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。这些系统应该以最小的传感器之间的通信和最小的编码计算开销来满足它们的目的。特别是，这些效率可以显著提高电池寿命，而在植入人体的系统中，更换废电池是非常困难的。该项目将开发传感器网络系统中信号编码的原始方法。尽管分布式源编码原理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、高延迟以及对假设概率模型的准确性的敏感性。此外，可能没有节点具有内存和计算能力来进行睡眠狼解码。这些方法在实践中的失败留下了明显的技术差距。大多数传感器网络使用简单的均匀标量量化和压缩，不利用传感器间的相关性，或者根本没有压缩。该项目将使用高分辨率量化理论来开发一个框架，用于在网络中附近节点之间提供和利用量化的侧信息，目的是支持推理和计算任务。核心创新思想是允许编码器之间的有限（低速率，短距离）通信以实现适应。创新的第二个关键领域是对信息获取系统的关注，这些系统的设计目的是进行计算，而不是使每个测量值能够重现。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，并且它将测量分布中的不确定性的鲁棒性带到最前沿。

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.