Call for Papers

Special Issue on : "Machine learning techniques for secure and energy-efficient IoT networks"

Guest Editors:

Arvind Dhaka
Manipal University Jaipur, India

Siddhartha Chauhan
NIT Hamirpur, India

Edmar Candeia Gurjao
Federal University of Campina Grande, Brazil

Amita Nandal
Manipal University Jaipur, India

Theme and Scope

The Internet of Things (IoT) is a new paradigm for the development of ubiquitous computing, which enables the connection of billions of heterogeneous devices with each other and the cloud. IoT is being used in many different applications, from smart homes to big data analytics. However, the IoT infrastructure is vulnerable to security threats, such as data breaches, man-in-the-middle attacks, and malicious actors. Furthermore, energy consumption is an important factor in IoT networks, as many IoT devices are battery-powered and need to conserve energy. Machine learning techniques are being increasingly used to enhance the security and energy efficiency of the IoT network. Machine learning algorithms can be used to detect malicious activities in the network and protect against them. It can also be used to identify anomalies in the network, which can be used to detect potential security threats. Furthermore, machine learning techniques can be used to optimize energy consumption in the network, by predicting energy demands and adjusting the network accordingly.

The Internet of Things (IoT) is a network of physical objects that are embedded with sensors, software, and other technologies for the purpose of collecting and exchanging data. As the use of IoT networks increase, security and energy efficiency become increasingly important topics of research. Machine learning techniques can be used to improve the security and energy efficiency of IoT networks. This call for papers invites authors to submit original research on the use of machine learning techniques for secure and energy-efficient IoT networks. We welcome both theoretical and applied research papers on topics such as, but not limited to: