Lightweight Joint Simulation of Vehicular Mobility and Communication with LIMoSim

TL;DR

This paper introduces LIMoSim, a lightweight, integrated simulation framework for vehicular mobility and communication that simplifies setup and models high interdependency scenarios, aiding development of ITS and autonomous vehicle communication systems.

Contribution

LIMoSim is a novel integrated simulation tool that eliminates the need for IPC, reducing complexity and enabling realistic joint mobility and communication modeling.

Findings

Effective in LTE scenarios with real-world maps

Reduces simulation setup complexity

Models high interdependency between mobility and communication

Abstract

The provision of reliable and efficient communication is a key requirement for the deployment of autonomous cars as well as for future Intelligent Transportation Systems (ITSs) in smart cities. Novel communications technologies will have to face highly-complex and extremely dynamic network topologies in a Vehicle-to-Everything (V2X)-context and will require the consideration of mobility information into decision processes for routing, handover and resource allocation. Consequently, researches and developers require simulation tools that are capable of providing realistic representations for both components as well as means for leveraging the convergence of mobility and communication. In this paper, we present a lightweight framework for the simulation of vehicular mobility, which has a communications-oriented perspective by design and is intended to be used in combination with a network simulator. In contrast to existing approaches, it works without requiring Interprocess Communication (IPC) using an integrated approach and is therefore able to reduce the complexity of simulation setups dramatically. Since mobility and communication share the same codebase, it is able to model scenarios with a high level of interdependency between those two components. In a proof-of-concept study, we evaluate the proposed simulator in different example scenarios in an Long Term Evolution (LTE)- context using real-world map data.