Data-Driven Adaptive Quantization for Distributed Inference

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。本研究的目的是开发一个集成框架，用于速率约束自适应量化技术，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量化数据，并且每个节点可以根据其他节点的先前传输自适应地改变其局部量化器。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，如分布式估计器和检测器、具有量化消息传递的分布式共识算法和分布式随机场估计方法。就智力价值而言，该项目解决了传感器网络环境中分布式推理量化的基本挑战，其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测的随机事件相关的未知参数。与使用固定的、数据独立的、通常是启发式选择量化器的传统方法不同，本研究采用数据驱动的方法，通过传感器合作和自适应学习，顺序更新局部量化器，从而收敛到最优解。就更广泛的影响而言，该项目具有解决带宽和功率限制的几个重要分布式推理问题的潜力，从而推进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。该项目有一个综合的研究和教育计划，旨在培训不同的学生群体，包括来自代表性不足群体的学生。

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.