TY - GEN
T1 - Simulating Data Flows of Very Large Scale Intelligent Transportation Systems
AU - Tangirala, Nagacharan Teja
AU - Sommer, Christoph
AU - Knoll, Alois
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/6/24
Y1 - 2024/6/24
N2 - Vehicular Ad-hoc Network (VANET) simulations are integral to pave way for rapid adoption of Intelligent Transportation Systems (ITS) applications. Popular VANET simulators are built with high-fidelity network and mobility models to enable comprehensive evaluation. However, the computational complexity of high-fidelity models inhibits scalability. Further, the increasing infrastructure density of 5G and beyond increases the node count, thereby imposing further performance constraints. In this paper, we present a VANET simulation methodology called Disolv to enable large-scale analysis. The key decisions towards performance enhancement include simplifying mobility and network models, abstraction of packets to messages, utilization of a discrete-time instead of discrete-event paradigm for message exchanges, incorporation of data streams, and pre-computation of network links. The proposed methodology is implemented to evaluate the benefits through experiments. Results indicate that significant performance gains are observed with statistically irrelevant loss of precision. Further experiments are carried out to highlight the limitations of the Disolv approach. Finally, ITS applications suitable for evaluation with the Disolv approach are discussed.
AB - Vehicular Ad-hoc Network (VANET) simulations are integral to pave way for rapid adoption of Intelligent Transportation Systems (ITS) applications. Popular VANET simulators are built with high-fidelity network and mobility models to enable comprehensive evaluation. However, the computational complexity of high-fidelity models inhibits scalability. Further, the increasing infrastructure density of 5G and beyond increases the node count, thereby imposing further performance constraints. In this paper, we present a VANET simulation methodology called Disolv to enable large-scale analysis. The key decisions towards performance enhancement include simplifying mobility and network models, abstraction of packets to messages, utilization of a discrete-time instead of discrete-event paradigm for message exchanges, incorporation of data streams, and pre-computation of network links. The proposed methodology is implemented to evaluate the benefits through experiments. Results indicate that significant performance gains are observed with statistically irrelevant loss of precision. Further experiments are carried out to highlight the limitations of the Disolv approach. Finally, ITS applications suitable for evaluation with the Disolv approach are discussed.
KW - Dataflow Analysis
KW - Traffic Simulation
KW - Vehicular Network Simulation
UR - http://www.scopus.com/inward/record.url?scp=85197518452&partnerID=8YFLogxK
U2 - 10.1145/3615979.3656062
DO - 10.1145/3615979.3656062
M3 - Conference contribution
AN - SCOPUS:85197518452
T3 - ACM International Conference Proceeding Series
SP - 98
EP - 107
BT - SIGSIM PADS 2024 - Proceedings of the 38th ACM SIGSIM International Conference on Principles of Advanced Discrete Simulation
PB - Association for Computing Machinery
T2 - 38th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, SIGSIM-PADS 2024
Y2 - 24 June 2024 through 26 June 2024
ER -