Improving scalability of wireless network simulation with bounded inaccuracies

Abstract

Discrete event network simulators have emerged as popular tools for verification and performance evaluation of wireless networks. Nevertheless, the desire to model such networks at high fidelity implies high computational costs, limiting most researchers the ability to simulate networks with thousands of nodes. Previous attempts to optimize simulation of large-scale wireless networks have not appropriately modeled accumulation of weak interference, thereby suffering inaccuracies that may be further magnified in the evaluation of upper-layer protocols. This article presents a comprehensive analysis on the effects of common optimization techniques for large-scale wireless network simulation on the overall network performance. Based on the analysis, it formulates distance limit derivation and mobility update reduction that introduce bounded inaccuracy to the radio propagation simulation. It further proposes a novel technique, Lazy Event Scheduling with Corrective Retrospection, that reduces simulation events twenty-five fold without introducing any inaccuracy at all. The experimental results show that these optimizations can substantially improve the runtime performance of an already efficient wireless network simulator, by a factor of up to 55 for wireless networks with 3200 nodes without compromising the simulation's accuracy.