How AI Can Improve IOT-Edge Work?

People are becoming increasingly curious about how artificial intelligence (AI) can optimize Internet of Things (IoT) applications as more companies integrate IoT devices and edge computing capabilities. Some thoughts to ponder are listed below.

1. With machine learning, we can improve IoT sensor inference accuracy

Using machine learning to improve edge-deployed IoT sensors is still in its infancy among technology researchers. There were some early applications involving the use of sensors to process natural language or to classify images. One example, however, shows how people are progressing.

It has been recognized by researchers at IMDEA Networks that IoT sensors cannot guarantee specific quality-of-service requirements, such as latency and inference accuracy, when they are used for deep-learning tasks. Machine learning algorithm AMR2 was developed to help with this challenge by the people working on this project.

IoT sensor inferences are made more accurate using AMR2's edge computing infrastructure, which enables faster responses and real-time analysis. Experiments suggested the algorithm improved inference accuracy by up to 40% compared to the results of basic scheduling tasks that did not use the algorithm.

For IoT sensors to function properly at the edge, an efficient scheduling algorithm such as this one is essential. According to a project researcher, using AMR2 for a service similar to Google Photos, which classes images based on their elements, could cause an execution delay. The algorithm could be deployed by the developer so that the user does not experience such delays.

2. AI at the Edge Reduces Energy Consumption of Connected Devices

80% of chief financial officers at tech companies expect revenue increases in 2023. The most likely way to accomplish that is to provide customers with products and services that meet their needs.

People are expected to wear IoT devices almost constantly according to the manufacturers. There are some wearables that detect if a lone worker falls over or becomes distressed, or if someone in a physically demanding job becomes too tired and needs rest. It is important for users to feel confident that their IoT devices will work reliably throughout their workdays.

In order to study the effects of a sedentary lifestyle on health and how correct posture could improve outcomes, researchers explored how AI at the edge could improve IoT devices' energy efficiency.

Due to the large volume of data transmitted, the batteries only lasted a few hours. Researchers found that a nine-channel motion sensor reading 50 samples per second produces more than 100 MB of data each day.

These examples show some of the things researchers focused on when exploring how artificial intelligence could improve the functionality of IoT devices deployed at the edge. Let them provide valuable insights and inspiration about how you might get similar results. It’s almost always best to start with a clearly defined problem you want to solve. Then, start exploring how technology and innovative approaches could help meet that goal.

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