BRT: Bus-Based Routing Technique in Urban Vehicular Networks

Noureddine Chaib, Omar Sami Oubbati, Mohamed Lahcen Bensaad, Abderrahmane Lakas, Pascal Lorenz, Abbas Jamalipour

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


Routing data in Vehicular Ad hoc Networks is still a challenging topic. The unpredictable mobility of nodes renders routing of data packets over optimal paths not always possible. Therefore, there is a need to enhance the routing service. Bus Rapid Transit systems, consisting of buses characterized by a regular mobility pattern, can be a good candidate for building a backbone to tackle the problem of uncontrolled mobility of nodes and to select appropriate routing paths for data delivery. For this purpose, we propose a new routing scheme called Bus-based Routing Technique (BRT) which exploits the periodic and predictable movement of buses to learn the required time (the temporal distance) for each data transmission to Road-Side-Units (RSUs) through a dedicated bus-based backbone. Indeed, BRT comprises two phases: (i) Learning process which should be carried out, basically, one time to allow buses to build routing tables entries and expect the delay for routing data packets over buses, (ii) Data delivery process which exploits the pre-learned temporal distances to route data packets through the bus backbone towards an RSU (backbone mode). BRT uses other types of vehicles to boost the routing of data packets and also provides a maintenance procedure to deal with unexpected situations like a missing nexthop bus, which allows BRT to continue routing data packets. Simulation results show that BRT provides good performance results in terms of delivery ratio and end-to-end delay.

Original languageEnglish
Article number8835155
Pages (from-to)4550-4562
Number of pages13
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number11
Publication statusPublished - Nov 2020


  • VANETs
  • backbone
  • bus
  • learning process
  • routing

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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