Movement, pressure, temperature, humidity, sound frequency — sensors that are now integrated into our daily lives collect an endless stream of data about the way we interact with products and their environments.
From autonomous vehicles to health monitoring devices, the ever-growing amount of smart devices and information generated is becoming challenging to manage and more expensive to process.
“This massive amount of data needs to be stored and analyzed, and as a result, real-time processing is critical,” says Afshin Rahimi, an assistant professor of mechanical and aerospace engineering. “We are examining new techniques to accelerate the process.”
One of which is using gateway devices to analyze the data with deep learning models. This is called edge computing, and 90 per cent of industrial enterprises will be using it by 2022, according to a report by business consultants Frost & Sullivan.
The name edge is in reference to applying a deep learning model to analyze the data at the edge of a framework — where data is acquired — differing from cloud computing, which conducts the analysis on a remote server — where data is usually warehoused.