Name
Abstract | ||
|---|---|---|
To improve the quality of wireless communication and extend the range of networking applications in Vehicular Ad Hoc Networks (VANET), a hybrid VANET structure is proposed by the combination of the Wireless Mesh Network (WMN) and the Ad Hoc Network. Making use of location information, congestion monitoring and routing switch, we design a geographic load balancing routing in hybrid VANETs, namely GLRV. The mesh routers are deployed to provide backbone supports. Data packet are transmitted in the form of forwarding set to provide multiple forwarding candidates. Three routing switch strategies are designed to ensure the Quality of Service (QoS) under various network connectivity and load scenarios, which are mesh routing when the mesh router is available, geographic greedy routing when the network connectivity is good, and opportunistic routing when the network connectivity is poor. Simulation results show that GLRV can reduce the transmission latency and increase network delivery ratio in hybrid VANET architecture. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ITSC.2011.6083019 | ITSC |
Keywords | Field | DocType |
vanet,geographic greedy routing,network delivery ratio,wmn,quality of service,glrv,geographic load balancing routing,wireless mesh networks,resource allocation,wireless mesh network,congestion monitoring,vehicular ad hoc networks,qos,mesh router,telecommunication network routing,routing switch strategy,vehicular ad hoc network,data packet transmission,multiple forwarding candidate | Dynamic Source Routing,Static routing,Computer network,Wireless Routing Protocol,Adaptive quality of service multi-hop routing,Optimized Link State Routing Protocol,Engineering,Wireless ad hoc network,Wireless mesh network,Geographic routing,Distributed computing | Conference |
Volume | Issue | ISSN |
null | null | 2153-0009 |
ISBN | Citations | PageRank |
978-1-4577-2198-4 | 12 | 0.70 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Wu | 1 | 36 | 5.37 |
| Juan Luo | 2 | 14 | 1.44 |
| Renfa Li | 3 | 647 | 97.10 |
| Amelia C. Regan | 4 | 12 | 0.70 |