Moth Flame Clustering Algorithm for Internet of Vehicle (MFCA-IoV)

IEEE Access

卷:7

Yunyoung Nam¹  Muhammad Fahad Khan²  Muazzam Maqsood²  Farhan Aadil²  Syed Hashim Raza Bukhari³  Maqbool Hussain⁴ 

[1] Department of Computer Science and Engineering, Soonchunhyang University, Asan, South Korea;

[2] Department of Computer Science, COMSATS University Islamabad at Attock Campus, Attock, Pakistan;

[3] Department of Electrical Engineering, COMSATS University Islamabad at Attock Campus, Attock, Pakistan;

[4] Department of Software Engineering, Sejong University, Seoul, South Korea;

关键词: Internet of Vehicle (IoV);    vehicular ad-hoc networks (VANETs);    intelligent transportation system (ITS);    Ant-colony-optimization (ACO);    particle swarm optimization (PSO);    MFO;   

DOI  :  10.1109/ACCESS.2018.2886420

来源: DOAJ

【 摘 要 】

A network of wirelessly connected vehicles by using any mean of connectivity is termed as the Internet of Vehicle (IoV). Managing this type of network is a challenging task. Clustering is a technique to efficiently manage resources in this type of network. In a cluster, all inter/intra cluster communication is managed by a cluster head (CH). Load on each CH, the lifetime of the cluster and the total number of clusters in a network are some parameters to measure the efficiency of the network. In this paper, a novel technique based on moth flame clustering algorithm for IoV (MFCA-IoV) is proposed. Moth flame optimizer is a nature-inspired algorithm. MFCA-IoV generates optimized clusters for robust transmission and is evaluated experimentally with renowned techniques. These techniques are Grey-Wolf-optimization-based method used for the clustering called as GWOCNETs, multi-objective particle-swarm-optimization (MOPSO), clustering algorithm based on Ant colony optimization for vehicular ad-hoc networks termed as CACONET and comprehensive learning particle-swarm-optimization (CLPSO). To assess the comparative efficiency of these algorithms, numerous experiments are performed. The parameters like network grid-size, number of nodes, speed, direction, and transmission-range of the nodes are considered for optimized clustering. The results indicate, MFCA-IoV is showing 73% nodes, which are not selected as a cluster head while existing techniques are providing 57%, 50%, 51%, and 58% for GWOCNETs, CLPSO, MOPSO, and CACONET, respectively. Hence, lesser the nodes are selected as CH, the more optimal result will be considered.

【 授权许可】

Unknown