The Best of Both Worlds: Hybrid Data-Driven and Model-Based Vehicular Network Simulation

TL;DR

This paper introduces a hybrid simulation approach combining model-based mobility, radio environmental maps, and data-driven network simulation to accurately and efficiently analyze vehicular networks, outperforming traditional methods in realism and speed.

Contribution

The paper presents a novel hybrid simulation framework that integrates multiple data sources and modeling techniques for improved vehicular network analysis.

Findings

Better mimicry of real-world behavior compared to traditional simulation.

Achieves 300 times higher computational efficiency.

Validated with real-world measurements and ns-3 simulations.

Abstract

The analysis of the end-to-end behavior of novel mobile communication methods in concrete evaluation scenarios frequently results in a methodological dilemma: Real world measurement campaigns are highly time-consuming and lack of a controllable environment, the derivation of analytical models is often not possible due to the immense system complexity, system-level network simulations imply simplifications that result in significant derivations to the real world observations. In this paper, we present a hybrid simulation approach which brings together model-based mobility simulation, multi-dimensional Radio Environmental Maps (REMs) for efficient maintenance of radio propagation data, and Data-driven Network Simulation (DDNS) for fast and accurate analysis of the end-to-end behavior of mobile networks. For the validation, we analyze an opportunistic vehicular data transfer use-case and compare the proposed method to real world measurements and a corresponding simulation setup in Network Simulator 3 (ns-3). In comparison to the latter, the proposed method is not only able to better mimic the real world behavior, it also achieves a 300 times higher computational efficiency.