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Social Clustering of Vehicles Based on Semi-Markov Processes

机译：基于半马尔可夫过程的车辆社会聚类

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Vehicle clustering is a crucial network management task for vehicular networks to address the broadcast storm problem and to cope with the rapidly changing network topology. Developing algorithms that create is a very challenging procedure because of the highly dynamic moving patterns of vehicles and the dense topology. Previous approaches to vehicle clustering have been based on either topology-agnostic features, such as vehicle IDs or hard-to-set parameters, or have exploited very limited knowledge of vehicle trajectories. This paper develops a pair of algorithms, namely, and , the latter being a specialization of the former that exploits, for the first time in the relevant literature, the “social behavior” of vehicles, i.e., their tendency to share the same/similar routes. Both methods exploit the historic trajectories of vehicles gathered by roadside units located in each subnetwork of a city and use the recently introduced clustering primitive of . The mobility, i.e., mobile patterns of each vehicle, is modeled as semi-Markov processes. To assess the performance of the proposed clustering algorithms, we performed a detailed experimentation by simulation to compare its behavior with that of high-performance state-of-the-art algorithms, namely, the , , and protocols. The comparison involved the investigation of the impact of a range of parameters on the performance of the protocols, including vehicle speed and transmission range, as well as the existence and strength of social patterns, for both urban and highway-like environments. All of the received results attested to th- superiority of the proposed algorithms for creating stable and meaningful clusters.
机译：车辆群集是车辆网络解决广播风暴问题并应对快速变化的网络拓扑的一项至关重要的网络管理任务。由于车辆的高动态运动模式和密集的拓扑结构，开发创建算法非常具有挑战性。车辆聚类的先前方法已经基于与拓扑无关的特征（例如，车辆ID或难以设置的参数），或者已经利用了非常有限的车辆轨迹知识。本文开发了一对算法，即和，后者是前者的一种专业化，在相关文献中首次利用了车辆的“社会行为”，即，它们共享相同/相似的趋势。路线。两种方法都利用位于城市每个子网的路边单位收集的车辆的历史轨迹，并使用最近引入的聚类原语。移动性，即每辆车的移动模式，被建模为半马尔可夫过程。为了评估所提出的聚类算法的性能，我们通过仿真进行了详细的实验，以比较其行为与高性能，最新算法（即，和）的行为。比较包括调查一系列参数对协议性能的影响，包括车速和传输范围，以及城市和高速公路环境下社会模式的存在和强度。所有收到的结果都证明了所提出的用于创建稳定且有意义的集群的算法的优越性。

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