International Journal of Science, Engineering and TechnologyAuthors: Laksh Veridhan
Abstract: The rapid growth of Internet of Things (IoT) deployments over wireless networks has intensified the demand for intelligent data processing and real-time decision-making. Traditional cloud-centric intelligence models struggle to meet the requirements of large-scale, latency-sensitive, and privacy-aware IoT applications. Distributed intelligence has emerged as a promising paradigm that decentralizes learning, inference, and control across edge devices, fog nodes, and cloud platforms. This review presents a comprehensive analysis of distributed intelligence models for cloud-supported IoT systems operating over wireless networks. It examines system architectures, distributed artificial intelligence and machine learning techniques, wireless communication considerations, and cloud-assisted orchestration mechanisms. Key paradigms such as edge intelligence, fog intelligence, hybrid cloud–edge intelligence, and collaborative learning are discussed, along with enabling technologies including federated learning, distributed deep learning, and reinforcement learning. The review also addresses critical security and privacy challenges, performance evaluation metrics, and real-world applications spanning smart cities, industrial IoT, healthcare, and energy systems. Finally, open challenges and future research directions are identified, highlighting the need for scalable, adaptive, and secure distributed intelligence frameworks to support next-generation IoT ecosystems.
DOI: https://doi.org/10.5281/zenodo.18161274
