Vehicular Ad Hoc Network (VANET) has received better consideration from academic researcher and automobile companies in modern years. Meanwhile, Congestion control remains the major area of research in VANET due to its characteristics such as high mobility, high vehicle density, and unbounded network size, frequent exchange of information, rapidly changing network topology, and lack of central management. Congestion in the VANET broadcast channel may occur, because of number of vehicles in a relatively tiny area raises, the communications channel becomes exceed by beacon message, and event-driven messages. Researchers have suggested a number of answers to control these issues and also to reduce congestion in VANET atmosphere. Congestion in VANET occurs when the vehicles are in the dense part while the network node is carrying more data than it can handle. More vehicles in cluster area are attempting to broadcast simultaneously in dense condition, it will reduce Packet Delivery Ratio and thus congestion will occur. We proposed density based dynamic clustering (DBDC) which finds the high dense region in the cluster and do sub-clustering. First, we find Average Vehicle Density Threshold Value using Received Signal Strength (RSS) to determine the high dense part. Performance result shows our approach is better in all aspects comparing with VWCA clustering approach and also provides high cluster stability.

近年来，车载自组织网络（VANET）受到了学术研究人员和汽车公司的更好考虑。同时，由于拥塞控制具有高移动性，高车辆密度，无限制的网络规模，频繁的信息交换，快速变化的网络拓扑以及缺乏集中管理等特点，因此仍然是VANET研究的主要领域。VANET广播通道中可能会出现拥塞，因为相对较小区域中的车辆数量增加，通信通道变得超过信标消息和事件驱动消息。研究人员提出了许多控制这些问题并减少VANET大气拥塞的答案。当车辆处于密集区域，而网络节点承载的数据超出其处理能力时，VANET就会发生拥塞。群集区域中有更多的车辆试图在密集条件下同时广播，这将降低数据包传输率，从而导致拥塞。我们提出了基于密度的动态聚类（DBDC），它可以找到聚类中的高密度区域并进行子聚类。首先，我们使用接收信号强度（RSS）确定平均车辆密度阈值，以确定高密度部分。性能结果表明，与VWCA群集方法相比，我们的方法在各个方面都更好，并且群集稳定性更高。我们提出了基于密度的动态聚类（DBDC），它可以找到聚类中的高密度区域并进行子聚类。首先，我们使用接收信号强度（RSS）确定平均车辆密度阈值，以确定高密度部分。性能结果表明，与VWCA群集方法相比，我们的方法在各个方面都更好，并且群集稳定性更高。我们提出了基于密度的动态聚类（DBDC），它可以找到聚类中的高密度区域并进行子聚类。首先，我们使用接收信号强度（RSS）确定平均车辆密度阈值，以确定高密度部分。性能结果表明，与VWCA群集方法相比，我们的方法在各个方面都更好，并且群集稳定性更高。