Peter Kimemiah Mwangi^1,, Stephen T. Njenga¹, Gabriel Ndung’u Kamau¹

Agent-Based Modelling (ABM) tools provide a cost-effective way to simulate complex systems like an Internet of Things Distributed Ledger Technology (IoT-DLT) networks, where nodes operate as autonomous agents. While physical testbeds are expensive, ABMs offer scalable and efficient alternatives. However, few studies compare ABM performance on standard consumer hardware. In this research, we evaluate NetLogo 6.3 and Agents.jl (Julia 1.9) by simulating an IoT-DLT model across two laptop configurations. Results show that Agents.jl runs up to 9× faster on newer hardware and 4× faster on older hardware compared to NetLogo, though it requires more setup. NetLogo remains user-friendly but underutilises system resources like GPU and multicore processing. The research uses inferential analysis tools, such as regression analysis, to rigorously evaluate the performance differences between the ABM tools and hardware configurations. This research helps researchers choose efficient ABM tools for large-scale simulations on personal computers, demonstrating that emerging tools like Agents.jl are promising candidates for future simulations.

Agent Based Modelling, Distributed Ledger Technology, Internet of Things, Julia, NetLogo, Performance, Simulation