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。本研究的目标是开发一个集成的框架，用于无线传感器网络中的分布式推理的速率约束自适应量化技术。 该方法允许传感器节点顺序地传输其量化数据，并且每个节点可以基于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和分布式随机场估计方法。该项目解决了在传感器网络环境中用于分布式推断的量化的基本挑战，其中，由于最佳量化器依赖于与传感器网络所监视的随机事件相关联的未知参数，因此通常不能实现最佳量化器。 与传统方法使用固定的，数据独立的，通常是随机选择的量化器不同，本研究采用了数据驱动的方法，通过传感器合作和自适应学习，局部量化器顺序更新，以收敛到最优解。关于更广泛的影响，该项目有可能解决几个重要的分布式推理问题的带宽和功率约束，从而推进了预期具有重大经济和社会影响的无线传感器网络的研究和开发。 该项目有一个综合研究和教育方案，旨在培训各种学生，包括代表性不足的群体的学生。