CIF: Small: Quantization for Acquisition and Computation Networks

CIF：小：采集和计算网络的量化

• 批准号: 1441917
• 负责人: Vivek Goyal
• 金额: $ 15.68万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1441917&HistoricalAwards=false
• 关键词: CIF; Small; Quantization; Acquisition; Computation

Networks of sensors are increasingly important in a variety of applications including national security, environmental monitoring, and health care. These systems should serve their purposes with minimal communication between sensors and minimal computation overhead from coding. In particular, these efficiencies can dramatically improve battery life, and in systems such as those implanted in a body, replacing spent batteries is very difficultThis project will develop original approaches to the signal coding in sensor network systems. Though distributed source coding principles seem to be a natural fit for sensor networks, they are rarely used in these systems. Reasons include high complexity, high delay, and sensitivity to the accuracy of the assumed probabilistic models. Also, there may be no node with the memory and computing power to do Slepian-Wolf decoding. The failure of these methods in practice has left a glaring technological gap. Most sensor networks use simple uniform scalar quantization and compression that does not exploit inter-sensor correlation, or no compression at all. This project will use high-resolution quantization theory to develop a framework for providing and exploiting quantized side information among nearby nodes in a network, with the aim of supporting inference and computation tasks.The central innovative idea is to allow limited (low-rate, short-range) communication among encoders to enable adaptation. A second key area of innovation is a focus on information acquisition systems that are designed to make a computation rather than enable reproduction of every measured value. The focus on acquisition and computation -- as opposed to communication -- is consistent with the actual motivation for deploying sensor networks, and it brings robustness to uncertainty in measurement distributions to the forefront.

传感器网络在包括国家安全、环境监测和医疗保健在内的各种应用中越来越重要。这些系统应该以传感器之间的最小通信和编码的最小计算开销来达到其目的。特别是，这些效率可以显着提高电池寿命，并且在诸如植入体内的系统中，更换废电池非常困难。该项目将开发传感器网络系统中信号编码的原始方法。虽然分布式信源编码原则似乎是一个天然的适合传感器网络，他们很少在这些系统中使用。原因包括高复杂性、高延迟以及对假设概率模型的准确性的敏感性。此外，可能没有具有进行Slepian-Wolf解码的存储器和计算能力的节点。这些方法在实践中的失败留下了一个明显的技术差距。大多数传感器网络使用简单的统一标量量化和压缩，不利用传感器间的相关性，或根本没有压缩。该项目将使用高分辨率量化理论来开发一个框架，用于提供和利用网络中邻近节点之间的量化边信息，目的是支持推理和计算任务。中心创新思想是允许编码器之间的有限（低速率，短距离）通信，以实现自适应。创新的第二个关键领域是专注于信息采集系统，这些系统旨在进行计算，而不是再现每个测量值。对采集和计算（而不是通信）的关注与部署传感器网络的实际动机是一致的，并且它将测量分布中的不确定性的鲁棒性带到了最前沿。