CAREER: New Architectures for Distributed Compression and Communication of Delay-Sensitive Information in Sensor Networks

职业：传感器网络中延迟敏感信息的分布式压缩和通信的新架构

• 批准号: 0448115
• 负责人: Sandeep Sadanandarao
• 金额: $ 40万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-01 至 2011-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0448115&HistoricalAwards=false
• 关键词: CAREER; New; Architectures; Distributed; Compression

In sensor networks comprised of energy- and complexity-limited inexpensive sensors, the underlying information field is distributed, endowed with certain dynamics in terms of physical movement (for example: seismic, acoustic waves traveling over a geographical region) and delay-sensitive that warrant real-time reconstruction. Further, in such multi-terminal settings, ``bit'' is not the universal currency of information, as the separation of multiuser compression and communication is sadly suboptimal. In thisproject these two issues are addressed by considering a new dynamic compression model and a new multi-partite graph-based architecture for distributed transmission of delay-sensitive information.In this new compression model the information available at certain nodes is changing as a function of time. This model is referred to as source coding with feedforward. This induces a completely new dynamism inthe information compression problem. Further, in the architecture for transmission of distributed information considered in this project, multi-partite graphs are used as a discrete interface between distributed compression and distributed communication. This leads to a modular design of multi-terminal sensor-based information processing systems. Using these mechanisms, the goal of this project is to realize a self-organizing sensor network enabled with a new architecture that is capable of performing more efficient representation, communication and real-time reconstruction of distributed and delay-sensitive complex information fields than possible by any system today. This research effort is complemented by an educational effort to train young engineers to become skilled in construction, management and development of such massively complex information systems. The specific issues considered in this project are development of project work in the courses, integrated undergraduate education and research effort, and curriculum development with a course on distributed signal processing and communication.

在由能量和复杂性限制的廉价传感器组成的传感器网络中，分布了基础信息字段，并在物理运动方面具有某些动力学（例如：地震，声波，在地理区域上传播）和延迟敏感性，以保证实时造型。 此外，在这种多末端设置中，``位'不是信息的通用货币，因为多源压缩和通信的分离是可悲的。在此项目中，这两个问题是通过考虑新的动态压缩模型和一个新的基于多面图的新架构来解决的，用于分布式延迟敏感信息的分布式传输。在此新压缩模型中，某些节点上可用的信息正在随着时间的函数而变化。该模型被称为用feedforward的源编码。这在信息压缩问题中引起了全新的活力。 此外，在该项目中考虑的分布式信息的传输体系结构中，多目标图用作分布式压缩和分布式通信之间的离散接口。这导致了基于多末端传感器的信息处理系统的模块化设计。 使用这些机制，该项目的目标是实现一个具有新体系结构的自组织传感器网络，该架构能够对分布式和延迟敏感的复杂信息字段的更有效表示，通信和实时重建，而不是当今任何系统。这项研究工作得到了教育的补充，以培训年轻工程师，以熟练于建造，管理和开发如此复杂的信息系统。该项目中考虑的具体问题是课程中项目工作的开发，综合的本科教育和研究工作以及课程开发，并开发了分布式信号处理和沟通课程。