Sparsity-based Missing Data Recovery and Compressive Transmission in Wireless Sensor Networks

在无线传感网中，节点断电、故障等多种原因常导致传感节点数据丢失。同时，传感器节点能量和带宽均非常有限。本论文利用数据本身的稀疏性，在在稀疏重建的理论框架下，研究无线传感网中的数据修复和压缩传输问题。本文主要工作包括： (1)提出二维空间数据修复方法，将数据看成一个整体，在不进行重传的情况下，用最小化范数算法修复空间平面上丢失的数据。零空间理论分析表明对于空间数据丢失，稠密型的二维DCT基是一种较合适的稀疏变换。DCT基不仅适合传感网数据低频分量多的特点，同时能保留数据的结构信息，比局部基函数（如小波变换）更能抵抗局部大范围的数据丢失。假设能接受的修复误差水平是，所提方法重建稀疏的低频信号时，...In wireless sensor networks, due to power outrage at a sensor node, hardware dysfunction, bad channel or environmental conditions, not all sensor samples can be successfully gathered at the sink. In this thesis, we aim to recover the missing samples without retransmission. Based on the sparsity of sensor networked data and the theory of compressed sensing, we use optimization algorithms to recover...学位：工学博士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332008015053

Technology of Data Transmission and Aggregation in Wireless Sensor Network

多跳传输原始测量数据的能耗较大,将数据进行分布式网内压缩后再传输的节能方案逐渐为人们所关注。在此从数据融合的角度,对无线传感器网络中各种数据传输技术进行了较全面的考察,着重介绍网内分布式小波变换算法(dWT_Irr)。最后,列表分析比较了几种常见的数据融合技术,并对该领域的进一步发展提出了一些思路。It wastes a lot of energy to dump all measurements to Sink with multi-hop method,and now a promising way is to do distributed in-network data aggregation before transmission.In this paper,a thorough survey of data transmission technology in WSN is provided in the perspective of data aggregation,particularly the algorithm of DWT_IRR.Typical algorithms are compared in a table,and some directions of future work are proposed.福建省自然科学基金资助项目(2007J0036

无线网状网络的路由协议研究

中文摘要：无线网状网络是一种新型的宽带无线网络，主要用于因特网的无线接入。路由是 WMN 中的一项关键技术。 本文先介绍路由技术的概念以及一般有线网络的路由，然后以Ad hoc 为例介绍无线多跳网络中的路由，并比较它们与有线网 络路由的区别。接着着重介绍 WMN 路由协议。最后本文详细介绍分析一种适用于无线网状接入网的 TBR 协议及其改进协 议

基于GA-GRNN的高速列车头型三维优化设计

针对CRH380A型高速列车头部外形的气动减阻问题, 设计了一种新型的基于自由曲面变形的局部型函数参数化方法, 提出了一套基于实数编码遗传算法的变光滑因子广义回归神经网络响应面模型(GA-GRNN)的气动外形优化方法. 优化结果表明: 局部型函数参数化方法操作简单、实现方便, 使用少量的设计参数可以控制较大变形区域, 且能保证变形的光顺性和不同变形区域间的光滑过渡; 使用同样的样本点进行训练, GA-GRNN 比GRNN的预测精度高, 更容易得到全局最优解; 优化后, CRH380A 三辆编组简化外形气动阻力减小8.7%, 本文提出的优化设计方法简单、高效, 为高速列车气动外形的工程优化设计提供了新思路

Optimized Local Superposition in Wireless Sensor Networks with t-average-mutual-coherence

Compressed sensing (CS) is a new technology for recovering sparse data from undersampled measurements. It shows great potential to reduce energy for sensor networks. First, a basic global superposition model is proposed to obtain the measurements of sensor data, where a sampling matrix is modeled as the channel impulse response (CIR) matrix while the sparsifying matrix is expressed as the distributed wavelet transform (DWT). However, both the sampling and sparsifying matrixes depend on the location of sensors, so this model is highly coherent. This violates the assumption of CS and easily produces high data recovery error. In this paper, in order to reduce the coherence, we propose to control the transmit power of some nodes with the help of t-average-mutual-coherence, and recovery quality are greatly improved. Finally, to make the approach more realistic and energy-e±cient, the CIR superposition is restricted in local clusters. Two key parameters, the radius of power control region and the radius of local clusters, are optimized based on the coherence and resource consideration in sensor networks. Simulation results demonstrate that the proposed scheme provides a high recovery quality for networked data and verify that t-average-mutual-coherence is a good criterion for optimizing the performance of CS in our scenario.Qualcomm-Tsinghua-Xiamen University Joint Research Program; National Natural Science Foundation of China under grant 61172097;Fellowship of Postgraduates' Oversea Study Program for Building High-Level Universities from the China Scholarship Council

国内高速列车气动噪声研究进展概述

随着运行速度的提升,气动噪声逐渐成为高速列车最主要的噪声源,并极有可能成为新设计高速列车的一个技术瓶颈。开展高速列车气动噪声研究,明晰高速列车气动噪声机理与规律,发展低噪声高速列车外形设计对更高速度级的高速列车研发具有重要意义。本文主要对自2010年以来国内进行的高速列车气动噪声研究进行梳理总结。首先详细介绍了高速列车气动噪声研究采用的一系列方法,主要从实车试验、风洞实验以及数值模拟方法三个方面展开。在掌握高速列车气动噪声研究方法的基础上,进而探讨了当前高速列车气动噪声研究的现状,重点就高速列车气动噪声源识别、主要噪声源机理与特性、噪声源优化等方向进行了阐述,并明确了当前研究获得的一些主要结论。最后简要探讨了高速列车气动噪声未来可能的研究方向

Sparsity-based Online Missing Sensor Data Recovery

In sensor networks, due to power outage at a sensor node, hardware dysfunction, or bad environmental conditions,not all sensor samples can be successfully gathered at the sink. Additionally, in the data stream scenario, some nodes may continually miss samples for a period of time. In this paper, a sparsity-based online data recovery approach is proposed. We construct an overcomplete dictionary composed of past data frames and traditional fixed transform bases. Assuming the current frame can be sparsely represented using only a few elements of the dictionary, missing samples in each frame can be estimated by Basis Pursuit. Our method was tested on data from a real sensor network application:monitoring the temperatures of the disk drive racks at a data center. Simulations show that in terms of estimation accuracy and stability, the proposed approach outperforms existing average-based interpolation methods, and is more robust to burst missing along the time dimension.This work was supported by Tsinghua-Qualcomm Joint Research Program,Fundamental Research Funds for the Central Universities (No. 2011121050),and National Natural Science Foundation of China (No. 61001142)

典型路基结构对高速列车横风气动特性影响分析

由于地域及环境的限制, 高速铁路采用多种路基结构如平直地面、不同高度路堤、高架桥等,当列车运行在路堤及高架桥上时,车体周围的绕流流场比平直地面更加复杂。在强横风的作用下,不同的路基结构上的高速列车横风气动特性存在明显差异,不合理的路基结构将影响列车的横风安全性。同时列车结构复杂,转向架、受电弓等都对列车的流场特性有重要作用,过于简化的短编组列车外形不能够精细反映列车的真实气动特性。为研究典型路基结构对高速列车横风气动特性的影响,以9编组动力集中型高速列车实车为研究对象,考虑风挡、转向架、受电弓等细节特征,对列车运行速度为200 km/h,横风速度分别为20 m/s、30 m/s、35 m/s、40 m/s,路基结构分别为平直地面、3 m路堤、6 m路堤、高架桥等四种场景下的高速列车空气动力学性能进行了仿真计算和对比,分析了不同路基地面条件下列车的横风气动特性的差异及规律,为横风条件下复杂路基结构的列车运行安全控制提供了参考

Sparsity-Based Spatial Interpolation in Wireless Sensor Networks

The authors would like to thank Ming-Ting Sun at University of Washington and Zicheng Liu at Microsoft for constructive suggestions.In wireless sensor networks, due to environmental limitations or bad wireless channel conditions, not all sensor samples can be successfully gathered at the sink. In this paper, we try to recover these missing samples without retransmission. The missing samples estimation problem is mathematically formulated as a 2-D spatial interpolation. Assuming the 2-D sensor data can be sparsely represented by a dictionary, a sparsity-based recovery approach by solving for l1 norm minimization is proposed. It is shown that these missing samples can be reasonably recovered based on the null space property of the dictionary. This property also points out the way to choose an appropriate sparsifying dictionary to further reduce the recovery errors. The simulation results on synthetic and real data demonstrate that the proposed approach can recover the missing data reasonably well and that it outperforms the weighted average interpolation methods when the data change relatively fast or blocks of samples are lost. Besides, there exists a range of missing rates where the proposed approach is robust to missing block sizes.Qualcomm-Tsinghua- Xiamen University Joint Research Program;Fellowship of Postgraduates’ Oversea Study Program for Building High-Level Universities from the China Scholarship Council

Influences of affiliated components and train length on the train wind

The induced airflow from passing trains, which is recognized as train wind, usually has adverse impacts on people in the surroundings, i.e., the aerodynamic forces generated by a high-speed train&#39;s wind may act on the human body and endanger the safety of pedestrians or roadside workers. In this paper, an improved delayed detached eddy simulation (IDDES) method is used to study train wind. The effects of the affiliated components and train length on train wind are analyzed. The results indicate that the affiliated components and train length have no effect on train wind in the area in front of the leading nose. In the downstream and wake regions, the longitudinal train wind becomes stronger as the length of the train increases, while the transverse train wind is not affected. The presence of affiliated components strengthens the train wind in the near field of the train because of strong flow solid interactions but has limited effects on train wind in the far field.</span