As the mobile and automotive industries move towards autonomous vehicles, many advanced driving applications have been developed. These driving applications may require different levels of intelligence, communication capabilities, and processing power from the communication network and processing platform. These advanced driving applications can be grouped into static, which runs all the time as the engine starts and dynamic, which runs for a duration of time depending on the vehicle conditions. After the emergence of IEEE Time-Sensitive Networking (TSN) features for Ethernet technology, the automotive industry started to move towards the usage of TSN for advanced driving applications. However, IEEE TSN poses a challenge in streamlining the schedules and routes of the dynamic traffic since they require swift and fast determination of transmission schedules and routes on-the-fly. In this paper, we mainly focus on static traffic and device a novel static scheduling and routing algorithm that would be conducive for dynamic traffic requirements. In this approach, we have developed Mixed-integer programming (MIP) based joint scheduling and routing of static applications with the aim of load balancing such that more dynamic traffic would be schedulable as the vehicle drives off. We proposed two load-balancing based objective functions and conducted an experimental analysis of objective functions with six different vehicle network configurations in two scales of zonal architecture. Experimental evaluations show the efficacy of our developed algorithm, in which the load is balanced in the egress port of the network, which in turn can schedule more dynamic traffic.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    MIP-based Joint Scheduling and Routing with Load Balancing for TSN based In-vehicle Networks


    Contributors:


    Publication date :

    2020-12-16


    Size :

    1352541 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Load balancing routing algorithm based on geographical location code

    Li, Hui / Chai, Furong / Chen, Dong et al. | IEEE | 2022




    Deep Reinforcement Learning Based Load Balancing Routing for LEO Satellite Network

    Zuo, Peiliang / Wang, Chen / Wei, Zhanzhen et al. | IEEE | 2022