A. Ibraheem, Z. Sheng, G. Parisis, D. Tian, In-Vehicle Network Delay Tomography, in Proc. of IEEE GLOBECOM, 2022.

Due to the increased complexity of new in-vehicle networking architectures, which makes direct monitoring of internal network components intractable, alternative solutions are required to tackle this issue. One solution is to leverage the end-to-end measurements to estimate the internal network performance. To this end, we propose to employ network tomography as a monitoring tool for in-vehicle networks. Network tomography can infer the overall network performance by measuring only subset of the network. We investigate the use of network tomography in in-vehicle network by analysing network identifiability of three main architectures: bus-based, central-gateway, and Ethernet-based architectures. Our analysis results indicate the applicability of network tomography in in-vehicle networks based on certain topological and monitors’ conditions. Furthermore, we validate our analytical results through simulation which shows a maximum error of only 0.174 milliseconds. Moreover, we compare the proposed approach with one of existing solutions and show that network tomography achieves better performance with minimal monitoring overhead of up to 52.2% and 782.3 microseconds bandwidth and latency improvements, respectively.