The work presents a joint design framework that combines an energy-efficient MIMO receiver architecture with an optimized power allocation strategy for spatial NOMA in miniature UAV-assisted IoT networks. Specifically, we design a low-power receiver using spatial modulation and intelligent transmit antenna selection to minimize energy usage. Simultaneously, a dynamic power allocation scheme is developed to ensure fairness by allowing all users to act as active data users in different time slots. The air-to-ground channel is modeled by considering UAV altitude, mobility, and probabilistic line-of-sight characteristics. Simulation results demonstrate that at a UAV altitude of 50 meters, the pr... More >

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 >

The widespread uptake of the Internet of Medical Things (IoMT) has transformed healthcare by facilitating real-time monitoring and data-driven decision-making, but maintaining data privacy and security is a vital challenge because data breaches and unauthorized access are on the rise. Differential Privacy (DP) and Federated Meta-Learning (FML) are being seen as promising candidates to tackle these issues with the model performance maintained, wherein DP adds noise to sensitive data in a controlled manner for rigorous privacy assurance, and FML allows for personalized learning across distributed IoMT devices without the need for patient data centralization. This survey delves into the combina... 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 >