CAREER: Scaling Laws and Measure-Matching in Sensor Networks

传感器网络对通信和信息处理的理解提出了新的挑战。这项研究解决了两个关键问题：网络的伸缩行为，以及传感器网络既涉及信源又涉及信道的事实，因此通常既执行数据压缩又执行数据传输。网络的伸缩行为表示其关键属性和特征作为节点数量的函数。对于传感器网络等低成本、低功耗、密集的场景，这是最相关的表征，也是理论上重要和成功的，因为准确地分析网络性能似乎是一个非常困难的问题。相比之下，伸缩律关注的是性能如何依赖于节点数量。研究人员的初步标度律结果表明，与小型网络相比，大型网络适用的定律和见解有质的不同。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。这项研究的第二个关键问题涉及到这样一个事实，即感知的世界通常是模拟的，需要数据压缩和数据传输。这个通信问题到目前为止还没有解决：编码策略无处不在地分为压缩和传输阶段，这可能导致性能大大低于最佳，这意味着需要修改对信息的良好理解，即信息是“比特”。这项研究探索了一种新的、不同的对沟通的理解，即将来源与渠道“匹配”。正如初步结果所显示的，从标度律的意义上讲，这可能会带来惊人的收益。

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.