Dr. Shalli Rani(Director, Research) completed her Post-doc from Manchester Metropolitan University, UK in June , 2023. She is Professor in Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India. She has 18+ years teaching experience. She received MCA degree from Maharishi Dyanand University, Rohtak in 2004 and the M.Tech. degree in Computer Science from Janardan Rai Nagar Vidyapeeth University, Udaipur in 2007 and Ph.D. degree in Computer Applications from Punjab Technical University, Jalandhar in 2017. Her main area of interest and research are Wireless Sensor Networks, Underwater Sensor networks, Machine Learning and Internet of Things. She has published/accepted/presented more than 100+ papers in international journals /conferences (SCI+Scopus) and edited/authored five books with international publishers. She is serving as the associate editor of IEEE Future Directions Letters. She served as a guest editor in IEEE Transaction on Industrial Informatics, Hindawi WCMC and Elsevier IoT Journals. She has also served as reviewer in many repudiated journals of IEEE, Springer, Elsevier, IET, Hindawi and Wiley. She has worked on Big Data, Underwater Acoustic Sensors and IoT to show the importance of WSN in IoT applications. She received a young scientist award in Feb. 2014 from Punjab Science Congress, Lifetime Achievement Award and Supervisor of the year award from Global Innovation and Excellence, 2021. .Her work has gained global and reputed recognition, and she has been nominated as one of the top 2% of scientists in her field by Stanford University.
Wireless Sensor Networks (WSNs) have emerged as a fundamental technology in modern digital ecosystems, enabling real-time data acquisition and communication. Their integration with the metaverse enhances immersive experiences by providing real-time environmental data, motion tracking, and networked interactions. However, the fusion of WSNs with the metaverse introduces significant security challenges, including network vulnerabilities, data privacy concerns, latency issues, and scalability constraints, which hinder seamless operation. To address these challenges, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques have been leveraged to enhance network secu... More >
Wireless Sensor Networks (WSNs) are prone to different security threats because of their open communication environment, distributed architecture, and resource constraints. For the security and integrity of a network to be ensured, robust intrusion detection systems (IDS) are required. The WSN-DS dataset has been used to provide an effective machine learning (ML) and Deep Learning (DL) based IDS and attack detection technique for WSNs. Several learning models, including K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Convolutional Neural Networks (CNN), Support Vector Machine (SVM), Logistic Regression (LR), and Neural Networks (NN), are compared in terms of performance. P... More >
With the goal of advancing the field of wireless communications, we are excited to present the inaugural issue of ICCK Transactions on Wireless Networks (TWN). The cutting-edge research in this field is becoming more and more necessary as wireless technology becomes more widely used. Wireless networks are essential to the innovations altering sectors around the world, from 5G to the growing Internet of Things (IoT). This journal intends to be the premier source for publishing high-quality research on both established and developing wireless networking subjects. Wireless communication technologies have advanced dramatically over the previous few decades, becoming a substantial contribution to... More >
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