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Distributed connected dominating set election from uniform random to power law network graphs

机译:从统一随机到幂律网络图的分布式连通支配集选择

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This work describes an analysis of applying different election heuristics to connected dominated set (CDS) algorithms within different topological graph taxonomies. Our initial motivation for this study was a general observation that a significant amount of research in mobile ad hoc network (manet) algorithm design and related election heuristic evaluation is based upon geometric random or other types of uniform random network models. Our hypothesis is that small world or scale-free network topology analysis can result in significantly different design feedback that may be suppressed by exclusive use of uniform random analysis. Previous research has shown that power law based networks more accurately represent types of real world and social networks and we believe it is important to understand the potential tradeoffs in distributed algorithm design for self-organizing networks beyond traditional random network models. We provide a concrete example by applying the existing specification for the essential-CDS algorithm used within the mobile ad hoc network (manet) Simplified Multicast Forwarding (SMF) and the OSPF Extensions for MANET MDR approach. We model random, small world, and power law based network types based upon well known models. We demonstrate that the network graph taxonomy used in modeling affects the design analysis such as understanding the resulting effectiveness of various distributed election heuristics. Future work is planned to extend these initial results to more dynamic analysis.
机译:这项工作描述了将不同的选举启发式方法应用于不同拓扑图分类法中的连接支配集(CDS)算法的分析。我们进行这项研究的最初动机是普遍观察到,对移动自组织网络(manet)算法设计和相关选举启发式评估的大量研究都是基于几何随机或其他类型的统一随机网络模型。我们的假设是,小世界或无标度的网络拓扑分析可能会导致明显不同的设计反馈,而这些反馈可能被统一随机分析的独占使用所抑制。先前的研究表明,基于幂律的网络可以更准确地表示现实世界和社交网络的类型,我们认为了解自组织网络的分布式算法设计中除传统随机网络模型之外的潜在折衷至关重要。我们通过应用现有规范来说明在移动自组织网络(路由),简化组播转发(SMF)和OSPF扩展用于MANET MDR方法中使用的Essential-CDS算法的具体示例。我们基于众所周知的模型对基于随机,小世界和幂律的网络类型进行建模。我们证明了建模中使用的网络图分类法会影响设计分析,例如了解各种分布式选举启发法的结果有效性。计划未来的工作以将这些初始结果扩展到更动态的分析中。

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