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

分布式源编码和路由受到高密度传感器网络的强烈推动，高密度传感器网络在许多科学和工程学科中具有很好的应用前景，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一种观察，即传统的分布式编码和路由的分离处理只有在严格简化的假设下才是最优的，而这些假设通常是无效的。一旦这些简化的假设被移除，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，以及最佳的实际系统设计。分布式源代码在多大程度上实际适用于未来的网络环境，关键取决于这种技术的发展。本研究项目形式化了基于多个汇聚网络的信息路由的分布式源编码的权衡，并开发了允许联合优化的系统范例。在确定了最优性需要将源信息的片段分散到各种接收器，并允许相关但未请求的源将信息传递到任何特定接收器的能力之后，本研究开发了“分散信息路由”范式及其在分布式编码系统中的集成。主要研究方向包括：基础和性能界限（从信源编码、估计和信息论原理中推导出理论基础）；编码器-路由器-解码器联合设计（开发所有系统组件联合设计的高效优化工具）；分布式存储（研究了与之密切相关的网络数据存储和检索问题）；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).