These findings validate that embedding hierarchical micro-cooperative intelligence directly inside the communication mesh significantly enhances adaptability without increasing computational load.
The emergence of sixth-generation (6G) wireless networks is expected to reshape communication by delivering ultra-reliable, low-latency, and high-capacity connections for large-scale Internet of Everything (IoE) scenarios. Yet, sustaining a strong signal-to-noise ratio (SNR) in terahertz (THz) and visible light communi...
It is concluded that future Internet of Things systems should adopt communication-computation-learning co-design, lightweight and adaptive models, privacy-aware distributed intelligence, and cross-layer orchestration to achieve scalable, trustworthy, and energy-efficient edge intelligence.
The increasing demand of smart border security systems has stimulated the creation of IoT-based surveillance systems that combine deep learning and edge computing. The paper includes a review and system-level architecture of smart border surveillance based on IoT, Edge AI, and multi-modal data fusion. The proposed arch...
A distributed IDS-based collaborative FL (IDS-CFL) across different learning levels: device and fog-cloud, to reduce data transfer and improve accuracy and enable fast processing is proposed, using a Deep Convolutional Generative Adversarial Network model to train data at each level.
Recently, energy efficient signal processing has emerged as a critical area of research, driven by the increasing demand for cost-effective solutions in modern communication systems, particularly in Internet of Things (IoT) sensor networks applications. At the same time, artificial intelligence (AI)-based approaches ha...
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