报告题目：Information Dissemination in Vehicular Networks in an Urban Hyperfractal Topology

主讲人：Bernard Mans

讲座时间：2019年10月29日（星期二）10：00-11:30

讲座地点：行政楼601会议室

报告人简介：

Professor Bernard Mans is currently the Executive Dean for the Faculty of Science and Engineering, at Macquarie University in Sydney.

In 2014-2017, he was member of the Australian Research Council (ARC) College of Experts (Engineering, Information and Computing Sciences).

Between 2014and 2016, he was the Associate Dean (Research) for the Faculty of Science and Engineering.

Between 2008 and 2014, he was Head of Department, for the Department of Computing (which he joined in 1997, after positions in Canada, Scotland and Queensland).

He received his Ph.D. degree in Computer Science in 1992 from the University Pierre and Marie Curie (UPMC), Paris, France (done with INRIA, the premier Computer Science national research centre in France).

In 2003, he was selected as the inaugural HITACHI-INRIA Chair with INRIA, France.

His research interests focus on algorithms and graphs for distributed and mobile computing, with an emphasis on wireless and socialnetworks.

报告摘要：

The goal of this report is to increase our understanding of the fundamental performance limits of urban vehicle networks by exploiting the self-similarity and hierarchical organization of modern cities. We use an innovative model called "hyperfractal" that captures the self-similarity of the topology and vehicle locations while avoiding the extremes of regularity and randomness. We use analytical tools to derive matching theoretical upper and lower bounds for the information propagation speed of a broadcast in an urban delay tolerant network which is disconnected at all time, i.e., where end-to-end multihop paths may not exist (requiring a store-carry-and-forward routing model). We prove that the average broadcast time in a hyperfractal setup is in $\Theta(n^{1-\delta})$ where $n$ is the number of mobile nodes and where $\delta$ depends on the precise hyperfractal dimension. Furthermore, we show that the performance is due in part to an interesting self-similar phenomenon, that we denote as {\em information teleportation}, that arises as a consequence of the topology and allows an acceleration of the broadcast time. We show how our model can be validated with real cities using a fitting procedure applied to open data sets, and also how it can be extended to cities that do not follow a regular hierarchical pattern. The study also presents simulations that confirm the validity of the bounds in multiple realistic settings, including scenarios with variable speed.

主办单位：人工智能学院