Source-Code Analysis of iFogSim for Simulating Distributed IoT Architectures: Coverage, Challenges, and Enhancements

TL;DR

This paper evaluates iFogSim's capabilities for simulating distributed IoT architectures, providing a comprehensive analysis, practical experience, and recommendations for improvement.

Contribution

It offers a structured survey of iFogSim, compares it with other tools, and shares detailed insights from simulating a smart emergency response system.

Findings

iFogSim effectively simulates certain IoT architectures but has notable modeling challenges.

Quantitative results include 205 ms alert latency and 10x FPGA speedup.

Seven developer recommendations aim to enhance iFogSim's accuracy and usability.

Abstract

Simulation is an indispensable tool for validating distributed IoT architectures before physical deployment, and iFogSim has emerged as one of the most widely adopted platform in the fog and edge computing research community. Yet the experience of using iFogSim for non-canonical, application-specific architectures remains incompletely documented, leaving practitioners without guidance on when the tool is appropriate, which scientific objectives it can address, and how to manage the modelling approximations it imposes. This article helps in providing that guidance through two complementary contributions. First, we present a structured state of the art covering iFogSim and iFogSim2, a taxonomy of ten scientific objectives that motivate IoT architecture simulation, and a comparative survey of eight simulation tools assessed against those objectives. Second, we report our experience of simulating a four-tier smart emergency response system for resource-constrained urban environments, covering a 25-node synthetic road topology, four experimental configurations, and quantitative results including end-to-end alert latency (near 205 ms), FPGA-accelerated Dijkstra path computation (x10 CPU speedup), concurrent incident conflict rates (75% under dual load), and path-cache acceleration (x197). The analysis is organised around five practitioner questions: whether iFogSim fits the target architecture, which objectives it covers natively versus partially, what modelling challenges arise and how their workarounds bias reported results, what changes to the iFogSim source code would close the identified gaps, and whether tool co-simulation can provide comprehensive coverage. Seven modelling challenges are documented with source-code-grounded root causes and explicit bias assessments; finally, seven developer recommendations are proposed as an actionable improvement roadmap for the iFogSim community.