Confluence of edge computing and deep learning - Gunawan, Teddy Surya (IEEE, 2022)
Massive amounts of data are generated by ubiquitous sensors and smart devices in industries and communities, and ever-increasing processing power is shifting the center of computation and services away from the cloud and toward the network edge. It is difficult to capture psychological, emotional, and physiological states, especially during a pandemic, and to use the sensory data collected within the pandemic management ecosystem. Recent advances in edge computing have shown promising results when it comes to collecting various types of emotional and physical health data from the home environment. Deep learning (DL) applications of the highest caliber can operate in a resource-constrained edge environment, allowing for local processing of data from edge devices and inference relevant to in-home health. This keeps health data close to the user's edge while maintaining the privacy, confidentiality, and low latency of the inferencing system. The recent advances in edge computing and deep learning will be discussed. The confluence of edge computing and deep learning will be explored further.