Fog computing is a paradigm that brings computational resources and services to the
network edge in the vicinity of user devices, lowering latency and connecting with cloud …

Businesses use sentiment analysis for market insights to improve performance. Sentiment
analysis application in building a smart society is immense, and there is a need to delineate …

Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …

In the traditional cloud-based Internet of Vehicles (IoV) architecture, it is difficult to guarantee
the low latency requirements of the current intelligent transportation system (ITS). As a …

The exponential growth in Internet of Things (IoT) devices and the limitations of cloud
computing in terms of latency and quality of service for time-sensitive applications have led …

In the context of big data, if the task of multivariate time series data anomaly detection cannot
be performed efficiently and accurately, it will bring great security risks to industrial systems …

Instead of using cloud computing technology directly, IoT and Fog computing has introduced
new data management methods that seem promising. Applications for real-time analytics …

In recent years, cloud computing has been widely used. Cloud computing refers to the
centralized computing resources, users through the access to the centralized resources to …

Industry 4.0 will only become a reality through the convergence of Operational and
Information Technologies (OT & IT), which use different computation and communication …

Fog computing is a promising paradigm aimed at reducing latency and network traffic
between end devices and cloud servers. In fog computing systems, a fog node can be …