Analyzing vehicular ad hoc networks (VANETs) poses a considerable challenge due to their constantly changing network topology and scarce network resources. Furthermore, defining suitable routing metrics for adaptive algorithms is a particularly hard task since these adaptive decisions should be taken according to the current conditions of the VANET. The literature contains different approaches aimed at optimizing the usage of wireless network resources. In a previous study, we introduced an analytical model based on a straightforward Markov reward chain (MRC) to capture transient measurements of the idle time of the link formed between two VANET nodes, which we denote as . This current study focuses on modeling and analyzing the influence of on adaptive decision mechanisms. Leveraging our MRC models, we have derived a concise equation to compute . This equation provides a quick evaluation of , facilitating quick adaptive routing decisions that align with the current VANET conditions. We have integrated our evaluation into multihop routing protocols. We specifically compare performance results of the 3MRP protocol with an enhanced version, I3MRP, which incorporates our metric. Simulation results demonstrate that integrating as a decision metric in the routing protocol enhances the performance of VANETs in terms of packet losses, packet delay, and throughput. The findings consistently indicate that I3MRP outperforms 3MRP by up to 50% in various scenarios across high, medium, and low vehicular densities.

中文翻译：

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基于马尔可夫奖励的车辆网络空闲时间估计以改进多度量路由协议


由于车辆自组织网络 (VANET) 不断变化的网络拓扑和稀缺的网络资源，分析车辆自组织网络 (VANET) 提出了相当大的挑战。此外，为自适应算法定义合适的路由度量是一项特别困难的任务，因为这些自适应决策应根据 VANET 的当前条件做出。文献包含旨在优化无线网络资源使用的不同方法。在之前的研究中，我们引入了一种基于简单马尔可夫奖励链 (MRC) 的分析模型，用于捕获两个 VANET 节点之间形成的链路空闲时间的瞬态测量，我们将其表示为 。当前的研究重点是建模和分析对自适应决策机制的影响。利用我们的 MRC 模型，我们导出了一个简洁的方程来计算 。该方程提供了 的快速评估，促进与当前 VANET 条件相一致的快速自适应路由决策。我们已将我们的评估集成到多跳路由协议中。我们专门将 3MRP 协议与增强版本 I3MRP 的性能结果进行比较，后者包含了我们的指标。仿真结果表明，作为决策指标集成到路由协议中可以增强 VANET 在数据包丢失、数据包延迟和吞吐量方面的性能。研究结果一致表明，在高、中、低车辆密度的各种场景中，I3MRP 的性能优于 3MRP 高达 50%。