On the Probability of Unsafe Disagreement in Group Formation Algorithms for Vehicular Ad Hoc Networks

Negin Fathollahnejad, R. Pathan, J. Karlsson
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引用次数: 5

Abstract

We address the problem of group formation in automotive cooperative applications using wireless vehicle-to-vehicle communication. Group formation (GF) is an essential step in bootstrapping self-organizing distributed applications such as virtual traffic lights. We propose a synchronous GF algorithm and investigate its behaviour in the presence of an unbounded number of asymmetric communication failures (receive omissions). Given that GF is an agreement problem, we know from previous research that it is impossible to design a GF algorithm that can guarantee agreement on the group membership in the presence of an unbounded number of messages losses. Thus, under this assumption, disagreement is an unavoidable outcome of a GF algorithm. We consider two types of disagreement(failure modes): safe and unsafe disagreement. To reduce the probability of unsafe disagreement, our algorithm uses a local oracle to estimate the number of nodes that are attempting to participate in the GF process. (Such estimates can be provided by roadside sensors or local sensors in a vehicle such as cameras.)For the proposed algorithm, we show how the probability of unsafe and safe disagreement varies for different system settings as a function of the probability of message loss. We also show how these probabilities vary depending on the correctness of the local oracles. More specifically, our results show that unsafe disagreement occurs only if the local oracles underestimates the number of participating nodes.
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车载Ad Hoc网络群形成算法中的不安全不一致概率研究
我们使用无线车对车通信解决了汽车协同应用中的组形成问题。群形成(GF)是自组织分布式应用(如虚拟交通灯)的一个重要步骤。我们提出了一种同步GF算法,并研究了它在无限数量的非对称通信失败(接收遗漏)存在下的行为。考虑到GF是一个协议问题,我们从以往的研究中知道,不可能设计出一个GF算法,在消息丢失无限大的情况下保证组成员的一致性。因此,在这种假设下,分歧是GF算法不可避免的结果。我们考虑两种类型的分歧(失效模式):安全和不安全分歧。为了减少不安全分歧的概率,我们的算法使用一个本地oracle来估计试图参与GF过程的节点数量。(这种估计可以由路边传感器或车辆上的本地传感器(如摄像头)提供。)对于所提出的算法,我们展示了不同系统设置下不安全和安全不一致的概率如何作为消息丢失概率的函数而变化。我们还展示了这些概率如何根据本地预言的正确性而变化。更具体地说,我们的结果表明,只有当本地预言机低估了参与节点的数量时,才会发生不安全的分歧。
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