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TTN: A time-to-network approach to data reporting in mobile ad hoc networks

机译:TTN:移动临时网络中数据报告的数据报告的时间方法

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For many applications in mobile wireless ad hoc networks (MANETs), forming an end-to-end data path is not always necessary; instead, the primary routing goal is often data collection or dissemination where only a data source is known. Routing algorithms must be carefully chosen in order to suit the needs of applications employing them. Our focus is on data collection applications in MANETs where limited mobility information is required to route data in a scalable manner. To address this goal, we employ the concepts of delay-tolerant networking (DTN) in which data makes progress toward a destination with high latency expectations and little knowledge of routing topology. Specifically, we present time-to-network (TTN) forwarding, a method of forwarding data generated by mobile nodes to a network endpoint in such a way that delivery latency is lowered without high networking cost. By segmenting mobility patterns into trips, we are able to apply TTN to a vehicular network using only an estimated destination arrival time for each vehicle. We evaluate TTN using mobility data from the TRANSIMS simulator for a real road network. Results show that our algorithm produces collection-to-network latencies similar to more generic algorithms but at a lower cost and with higher efficiency. Furthermore, we establish a lower bound for delivery latency in our experiments and compare it to TTN. This also helps normalize the interpretation of results specific to our mobility model.
机译:对于移动无线ad hoc网络(MANET)中的许多应用,形成端到端的数据路径并不总是必要的;相反,主路由目标通常是数据收集或传播,其中仅在数据源是已知的。必须仔细选择路由算法,以满足所用应用程序的需求。我们的重点是在船只中的数据收集应用程序,其中需要有限的移动信息以可扩展的方式路由数据。为了解决这一目标,我们采用了延迟容忍网络(DTN)的概念,其中数据在具有高延迟期望的目的地和对路由拓扑知之甚少的知识中取得进展。具体地,我们提供时间 - 网络时间(TTN)转发,一种将移动节点生成的数据转发到网络端点的方法,这种方式使得输送延迟没有高网络成本。通过将移动性模式分段为次级,我们能够仅使用每个车辆的估计目的地到达时间将TTN应用于车辆网络。我们使用来自Transims Simulator的移动性数据来评估TTN,用于真正的道路网络。结果表明,我们的算法产生了类似于更多通用算法的集合到网络延迟,但成本较低,效率更高。此外,我们在我们的实验中建立了递送延迟的下限,并将其与TTN进行比较。这也有助于使对我们移动模型特异的结果的解释进行规范化。

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