Trace: Tennessee Research and Creative Exchange Distributed Data Aggregation for Sparse Recovery in Wireless Sensor Networks Recommended Citation Distributed Data Aggregation for Sparse Recovery in Wireless Sensor Networks
Trace: Tennessee Research and Creative Exchange Distributed Data Aggregation for Sparse Recovery in Wireless Sensor Networks Recommended Citation Distributed Data Aggregation for Sparse Recovery in Wireless Sensor Networks
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Masters Theses;Shuang Jiang;Li;Shuangjiang Li;Jiang;H. Qi;Major Professor;Husheng Li;Qing Cao;Carolyn R Hodges;Shuangjiang Li
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Masters Theses;Shuang Jiang;Li;Shuangjiang Li;Jiang;H. Qi;Major Professor;Husheng Li;Qing Cao;Carolyn R Hodges;Shuangjiang Li
我在此提交了李双江的论文，题目是“分布式数据聚合在无线传感器网络中的稀疏恢复”。“我已经检查了这篇论文的形式和内容的最终电子副本，并建议它被接受部分满足理学硕士学位的要求，主修计算机工程。（原始签名与正式学生记录一起存档。致谢我想向所有使这篇论文成为可能的人表示衷心的感谢和赞赏。首先，我要感谢我的导师齐海荣博士，感谢他对我的帮助和鼓励，感谢他总是在我的研究中伸出援助之手。没有她的出色指导和支持，这项工作是不可能完成的。我还要感谢李博士和曹博士。我非常感谢他们的时间和投入这篇论文。我要特别感谢我的父母、公婆、姐姐和妻子，他们在我的学习生活中给予了我无微不至的鼓励和大力支持。最后，我感谢AICIP实验室的所有成员在AICIP小组会议上提出的有用的建议和建议。iii摘要我们考虑了无线传感器网络中使用压缩感知/压缩采样（CS）的近似稀疏恢复问题。我们的目标是恢复的n维数据值，只查询m n传感器的传感器读数的一些线性投影的基础上。针对这一问题，提出了一种基于稀疏二进制CS测量矩阵的分布式压缩稀疏采样（DCSS）算法。在双层采样模型中，每个传感器首先独立地对环境进行采样。然后，融合中心（FC），作为一个伪传感器，采样的传感器网络，以选择一个子集的传感器（m出n），直接响应FC的数据恢复的目的。稀疏二进制矩阵的设计使用非平衡扩展图，达到了最先进的性能CS计划。这个二进制矩阵可以解释为传感器选择矩阵，其公平性进行了分析。在合成和真实的数据集上的广泛实验表明，通过使用DCSS算法仅查询最小量的m个传感器，CS恢复精度可以与密集测量矩阵（例如，高斯、傅立叶加扰）。我们...
I am submitting herewith a thesis written by Shuang Jiang Li entitled "Distributed Data Aggregation for Sparse Recovery in Wireless Sensor Networks." I have examined the final electronic copy of this thesis for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Master of Science, with a major in Computer Engineering. (Original signatures are on file with official student records.) ii Acknowledgements I would like to extend my sincere gratitude and appreciation to all the individuals who made this thesis possible. First and foremost, I would like to thank my advisor, Dr. Hairong Qi for assisting and encouraging me and for always being there to lend a helping hand in my research. Without her excellent guidance and support, this work would not have been possible. I also want to give thanks to Dr. Li and Dr. Cao. I greatly appreciate their time and input to this Thesis. I would like to give my special thanks to my parents, my parents-in-law, my sister, and my wife, who provide the encouragement and the strongest support during my study life in every possible way. Finally, I thank all the members in the AICIP Lab for their useful advise and suggestions presented at AICIP group meetings. iii Abstract We consider the approximate sparse recovery problem in Wireless Sensor Networks (WSNs) using Compressed Sensing/Compressive Sampling (CS). The goal is to recover the n-dimensional data values by querying only m n sensors based on some linear projection of sensor readings. To solve this problem, a two-tiered sampling model is considered and a novel distributed compressive sparse sampling (DCSS) algorithm is proposed based on sparse binary CS measurement matrix. In the two-tiered sampling model, each sensor first samples the environment independently. Then the fusion center (FC), acting as a pseudo-sensor, samples the sensor network to select a subset of sensors (m out of n) that directly respond to the FC for data recovery purpose. The sparse binary matrix is designed using unbalanced expander graph which achieves the state-of-the-art performance for CS schemes. This binary matrix can be interpreted as a sensor selection matrix-whose fairness is analyzed. Extensive experiments on both synthetic and real data set show that by querying only the minimum amount of m sensors using the DCSS algorithm, the CS recovery accuracy can be as good as dense measurement matrices (e.g., Gaussian, Fourier Scrambles). We …