CAREER: Scaling Laws and Measure-Matching in Sensor Networks

传感器网络在理解通信和信息处理方面带来了新的挑战。本研究解决了两个关键问题：网络的缩放行为，以及传感器网络涉及源和通道的事实，因此通常同时执行数据压缩和数据传输。网络的“缩放行为”表示其关键属性和特征是节点数量的函数。对于低成本、低功耗和密集的场景，如传感器网络，这是最相关的表征，并且在理论上是重要和成功的，因为对网络性能的精确分析似乎是一个非常困难的问题。相比之下，缩放定律关注的是性能如何依赖于节点数量。研究人员的初步缩放定律结果表明，与小型网络相比，大型网络适用于定性不同的定律和见解。这对研究和教育都是至关重要的：必须开发不同的代码结构，算法设计和信号处理技术，并且必须教授不同的直觉来覆盖这些网络。本研究的第二个关键问题是，感知世界通常是模拟的，需要数据压缩和数据传输。到目前为止，这个通信问题还没有得到解决：将编码策略分离为压缩和传输阶段的普遍做法可能导致性能显著低于最优，这意味着需要修改将信息视为“位”的既定理解。本研究探讨了一种新的、不同的对传播的理解，即源与渠道的“匹配”。正如初步结果所显示的那样，从比例定律的角度来看，这可以带来惊人的收益。

Sensor networks create new challenges in the understanding of communication and information processing. This research addresses two of the key issues: the scaling behavior of the networks, and the fact that sensor networks involve both sources and channels and hence generally perform both data compression and data transmission.The "scaling behavior" of the network denotes its key properties and characteristics as a function of the number of nodes. For low-cost, low-power, and dense scenarios such as sensor networks, this is the mostrelevant characterization, and it is theoretically important and successful since the exact analysis of network performance appears to be a very hard problem. By contrast, a scaling law focuses on how the performance depends on the number of nodes. Preliminary scaling-law results of the investigators suggest that qualitatively different laws and insights apply to large networks as compared to small networks. This is of fundamental importance to both research and education: different code constructions, algorithm designs, and signal processing techniques have to be developed, and a different intuition has to be taught to cover such networks.The second key issue of this research concerns the fact that the sensed world is often analog, requiring both data compression and data transmission. This communication problem is unsolved to date: The omnipresent separation of the coding strategy into a compression and a transmission stage can lead to dramatically suboptimal performance, implying that the well-established understanding of information as "bits" needs to be revised. This research investigates a new and different understanding of communication as "matching" the source to the channel. In a scaling-law sense, this can lead to spectacular gains, as preliminary results have shown.