FogZoneSim: A Zone-Based Simulator for Resource Management in Large-Scale IoT–Fog Networks

While cloud computing offers a more scalable and resource-rich alternative against localized edge and IoT device data processes, it falls short in latency-sensitive, safety-critical applications due to delays caused by geographical distance. In domains like healthcare, where real-time analysis of patient vitals and emergency alerts is essential, and in VANETs, where vehicles must instantly exchange data for collision avoidance and traffic safety, such delays can have serious consequences. To address these limitations, fog computing architectures are increasingly adopted to enable proximate, low-latency data processing, ensuring timely and reliable responses in both healthcare and intelligent transportation systems. However, adopting fog computing introduces additional complexities related to task offloading, task scheduling, resource management, heterogeneity, and scalability. While these scientific fields of study have gained significant attention in the past few years, the existing widely used simulation tools, e.g., FogWorkflowSim, cannot effectively address critical issues such as supporting large-scale IoT networks, handling environment partitioning for parallel execution, and managing diverse resource management policies. Furthermore, since they are written in Java, they do not support many of the existing Python-based Machine Learning (ML) libraries, which are significantly attractive to many researchers. To address these limitations, this paper introduces FogZoneSim, an advanced modular fog computing simulator specifically designed to manage large-scale IoT environments. FogZoneSim effectively simulates scalable IoT ecosystems by clearly modeling IoT resources, brokers, and the infrastructure, supporting dependent workflows, providing customizable policies, partitioning environments, and scaling across numerous IoT devices. Also, its seamless integration with ML algorithms through Python libraries is another strong point. According to an extensive set of simulations, FogZoneSim has demonstrated its ability to significantly enhance researchers’ capabilities in analyzing and optimizing resource management strategies, energy efficiency, execution time, and overall system performance.