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

高密度传感器网络在众多科学和工程学科中具有广阔的应用前景，分布式信源编码和路由受到强烈的推动，其中最小化数据通信的资源需求至关重要。这个研究项目源于这样的观察，即传统的分布式编码和路由的单独处理只有在严格简化的假设下才是最优的，而这些假设往往是无效的。一旦这些简化的假设被删除，主要的理论和实践挑战出现，包括基本性能界限的推导，和最佳的实际系统设计。分布式信源编码将在多大程度上实际应用于未来的网络环境中，关键取决于这种technology.This研究项目的发展正式权衡的基础上分布式编码的源与信息路由在多个汇网络，并开发系统的范例，允许联合优化。建立了最优性需要分散的源信息片段的各种汇的能力，并允许相关的，但未请求的源传递信息到任何特定的汇，本研究开发的“分散信息路由”的范例和它的集成在分布式编码系统。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理论基础）;联合编码器-路由器-解码器设计（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（网络上数据存储和检索的密切相关问题的进展）;大规模分布式编码（消除了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).