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

0728986 Rose，KennethU of Cal Santa Barbara在传感器网络中对具有存储和应用程序的信源进行分布式编码的优化分布式信源编码受到高密度传感器网络的强烈推动，在众多不同的科学和工程学科中具有广阔的应用前景。能源和能力的限制导致广泛努力利用传感器间(空间)相关性，以最大限度地减少数据通信所需的资源。然而，实际的分布式震源几乎总是表现出相当大的时间相关性。由于利用时间相关性和空间相关性的目标之间的冲突，出现了一个重大挑战。分布式信源编码在多大程度上实际应用于传感器网络，关键取决于有效解决方案的开发。该项目的重点是联合开发时间和空间相关性，这需要基本的权衡分析，开发新的编码器范例，以及处理设计复杂性和系统复杂性约束的优化工具。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，并开发实用的算法工具来优化分布式预测编码器，以克服以下几个障碍和挑战：预测和分布式量化之间的冲突，陷入局部极小的代价函数的困难，通过预测环路反馈导致的训练过程的不稳定性，信道损失对性能和设计的影响，对成本函数进行调整，以(在时间和空间上)考虑重大的感知事件。首席调查员研究小组开发的全球优化和预测编码器稳定设计的相关工具将作为这一办法的初始组成部分。对传感器网络的广泛的多学科兴趣被用来获取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.