TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
An Efficient Optimization Technique for Node Clustering in VANETs Using Gray Wolf Optimization |
| 영문제목(English Title) |
An Efficient Optimization Technique for Node Clustering in VANETs Using Gray Wolf Optimization |
| 저자(Author) |
Muhammad Fahad Khan
Farhan Aadil
Muazzam Maqsood
Salabat Khan
Bilal Haider Bukhari
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| 원문수록처(Citation) |
VOL 12 NO. 09 PP. 4228 ~ 4247 (2018. 09) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Many methods have been developed for the vehicles to create clusters in vehicular ad hoc networks (VANETs). Usually, nodes are vehicles in the VANETs, and they are dynamic in nature. Clusters of vehicles are made for making the communication between the network nodes. Cluster Heads (CHs) are selected in each cluster for managing the whole cluster. This CH maintains the communication in the same cluster and with outside the other cluster. The lifetime of the cluster should be longer for increasing the performance of the network. Meanwhile, lesser the CH's in the network also lead to efficient communication in the VANETs. In this paper, a novel algorithm for clustering which is based on the social behavior of Gray Wolf Optimization (GWO) for VANET named as Intelligent Clustering using Gray Wolf Optimization (ICGWO) is proposed. This clustering based algorithm provides the optimized solution for smooth and robust communication in the VANETs. The key parameters of proposed algorithm are grid size, load balance factor (LBF), the speed of the nodes, directions and transmission range. The ICGWO is compared with the well-known meta-heuristics, Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO) for clustering in VANETs. Experiments are performed by varying the key parameters of the ICGWO, for measuring the effectiveness of the proposed algorithm. These parameters include grid sizes, transmission ranges, and a number of nodes. The effectiveness of the proposed algorithm is evaluated in terms of optimization of number of cluster with respect to transmission range, grid size and number of nodes. ICGWO selects the 10% of the nodes as CHs where as CLPSO and MOPSO selects the 13% and 14% respectively.
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| 키워드(Keyword) |
VANETs
LBF
Optimization
Intelligent Transportation Systems and clustering
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| 파일첨부 |
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