Data-Driven Adaptive Quantization for Distributed Inference

用于分布式推理的数据驱动自适应量化

• 批准号: 0901066
• 负责人: Hongbin Li
• 金额: $ 35万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0901066&HistoricalAwards=false
• 关键词: Data; Driven; Adaptive; Quantization; Distributed

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).The objective of this research is to develop an integrated framework for rate-constrained adaptive quantization techniques with application to distributed inference in wireless sensor networks. The approach allows sensor nodes to sequentially transmit their quantized data and each individual node can adaptively change its local quantizer based on prior transmissions from other nodes. Specific goals include development of linear and nonlinear adaptive quantization schemes and distributed inference methods, such as distributed estimators and detectors, distributed consensus algorithms with quantized message passing, and distributed random field estimation methods, by exploiting adaptive quantization, graphical models and distributed optimization techniques.With respect to intellectual merit, the project addresses a fundamental challenge of quantization for distributed inference in a sensor network environment, where the optimum quantizer generally cannot be implemented due to its dependence on unknown parameters associated with the random event being monitored by the sensor network. Unlike conventional methods using fixed, data-independent and often heuristically selected quantizers, this research takes a data-driven approach where, through sensor cooperation and adaptive learning, the local quantizers are sequentially updated so as to converge to an optimum solution.With respect to broader impact, the project has the potential of solving several important distributed inference problems with bandwidth and power constraints, thereby advancing the research and development of wireless sensor networks that are expected to have significant economic and social impact. The project has an integrated research and education program aimed at the training of a diverse population of students, including those from underrepresented groups.

该奖项根据 2009 年美国复苏和再投资法案（公法 111-5）提供资助。本研究的目的是开发速率受限自适应量化技术的集成框架，并将其应用于无线传感器网络中的分布式推理。 该方法允许传感器节点顺序传输其量化数据，并且每个单独的节点可以根据来自其他节点的先前传输自适应地改变其本地量化器。具体目标包括通过利用自适应量化、图形模型和分布式优化技术，开发线性和非线性自适应量化方案和分布式推理方法，例如分布式估计器和检测器、具有量化消息传递的分布式一致性算法以及分布式随机场估计方法。在智力价值方面，该项目解决了传感器网络中分布式推理的量化的基本挑战 在这种环境中，最佳量化器通常无法实现，因为它依赖于与传感器网络监视的随机事件相关的未知参数。 与使用固定、数据无关且通常启发式选择的量化器的传统方法不同，该研究采用数据驱动的方法，通过传感器协作和自适应学习，顺序更新局部量化器以收敛到最佳解决方案。就更广泛的影响而言，该项目有潜力解决几个具有带宽和功率限制的重要分布式推理问题，从而推动无线传感器网络的研究和开发 预计将产生重大的经济和社会影响。 该项目有一个综合研究和教育计划，旨在培训不同群体的学生，包括来自代表性不足群体的学生。