贪婪的八卦与Escherichia dropping
Greedy Gossip with Eavesdropping
摘要：
针对平均一致性问题的分布式计算，提出了一种新的随机化Gossip算法--带窃听的贪婪Gossip算法。在流言算法中，网络中的节点随机地与它们的邻居通信，并迭代地交换信息。算法简单，分散，使他们有吸引力的无线网络应用程序。一般来说，流言算法对不可靠的无线条件和时变的网络拓扑是鲁棒的。在本文中，我们介绍了GGE，并证明了贪婪的更新导致快速收敛。我们不要求节点有任何位置信息。相反，通过利用无线通信的广播性质，贪婪更新成为可能。在GGE的操作过程中，当一个节点决定八卦时，它不是随机选择一个邻居，而是做出贪婪的选择，选择与自己的值最不同的节点。为了做出这种选择，节点需要知道它们邻居的值。因此，我们假设所有的传输都是无线广播，节点通过窃听它们的通信来跟踪它们邻居的值。我们表明，GGE的收敛性是保证连接的网络拓扑结构。我们还研究了收敛速度，并说明，通过理论界和数值模拟，GGE始终优于随机八卦，并执行中等大小的随机几何图形拓扑的地理八卦。
英文摘要：
This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average consensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.
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