Mobile cluster assisted routing for urban VANET

Naveen, K. P.; Karuppanan, Komathy

doi:10.1109/icrtit.2011.5972426

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Given the light traffic load (i.e., 20 sessions), when the vehicle's speed is low (i.e., below 40 km/hr), the proposed scheme adopts Level-1 and thus generates lowest packet delivery rate (in Figure 20(a)) and longest end-to-end delay (in Figure 21(a)). Oppositely, when the vehicle's speed gets faster (i.e., above 40 km/hr) or the traffic load becomes heavier (i.e., 40 and 60 sessions), the proposed scheme adopts (1) case (QoS ) (2) if ( < ) (3) set Level-1 Cluster (4) if else ( = ) (5) if ( < V ) (6) set Level-1 Cluster (7) if else ( ≥ V ) (8) set Level-2 Cluster (9) end (10) if else ( < ≤ ) (11) set Level-2 Cluster (12) if else ( < ) (13) set Level-2 Cluster (14) if ( < V ) (15) set communication range as 250 (16) if else ( = V ) (17) set communication range as 200 (18) if else ( V < < ℎV ) (20) set communication range as 150 (21) if else ( ≥ ℎV ) (22) set communication range as 100 (23) end ( Level-2. The performance of the proposed scheme, including packet delivery rate (in Figures 20(b), 20(c), and 20(d)), the end-to-end delay (in Figures 21(b), 21(c), and 21(d)), and the signaling overhead (in Figures 22(b), 22(c), and 22(d)), fall between those of the ADOV+ and MGRP.…”

Section: Comparing Different Methodsmentioning

confidence: 99%

“…Three methods for finding gateways, namely, reactive, proactive, and hybrid gateway discovery, are introduced and compared in this study. In [21], the packet delivery rate can be greatly improved by combining static node mechanism and mobile cluster approach. A static node is set up at the road junction to meet the high mobility and scalability requirements while mobile cluster is used for helping routing in VANETs.…”

Section: Preliminarymentioning

confidence: 99%

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An Adaptive Clustering Scheme for Improving the Scalability in Intelligent Transportation Systems

Lin

Weng

Lee

et al. 2013

International Journal of Distributed Sensor Networks

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Intelligent Transportation Systems (ITSs) aim at making the vehicle users be safer, comfortable, and better informed. Internetenabled ITS applications get increasingly attractive recently. In this context, gateway is the portal of accessing the Internet. The efficiency of gateway management in ITSs thus profoundly affects the applicability of Internet-enabled applications. When the number of vehicles on the road greatly increased, the effectiveness of gateway requisition challenges the usage of the ITS services. To remedy the scalability problem in accessing the Internet in ITSs, an adaptive clustering scheme for gateway management is proposed in this paper. In the proposed scheme, the gateways are organized as a two-level cluster architecture. The gateways are dynamically employed according to the QoS requirements of the applications and current networking conditions. As shown in the simulation results, the proposed scheme is able to achieve the QoS requirements with fair gateway deployment cost.

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Section: Comparing Different Methodsmentioning

confidence: 99%

Section: Preliminarymentioning

confidence: 99%

An Adaptive Clustering Scheme for Improving the Scalability in Intelligent Transportation Systems

Lin

Weng

Lee

et al. 2013

International Journal of Distributed Sensor Networks

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“…Network management architectures designed for terrestrial wire‐line networks cannot be easily ported to airborne network due to their reliance on persistent connections [1]. Classical routing algorithms are inefficient in high mobility and scalability scenarios [2] as normally encountered in military operations. Additionally, adversaries might resort to interference or jamming to degrade network performance.…”

Section: Introductionmentioning

confidence: 99%

Context aware routing management architecture for airborne networks

2016

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Military environments require highly dynamic mobile ad hoc networks (MANETs) to meet operational mission requirements. Decision makers rely on the timely delivery of critical battlefield information to make informed determinations quickly and as accurately as possible. However, traditional MANET routing protocols do not provide quality of service (QoS). Furthermore, they do not implement active controls to minimise the impact of network congestion. This study proposes the use of the information embedded in an air tasking order (ATO) during the planning phase of military missions to optimise the network performance. The trajectories of relevant nodes (airborne platforms) participating in the MANET can be forecasted by parsing key information contained in the ATO. Using this idea it is possible to optimise network routes to minimise edge overutilisation and increase network throughput. In one simulated test case, there was a 25% improvement of network throughput, and 23% reduction on dropped packets. Using this technique, the authors can selectively preserve the QoS by establishing network controls that drop lowpriority packets when necessary. The algorithm improves the overall MANET throughput while minimising the packets dropped due to network congestion.

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