CIF: Small: Quantization for Acquisition and Computation Networks

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最小的通信和最小的编码计算开销来服务于它们的目的。特别是，这些效率可以显著提高电池寿命，在植入人体的系统中，更换废电池非常困难。这个项目将开发传感器网络系统中信号编码的原创方法。虽然分布式信源编码原则似乎很适合传感器网络，但它们很少在这些系统中使用。原因包括高复杂性、高延迟和对假设的概率模型的准确性的敏感性。此外，可能没有节点具有执行Slepian-Wolf解码所需的内存和计算能力。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，或者根本不使用压缩。这个项目将使用高分辨率量化理论来开发一个框架来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。其核心创新思想是允许编码者之间进行有限的(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获取系统，该系统旨在进行计算，而不是能够复制每个测量值。对采集和计算的关注--而不是通信--与部署传感器网络的实际动机是一致的，它将对测量分布中的不确定性的稳健性带到了最前面。

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.