Optimization of Distributed Coding for Sources with Memory and Applications in Sensor Networks

0728986Rose，加州圣巴巴拉大学的Anasthus传感器网络中具有存储器和应用的源的分布式编码优化分布式源编码受到高密度传感器网络的强烈推动，在许多不同的科学和工程学科中具有广阔的应用前景。能源和容量的限制导致广泛的努力，利用传感器间（空间）的相关性，以尽量减少数据通信的资源需求。然而，实际的分布式源几乎总是表现出相当大的时间相关性。一个主要的挑战出现，由于利用时间与空间的相关性的目标之间的冲突。分布式信源编码在传感器网络中的实际应用程度取决于有效解决方案的开发。该项目的重点是时间和空间相关性的联合开发，这需要基本的权衡分析，开发新的编码器范例，以及优化工具来处理设计复杂性和系统复杂性约束。研究工作包括从信源编码，估计和信息论原理推导这些方法的理论基础，以及开发用于优化分布式预测编码器的实用算法工具，以克服若干障碍和挑战：预测和分布式量化之间的冲突，充满局部最小值的代价函数的棘手性，由于通过预测环路的反馈导致的训练过程的不稳定性、信道损耗对性能和设计的影响、成本函数的适应性以考虑（在时间和空间上）重要的感知事件。由首席研究员的研究小组开发的用于全局优化和稳定设计预测编码器的相关工具将作为该方法的初始构建块。传感器网络中广泛的多学科兴趣被利用来访问不同的数据和真实世界的实验环境，让学生接触到广泛的学科组合，并广泛传播结果。

0728986Rose, KennethU of Cal Santa BarbaraOptimization of Distributed Coding for Sources with Memory and Applications in Sensor NetworksDistributed source coding is strongly motivated by high-density sensor networks with promising applications in numerous diverse scientific and engineering disciplines. Energy and capacity constraints led to extensive efforts to exploit inter-sensor (spatial) correlations so as to minimize resource requirements for data communications. However, practical distributed sources virtually always exhibit considerable time correlations. A major challenge emerges due to conflicts between the objectives of exploiting temporal versus spatial correlations. The degree to which distributed source coding will be practically applicable to sensor networks crucially depends on the development of effective solutions. The project focuses on joint exploitation of temporal and spatial correlations, which requires fundamental tradeoff analysis, development of new coder paradigms, and optimization tools to handle design intricacies and system complexity constraints.The research work comprises derivation of the theoretical foundation for the approaches from source coding, estimation and information theory principles, and the development of practical algorithmic tools for optimizing distributed predictive coders to overcome several obstacles and challenges: conflicts between prediction and distributed quantization, intractability of the cost function which is riddled with local minima, instability of training procedures due to feedback through the prediction loop, impacts of channel loss on performance and design, adaptation of the cost function to account (in time and space) for significant sensed events. Relevant tools for global optimization and for stable design of predictive coders, developed by the Principal Investigator's research group, will serve as initial building blocks for the approach. Extensive multidisciplinary interest in sensor networks is leveraged for access to diverse data and real-world experimental settings, for exposure of students to a broad mix of disciplines, and for broad dissemination of results.