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首页> 外文期刊>IEEE Transactions on Information Theory >Graph Codes for Distributed Instant Message Collection in an Arbitrary Noisy Broadcast Network
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Graph Codes for Distributed Instant Message Collection in an Arbitrary Noisy Broadcast Network

机译：任意噪声广播网络中分布式即时消息收集的图形代码

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摘要

We consider the problem of minimizing the number of broadcasts for collecting all sensor measurements at a sink node in a noisy broadcast sensor network. Focusing first on arbitrary network topologies, we provide: 1) fundamental limits on the required number of broadcasts of data gathering and 2) a general in-network computing strategy to achieve an upper bound within factor of the fundamental limits, where is the number of agents in the network. Next, focusing on two example networks, namely, arbitrary geometric networks and random Erdös-Rényi networks, we provide improved in-network computing schemes that are optimal in that they attain the fundamental limits, i.e., the lower and upper bounds are tight in scaling sense. Our main techniques are three distributed encoding techniques, called graph codes, which are designed, respectively, for the above-mentioned three scenarios. Our work, thus, extends and unifies previous works such as those of Gallager and Karamchandani on the number of broadcasts for distributed function computation in special network topologies, while bringing in novel techniques, e.g., from error-control coding and noisy circuits, for both upper and lower bounds.
机译：我们考虑使在嘈杂的广播传感器网络中的汇聚节点上收集所有传感器测量值的广播数量最小化的问题。首先关注于任意网络拓扑，我们提供：1）所需的数据收集广播数量的基本限制，以及2）达到基本限制因素上限的一般网络内计算策略，其中的数量是网络中的代理商。接下来，针对两个示例网络，即任意几何网络和随机Erdös-Rényi网络，我们提供了改进的网络内计算方案，这些方案在达到基本限制（即上下限紧缩性）方面是最佳的感。我们的主要技术是分别针对上述三种情况设计的三种分布式编码技术，称为图形代码。因此，我们的工作扩展并统一了诸如Gallager和Karamchandani之类的先前工作，涉及特殊网络拓扑中用于分布式函数计算的广播数量，同时为这两种方法引入了错误控制编码和噪声电路等新技术。上下限。

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