How Does Analytics Work Properly With Edge Computing?

Edge Technology might be impacting the growth of building things in a parallel approach by connecting applications and systems and moving them to the cloud faster than ever before through the implementation of IoT. Parallelly, the number of end devices and data generated on the cloud is also increasing sensors, mobile devices, and wearable technology in the IoT ecosystem, generating a vast amount of decentralized data. Lack of reliable connectivity, delays, and difficulties in processing large data quantities on the cloud, therefore, raised challenges in analyzing and extracting important insights from this data.

To overcome this challenge, enterprises are leveraging edge analytics with the help of cloud computing. This will bring stability to IoT networks by bringing computational power nearer to the data source and reducing the delays in analytics, thus resulting in instantaneous vision and resolution. Because of this edge, analytics is able to create algorithms with the data and make crucial insights too.

With the advancement in semiconductor technology, MCUs and processors are equipped with more processing power, specialized hardware components, and computation capabilities, helping to provide faster analytics on the edge by deploying advanced machine learning methods, such as deep neural networks or convolutional neural networks.

A model developer on popular frameworks like TensorFlow, Keras, and Caffe can be deployed after optimization to run on inference devices like Android and micro-controllers. Inference engines are designed while considering capabilities of MCUs, like TensorFlow-Lite, TensorFlow-micro, CMSIS-NN, etc., can execute the quantized model on the edge for faster analytics.

Edge analytics benefits organizations where data insights are needed at the edge. According to the report, "Global Market for Automotive Sensor Technologies," the average number of sensors used in automobiles has increased from 50-60 to more than 100. As a direct result of an increase in sensors, we are now seeing a large increase in the amount of data provided by these vehicles. Edge analytics in the automotive industry will help companies collect, analyze, and process data in real-time, allowing them to take the necessary actions. Also, intelligent applications like collision prevention, traffic routing, eyes-off-the-road detection systems, etc. can be designed through artificial intelligence and machine learning on the edge.

This ensures optimized asset, usage, low maintenance, and passenger safety. Similarly, IoT-powered healthcare devices can collect patient's data. Edge analytics can analyze the collected data without the need for continuous network connectivity. A clinical treating a patient with a mobile device or tablet will be able to enter patient data into the analytics platform at the edge where it is processed and displayed in near real-time. This helps to treat patients faster and reduce visit frequency. Also, It adds a secure layer of computing power between the cloud and the device, thus safeguarding patients data too.

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