Systematic Analysis of Task Offloading Approaches for Fog Computing

With the exponential rise in Internet of Things (IoT) and 5G-based applications, there is an increasing demand for low-latency, energy-efficient, and real-time data processing capabilities. Fog computing has emerged as a promising paradigm that extends cloud services closer to the network edge, enabling faster and context-aware task execution. However, resource limitations and heterogeneity in fog environments make efficient task scheduling a complex challenge. This study presents a systematic analysis of task offloading and scheduling approaches in fog computing. A total of 26 research articles were carefully selected from databases such as Scopus, Web of Science, and Google Scholar. Each work is reviewed and compared based on performance metrics like makespan, latency, energy consumption, trust, fault tolerance, and scalability. In addition, the study highlights commonly used simulation tools and identifies prevailing research gaps. The findings aim to guide researchers toward the development of robust, adaptive, and intelligent scheduling models for fog environments. This work serves as a valuable reference for designing future fog-cloud scheduling systems.