The paper introduces iFogSim, a simulator designed to model and evaluate resource management techniques in IoT, Edge, and Fog computing environments. The primary goal is to address the challenges of real-time analytics and low latency in IoT applications, which often require data processing close to the source of data. iFogSim enables the simulation of resource management policies, including application placement and scheduling, to minimize latency, network congestion, energy consumption, and operational costs. The simulator supports two main application models: Sense-Process-Actuate and Stream Processing, and can handle various physical network topologies. Two case studies are presented to demonstrate the effectiveness of iFogSim: a latency-sensitive online game and an intelligent surveillance system. The paper also evaluates the scalability of iFogSim in terms of RAM consumption and execution time under different scenarios.The paper introduces iFogSim, a simulator designed to model and evaluate resource management techniques in IoT, Edge, and Fog computing environments. The primary goal is to address the challenges of real-time analytics and low latency in IoT applications, which often require data processing close to the source of data. iFogSim enables the simulation of resource management policies, including application placement and scheduling, to minimize latency, network congestion, energy consumption, and operational costs. The simulator supports two main application models: Sense-Process-Actuate and Stream Processing, and can handle various physical network topologies. Two case studies are presented to demonstrate the effectiveness of iFogSim: a latency-sensitive online game and an intelligent surveillance system. The paper also evaluates the scalability of iFogSim in terms of RAM consumption and execution time under different scenarios.