Emmanuel Siman, Isnin Ismail Fauzi Bin, Mohamad Mohd Murtadha Bin
Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia.
Sensors (Basel). 2022 Jun 27;22(13):4861. doi: 10.3390/s22134861.
The vehicular ad hoc network (VANET) is a potential technology for intelligent transportation systems (ITS) that aims to improve safety by allowing vehicles to communicate quickly and reliably. The rates of merging collision and hidden terminal problems, as well as the problems of picking the best match cluster head (CH) in a merged cluster, may emerge when two or more clusters are merged in the design of a clustering and cluster management scheme. In this paper, we propose an enhanced cluster-based multi-access channel protocol (ECMA) for high-throughput and effective access channel transmissions while minimizing access delay and preventing collisions during cluster merging. We devised an aperiodic and acceptable merge cluster head selection (MCHS) algorithm for selecting the optimal merge cluster head (MCH) in centralized clusters where all nodes are one-hop nodes during the merging window. We also applied a weighted Markov chain mathematical model to improve accuracy while lowering ECMA channel data access transmission delay during the cluster merger window. We presented extensive simulation data to demonstrate the superiority of the suggested approach over existing state-of-the-arts. The implementation of a MCHS algorithm and a weight chain Markov model reveal that ECMA is distinct and more efficient by 64.20-69.49% in terms of average network throughput, end-to-end delay, and access transmission probability.
车载自组织网络(VANET)是智能交通系统(ITS)的一项潜在技术,旨在通过让车辆快速可靠地通信来提高安全性。在聚类和集群管理方案的设计中,当两个或更多集群合并时,可能会出现合并冲突率、隐藏终端问题,以及在合并集群中挑选最佳匹配簇头(CH)的问题。在本文中,我们提出了一种增强型基于集群的多址信道协议(ECMA),以实现高吞吐量和有效的接入信道传输,同时在集群合并期间最小化接入延迟并防止冲突。我们设计了一种非周期性且可接受的合并簇头选择(MCHS)算法,用于在合并窗口期间所有节点均为一跳节点的集中式集群中选择最优合并簇头(MCH)。我们还应用了加权马尔可夫链数学模型,以提高准确性,同时降低集群合并窗口期间ECMA信道数据接入传输延迟。我们展示了大量仿真数据,以证明所提方法优于现有最先进技术。MCHS算法和权重链马尔可夫模型的实现表明,ECMA在平均网络吞吐量、端到端延迟和接入传输概率方面具有独特性且效率提高了64.20 - 69.49%。