6 research outputs found
Hybrid-Vehcloud: An Obstacle Shadowing Approach for VANETs in Urban Environment
Routing of messages in Vehicular Ad-hoc Networks (VANETs) is challenging due
to obstacle shadowing regions with high vehicle densities, which leads to
frequent disconnection problems and blocks radio wave propagation between
vehicles. Previous researchers used multi-hop, vehicular cloud or roadside
infrastructures to solve the routing issue among the vehicles, but they suffer
from significant packet delays and frequent packet losses arising from obstacle
shadowing. We proposed a vehicular cloud based hybrid technique called
Hybrid-Vehcloud to disseminate messages in obstacle shadowing regions, and
multi-hop technique to disseminate messages in non-obstacle shadowing regions.
The novelty of our approach lies in the fact that our proposed technique
dynamically adapts between obstacle shadowing and non-obstacle shadowing
regions. Simulation based performance analysis of Hybrid-Vehcloud showed
improved performance over Cloud-assisted Message Downlink Dissemination Scheme
(CMDS), Cross-Layer Broadcast Protocol (CLBP) and Cloud-VANET schemes at high
vehicle densities
BRT: Bus-based Routing Technique in Urban Vehicular Networks
International audienceRouting 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 RoadSide 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