CAREER: Scaling Laws and Measure-Matching in Sensor Networks

职业：传感器网络中的缩放定律和测量匹配

• 批准号: 0347298
• 负责人: Michael Gastpar
• 金额: $ 40万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-15 至 2010-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0347298&HistoricalAwards=false
• 关键词: CAREER; Scaling; Laws; Measure; Matching

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.

传感器网络给通信和信息处理的理解带来了新的挑战。这项研究解决了两个关键问题：网络的扩展行为，以及传感器网络同时涉及源和通道，因此通常执行数据压缩和数据传输的事实。网络的“扩展行为”表示其关键属性以及作为节点数量函数的特征。对于传感器网络等低成本、低功耗和密集的场景，这是最相关的表征，并且它在理论上是重要且成功的，因为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，缩放法则侧重于性能如何取决于节点数量。研究人员的初步缩放定律结果表明，与小型网络相比，大型网络适用的定律和见解有质的不同。这对于研究和教育都至关重要：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此类网络。这项研究的第二个关键问题涉及以下事实感知的世界通常是模拟的，需要数据压缩和数据传输。迄今为止，这一通信问题尚未得到解决：将编码策略普遍分离为压缩和传输阶段可能会导致性能显着低于最佳状态，这意味着需要修改对信息“位”的既定理解。这项研究调查了对沟通的一种新的、不同的理解，即“匹配”源与渠道。从标度律的意义上来说，这可以带来惊人的收益，正如初步结果所显示的那样。