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Ȩ Ȩ > ¿¬±¸¹®Çå > ¿µ¹® ³í¹®Áö > TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)

TIIS (Çѱ¹ÀÎÅͳÝÁ¤º¸ÇÐȸ)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) Routing Protocols for VANETs: An Approach based on Genetic Algorithms
¿µ¹®Á¦¸ñ(English Title) Routing Protocols for VANETs: An Approach based on Genetic Algorithms
ÀúÀÚ(Author) Emilio C. G. Wille   Hermes I. Del Monego   Bruno V. Coutinho   Giovanna G. Basilio  
¿ø¹®¼ö·Ïó(Citation) VOL 10 NO. 02 PP. 0542 ~ 0558 (2016. 02)
Çѱ۳»¿ë
(Korean Abstract)
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(English Abstract)
Vehicular Ad Hoc Networks (VANETs) are self-configuring networks where the nodes are vehicles equipped with wireless communication technologies. In such networks, limitation of signal coverage and fast topology changes impose difficulties to the proper functioning of the routing protocols. Traditional Mobile Ad Hoc Networks (MANET) routing protocols lose their performance, when communicating between vehicles, compromising information exchange. Obviously, most applications critically rely on routing protocols. Thus, in this work, we propose a methodology for investigating the performance of well-established protocols for MANETs in the VANET arena and, at the same time, we introduce a routing protocol, called Genetic Network Protocol (G-NET). It is based in part on Dynamic Source Routing Protocol (DSR) and on the use of Genetic Algorithms (GAs) for maintenance and route optimization. As G-NET update routes periodically, this work investigates its performance compared to DSR and Ad Hoc on demand Distance Vector (AODV). For more realistic simulation of vehicle movement in urban environments, an analysis was performed by using the VanetMobiSim mobility generator and the Network Simulator (NS-3). Experiments were conducted with different number of vehicles and the results show that, despite the increased routing overhead with respect to DSR, G-NET is better than AODV and provides comparable data delivery rate to the other protocols in the analyzed scenarios.
Å°¿öµå(Keyword) VANETs   Mobility Simulation   Routing Algorithms   AODV   DSR   Genetic Algorithm   Performance Evaluation  
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