful. Medical equipment that captures heart rate, temperature, respiration rate, blood pressure, and other parameters can use AI to detect any anomaly in real time. The devices may then alert hospital staff, who could subsequently take action. This is important for the patient, but it also improves their whole experience.

Radiology

DICOM (Digital Imaging and Communications) images in radiology are quite huge. As a result, sending these photos to the cloud or a central server for processing and receiving machine learning inference can be quite costly and time-consuming. On the other hand, Edge AI allows the analysis to take place locally, resulting in a considerably faster diagnosis.

In the healthcare industry, advanced imaging applications are used. Some of them are:

Abnormalities in the Cardiovascular System

The automation of cardiovascular problems in routine imaging tests such as chest X-rays may result in speedier decision-making and fewer diagnostic errors. Artificial Intelligence applied to the concept of imaging data can also aid in recognizing key issues such as muscle thickening, monitoring changes in total blood flow in the heart, and so on.

Fractures and Other Musculoskeletal Injuries Detection

Using Artificial Intelligence to detect hairline fractures, soft tissue injuries, and dislocations can help surgeons feel more confident about their treatment options. Supporting the Diagnosis of Neurological Disorders, Algorithms can help speed up the diagnosis of neurological diseases by identifying photos with questionable findings and providing risk ratios for whether the images contain signs of PLS or ALS.

Edge AI in Smart Homes

Edge Artificial Intelligence is highly used in making smart homes. Some of the use cases of edge Artificial Intelligence in smart homes are:

Security and Privacy

Smart security devices such as security cameras, video doorbells, and alarms send data to fog nodes established in the home network. The edge node analyzes this information locally to detect unwanted activity, provide notifications, and raise alarms.

Entertainment

Entertainment is a significant plus in a smart home concept. It's ideal to control music, audio, or video in a smart home setup. The rise of smart televisions has also increased their position in smart home setups.