CIF: Small: An Integrated Framework for Distributed Source Coding and Dispersive Information Routing

分布式信源编码和路由受到高密度传感器网络的强烈推动，在许多科学和工程领域都有很好的应用前景，在这些领域中，最小化数据通信的资源需求是至关重要的。这一研究项目源于这样一个观察，即传统的分布式编码和路由的分离处理只有在严格的简化假设下才是最优的，而这些假设往往是无效的。一旦去掉这些简化的假设，主要的理论和实践挑战就会出现，包括基本性能界限的推导和最优的实用系统设计。分布式信源编码在多大程度上适用于未来的网络环境，关键取决于此类技术的发展。该研究项目形式化了分布式信源编码与多汇聚网络上的信息路由之间的权衡，并开发了允许联合优化的系统范例。在确定了最佳性需要能够将源信息的片段分散到各种信宿，并允许相关但未被请求的信源将信息传送到任何特定信宿之后，该研究开发了“分散信息路由”范例及其在分布式编码系统中的集成。主要研究内容包括：基础和性能界限(从信源编码、估计和信息论原理推导出理论基础)；联合编码器-路由器-译码设计(为所有系统组件的联合设计开发高效的优化工具)；分布式存储(在网络上密切相关的数据存储和检索问题上的进展)；大规模分布式编码(消除了在非常密集的传感器网络中分布式编码的主要复杂性瓶颈)。

Distributed source coding and routing are strongly motivated by high density sensor networks with promising applications in numerous scientific and engineering disciplines, where it is critical to minimize resource requirements for data communication. This research project stems from the observation that traditional separate treatment of distributed coding and routing is only optimal under strict simplifying assumptions that are often invalid. Major theoretical and practical challenges emerge once these simplifying assumptions are removed, including derivation of the fundamental performance bounds, and optimal practical system design. The degree to which distributed source coding will be practically applicable to future network environments, crucially depends on the development of such technology.This research project formalizes the tradeoffs that underlie distributed coding of sources with information routing over multiple sink networks, and develops system paradigms that allow joint optimization. Having established that optimality requires the ability to disperse fragments of a source information to various sinks, and to allow related but unrequested sources to convey information to any particular sink, this research develops the "dispersive information routing" paradigm and its integration within the distributed coding system. The main research thrusts include: foundation and performance bounds (derivation of the theoretical foundation from source coding, estimation and information theory principles); joint encoder-router-decoder design (development of efficient optimization tools for joint design of all system components); distributed storage (advances on the closely related problem of data storage and retrieval on a network); large scale distributed coding (eliminating the main complexity bottlenecks for distributed coding in very dense sensor networks).