Machine learning techniques have been widely adopted to assist in data analysis in a
variety of Internet of Things (IoT) systems. To enable flexible use of trained learning models …

Rapid developments in hardware, software, and communication technologies have
facilitated the emergence of Internet-connected sensory devices that provide observations …

Collecting and analysing heterogeneous data sources from the Internet of Things (IoT) and
Industrial IoT (IIoT) are essential for training and validating the fidelity of cybersecurity …

Abstract The Internet of Things (IoT) is an emerging paradigm that offers remarkable
opportunities for data mining and analysis. IoT envisions a world where all smartphones …

With the numerous Internet of Things (IoT) devices, the cloud-centric data processing fails to
meet the requirement of all IoT applications. The limited computation and communication …

In the Internet-of-Things (IoT) era, billions of sensors and devices collect and process data
from the environment, transmit them to cloud centers, and receive feedback via the Internet …

With the advent of rapid developments, large number of heterogeneous devices is able to
connect with the help of IOT technology. Although IOT possess very complex architecture …

How can the advantages of deep learning be brought to the emerging world of embedded
IoT devices? The authors discuss several core challenges in embedded and mobile deep …

Abstract The Internet of Things (IoT) has recently proliferated in both size and complexity.
Using multi-source and heterogeneous IoT data aids in providing efficient data analytics for …

Abstract Internet of Things (IoT) is often envisioned as a paradigm shift from the traditional
Internet to a scenario where all the “things” will be connected with the Internet. IoT forced the …