On the Interdependence of Routing and Data Compression in Multi-Hop Sensor Networks
On the Interdependence of Routing and Data Compression in Multi-Hop Sensor Networks
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DOI：
10.1145/570645.570663
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发表时间：
2002-09
影响因子：
3
通讯作者：
A. Scaglione;S. Servetto
中科院分区：
文献类型：
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作者：
A. Scaglione;S. Servetto
摘要我们考虑了传感器网络中的广播通信问题，在该问题中，在每个节点处收集随机场的样本，并且目标是使所有节点在规定的失真值内获得整个场的估计。我们在本文中探索的主要思想是，联合压缩由不同的节点产生的数据，因为这个信息在多跳传播，以消除相关性的采样字段的表示。我们的主要贡献是：（a）我们使用简单的网络流概念获得关于随机场的速率/失真函数的条件，以保证任何节点都可以获得在网络中的每个其他节点处收集的测量，量化到任何规定的失真值内;以及（B）我们为传感器数据构建了一大类物理激励的随机模型，为此，我们能够证明由整个网络生成的所有数据的联合速率/失真函数比（a）中找到的界限增长得更慢。我们工作的一个真正新颖的方面是路由和源代码之间的紧密耦合，明确制定了一个简单的和易于分析的模型-据我们所知，这种连接之前没有研究过。
Abstract We consider a problem of broadcast communication in sensor networks, in which samples of a random field are collected at each node, and the goal is for all nodes to obtain an estimate of the entire field within a prescribed distortion value. The main idea we explore in this paper is that of jointly compressing the data generated by different nodes as this information travels over multiple hops, to eliminate correlations in the representation of the sampled field. Our main contributions are: (a) we obtain, using simple network flow concepts, conditions on the rate/distortion function of the random field, so as to guarantee that any node can obtain the measurements collected at every other node in the network, quantized to within any prescribed distortion value; and (b) we construct a large class of physically-motivated stochastic models for sensor data, for which we are able to prove that the joint rate/distortion function of all the data generated by the whole network grows slower than the bounds found in (a). A truly novel aspect of our work is the tight coupling between routing and source coding, explicitly formulated in a simple and analytically tractable model – to the best of our knowledge, this connection had not been studied before.