Hybrid Edge–Cloud Models for IoT: A Systematic Review

Abstract

The rapid proliferation of Internet of Things (IoT) devices across diverse domains such as smart cities, healthcare, industrial automation, and intelligent transportation systems has led to an exponential growth in data generation and real-time processing demands. Traditional cloud-centric computing architectures, although powerful, face significant challenges in meeting the stringent requirements of modern IoT applications, particularly in terms of latency, bandwidth utilization, scalability, energy efficiency, and data privacy. To address these limitations, hybrid edge–cloud computing has emerged as a promising paradigm that synergistically integrates the global processing capabilities of cloud computing with the localized, low-latency intelligence of edge computing. This paper presents a comprehensive review of hybrid edge–cloud computing models for IoT systems, focusing on architectural designs, enabling technologies, task offloading strategies, scheduling mechanisms, and security frameworks. The role of key enabling technologies such as 5G/6G networks, virtualization, containerization, artificial intelligence, and software-defined networking in enhancing hybrid deployments is also discussed in detail. Additionally, this review examines task scheduling and resource management techniques that aim to optimize energy consumption, reduce response time, and improve system throughput.