A VANETs routing algorithm based on Euclidean distance clustering

Wang, Yunpeng; Lu, Guangquan; Yu, Guizhen

doi:10.1109/icfcc.2010.5497808

Cited by 19 publications

(9 citation statements)

References 9 publications

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“…Clustering of GRS is done based on the distance among the GRSs. Euclidean Distance from one GRS to the other GRSs is calculated and 3 GRSs which are close to the GRS from which distance has been calculated are grouped under one cluster [28]. We have ensured that a GRS doesn't belong to two or more clusters simultaneously.…”

Section: Numerical Resultsmentioning

confidence: 99%

Joint Resource Allocation and UAV Scheduling With Ground Radio Station Sleeping

et al. 2021

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Applications of Unmanned aerial vehicles (UAVs) have advanced rapidly in recent years. The UAVs are used for a variety of applications, including surveillance, disaster management, precision agriculture, weather forecasting, etc. In near future, the growing number of UAV applications would necessitate densification of UAV infrastructure (ground radio station (GRS) and ground control station (GCS)) at the expense of increased energy consumption for UAV communications. Maximizing the energy efficiency of this UAV infrastructure is important. Motivated by this, we propose joint resource allocation and UAV scheduling with GRS sleeping. Further, we propose the use of coordinated multipoint (CoMP) with joint transmission (JT) and non-orthogonal multiple access (NOMA) along with GRS sleeping to increase the coverage and data rates, respectively. We then present exhaustive simulation results showcasing the trade-off between throughput and energy efficiency with varying UAV densities. We also compare the coverage, throughput, and energy efficiency for NOMA, CoMP with JT and the benchmark scenario.INDEX TERMS Coordinated multi-point (CoMP), ground control station (GCS), ground radio station sleeping (GRSS), non-orthogonal multiple access (NOMA), unmanned aerial vehicle (UAV).

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Section: Numerical Resultsmentioning

confidence: 99%

Joint Resource Allocation and UAV Scheduling With Ground Radio Station Sleeping

et al. 2021

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“…In this step, we should choose a sufficient method depending on the circumstances and constraints of the problem. For instant, some of the proposed clustering methods are suitable for crowded paths [14,15] or some other ones are convenient for a way with low density of vehicles [16]. On the next step, we should find a sufficient CH.…”

Section: Overviewmentioning

confidence: 99%

Utilization of nested clustering in VANET to reduce data loss in mobile cloud computing

Arzhmand

Rashid

Ashrafi

2016

Vietnam J Comput Sci

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Over the recent few years, most of network service providers have tried to expand their services all around by newly emerged technologies in network, especially mobile cloud computing. Most emerging cars and vehicles with powerful computers and storages, can be equipped with such new technologies such as vehicular ad hoc network radio, a new wireless technology. These capabilities lead network service providers to use these potentials to expand their network services such as Internet on the roads. It seems there are enough materials and peripherals to design a convenient network but every new architecture will have its limitations and problems, such as data loss, in performance. In this paper at first, we propose a design to find a proper cluster head in a group of vehicles. Then, based on some mathematical and physical methods and criteria, we demonstrate that nested clustering can reduce data loss in real streets and at equipped traffic-light crossroads, noticeably. Finally, we perform the simulation experiments to demonstrate the efficiency of our proposed approach in comparison with other approaches.

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“…In approaches provided in [13]- [15], uses the location attribute for clustering the vehicles in the VANET. In [16] Syed et.…”

Section: Related Workmentioning

confidence: 99%

Rough Set Based Fuzzy Scheme for Clustering and Cluster Head Selection in VANET

Jinila

2015

ElAEE

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In Vehicular Ad hoc Network (VANET), clustering helps vehicles to communicate with other vehicles and to the nearby Road Side Unit (RSU). Conventional clustering methods follow precise clustering which degrades the stability of cluster formation. To stabilize the formation of clusters, the vehicles in the boundary region of more than one cluster should be uniquely added to the proper cluster. Fuzzy set representation of clusters makes this possible by assigning a membership value to all the vehicles and supports the formation of clusters based on this membership value. Since the cluster lifetime is very minimal in vehicular network, fuzzy based clustering is too descriptive to interpret the clustering results. In this paper, the rough set based fuzzy clustering is employed for formation of clusters in a VANET. Using this scheme, a vehicle in the transmission range of more than one cluster namely, the boundary vehicles are assigned with a membership value. Based on the fuzzy rule base, the vehicles are assigned to the appropriate cluster. Theoretical analysis and experimental results show that rough set based fuzzy scheme obtains 10 % to 20 % more average cluster lifetime and 20 % to 25 % more cluster head lifetime when compared to existing approaches.Index Terms-Clustering, fuzzy sets, rough sets, vehicular ad hoc network.

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A VANETs routing algorithm based on Euclidean distance clustering

Cited by 19 publications

References 9 publications

Joint Resource Allocation and UAV Scheduling With Ground Radio Station Sleeping

Joint Resource Allocation and UAV Scheduling With Ground Radio Station Sleeping

Utilization of nested clustering in VANET to reduce data loss in mobile cloud computing

Rough Set Based Fuzzy Scheme for Clustering and Cluster Head Selection in VANET

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