Real-Time Physical Threat Detection on Edge Data Using Online Learning

Abstract

Sensor-powered devices offer safe global connections, cloud scalability and flexibility, and new business value driven by data. The constraints that have historically obstructed major innovations in technology can be addressed by advancements in artificial intelligence (AI) and machine learning, cloud, quantum computing, and the ubiquitous availability of data. Edge artificial intelligence refers to the deployment of AI applications on the edge device near the data source rather than in a cloud computing environment. Although edge data have been utilized to make inferences in real time through predictive models, real-time machine learning has not yet been fully adopted. Real-time machine learning utilizes real-time data to learn on the go, which helps in faster and more accurate real-time predictions and eliminates the need to store data eradicating privacy issues. In this article, we present the practical prospect of developing a physical threat detection system using real-time edge data from security cameras/sensors to improve the accuracy, efficiency, reliability, security, and privacy of the real-time inference model.