Due to high node mobility, stability has been always one of the major concerns of vehicle clustering algorithms in vehicular ad hoc networks. In this paper, we propose a novel clustering algorithm based on the informa...Due to high node mobility, stability has been always one of the major concerns of vehicle clustering algorithms in vehicular ad hoc networks. In this paper, we propose a novel clustering algorithm based on the information of routes planned by vehicular navigation systems. In the clustering algorithm, we design a residual route time function to quantitatively calculate the overlapping time among vehicles based on route information, with which a novel clusterhead selection metric is presented. We further design a mechanism of future-clusterhead, which can help avoid message exchanges at intersections and reduce the overhead of cluster maintenance. The simulation results show that, compared with previous works, our clustering algorithm can achieve higher stability and at the same time lower communication cost.展开更多
基金partially supported by The National Key Research and Development Program of China(No.2016YFB0200404)National Natural Science Foundation of China(No.61501527,61379157,U1711263)+6 种基金MOE-CMCC Joint Research Fund of China(No.MCM20160104)State’s Key Project of Research and Development Plan(No.2016YFE01229003)the Fundamental Research Funds for the Central Universitiesthe Science,Technology and Innovation Commission of Shenzhen Municipality(JCYJ20160429170032960)Guangdong Science and Technology Project(No.2016B010126003)2016 Major Project of Collaborative Innovation in Guangzhou(No.201604046008)Program of Science and Technology of Guangdong(No.2015B010111001)
文摘Due to high node mobility, stability has been always one of the major concerns of vehicle clustering algorithms in vehicular ad hoc networks. In this paper, we propose a novel clustering algorithm based on the information of routes planned by vehicular navigation systems. In the clustering algorithm, we design a residual route time function to quantitatively calculate the overlapping time among vehicles based on route information, with which a novel clusterhead selection metric is presented. We further design a mechanism of future-clusterhead, which can help avoid message exchanges at intersections and reduce the overhead of cluster maintenance. The simulation results show that, compared with previous works, our clustering algorithm can achieve higher stability and at the same time lower communication cost.
