The integration of edge computing with IoT (EC-IoT) systems provides significant
improvements in addressing security and privacy challenges in IoT networks. This paper …

The development of AI applications, especially in large-scale wireless networks, is growing
exponentially, alongside the size and complexity of the architectures used. Particularly …

This paper presents an analysis of the performance of the Energy Aware Scheduling
Algorithm (EASA) in a 5G green communication system. 5G green communication systems …

Federated Learning (FL) is an emerging paradigm for training machine learning models
across decentralized edge devices, ensuring data privacy and reducing computational tasks …

Federated Learning (FL) has emerged as a pivotal approach for decentralized Machine
Learning (ML), addressing the unique demands of the Internet of Things (IoT) environments …

The development of the Internet of Things (IoT) paradigm and its significant spread as an
affordable data source has brought many challenges when pursuing efficient data collection …

In today's world, the importance of the Green Internet of Things (GIoT) in the transformed
sustainable smart cities cannot be overstated. For a variety of applications, the GIoT may …

Federated learning (FL) is considered a promising machine learning technique that has
attracted increasing attention in recent years. Instead of centralizing data in one location for …

In this article, we utilize Digital Twins (DT) with edge networks using blockchain technology
for reliable real-time data processing and provide a secure, scalable solution to bridge the …

The surge in adopting Internet of Things (IoT) devices has presented fresh challenges
regarding security and networks. Conventional centralized methods encounter scalability …