【作者】 蔡述庭；

【导师】 王钦若；

【作者基本信息】 广东工业大学 ， 控制理论与控制工程， 2011， 博士

【摘要】 无线传感器网络(WSNs)作为一个新兴学科与传统学科交叉的领域,有着极强的应用背景和实用价值,受到世界各国学者的重视。国内将WSNs研究提高到一个战略高度始于中国科学院1999年发表的《知识创新工程试点领域方向研究》信息与自动化领域研究报告,将其作为信息与自动化领域的几个重点突破方向之一。能量问题是WSNs最核心的问题之一。传统WSNs传感器节点获取的数据相对简单,故节点能量模型中数据处理能耗常被忽略,而数据传输能耗占整个节点能耗的绝大部分。因此目前WSNs研究主要针对数据传输所涉及到的一系列问题,包括流量控制,拥塞控制,路由协议以及相应的能量控制等方面,以解决数据传输过程中能量受限条件下的性能优化问题。随着多媒体传感器加入到WSNs中,传感器获取的数据量大大增加。传输这些数据之前必须进行有效压缩,否则需求的网络带宽和传输功耗都会很大。然而,由于视频压缩需要进行复杂计算,其消耗了WSNs节点总能量的大部分,完全颠覆了传统WSNs节点能量模型。为了降低WSNs节点整体能耗,除了传统能量控制策略以外,还必须采取恰当措施来降低节点计算能耗,即降低压缩复杂度。现有的视频编码标准都面向广播应用,其视频压缩具有很高的复杂度,不适合于WSNs能量控制。一种方法是将WSNs节点的编码复杂度转移到解码端,从而降低WSNs节点的压缩计算能耗。分布式信源编码从理论上证明将编码复杂度转移到解码端是可行的。本文基于分布式信源编码理论,围绕保证视频编码率失真性能的基础上降低视频压缩能耗的问题进行了深入研究。在详细探讨了国内外WSNs研究及WSNs中分布式视频编码研究(Distributed Video Coding,DVC)现状的基础上,对WSNs及分布式视频编码研究当前面临的关键问题进行了分析。针对这些问题展开研究,以下是主要研究内容及取得的成果：1)基于无比率(Rateless)思想,设计了一种低编码能耗的分布式视频压缩机制,提出了相应的置信传播译码算法,并对其编码计算复杂度进行了详细测试,对编码器能耗进行了估计。首先对目前分布式视频编码中广为使用的固定码率LDPC的局限性进行讨论,提出了一种基于无比率LDPC的码率自适应机制,设计了相应的编码码率控制(ERC)和混合码率控制(HRC)机制,并有针对的改进了相应的置信传播(BP)译码算法,提高其解码性能。最后将设计的方案与现有的编码标准的PSNR性能和能耗进行了比较和分析。实验结果表明,提出的无比率分布式视频压缩机制相比较其它几种帧内编码的率失真性能有1—2dB的提升,而编码计算能耗要降低大约5%—40%；相对于帧间编码其率失真性能还有2dB的差距,但帧间编码能耗是提出的无比率分布式视频编码能耗的50—60倍。2)为了进一步提高WSNs节点视频信号传输的鲁棒性,将数字喷泉技术用于分布式视频编码。在简要分析喷泉码技术的基本特点后,重点讨论了性能比较好的Raptor码。由于目前分布式视频编码中大都采用与实际不符的无记忆的相关信道模型,提出将信源—边信息的统计相关建模为有记忆的隐式马尔科夫模型,并采用联合信源—信道设计方法,将Raptor用于分布式视频编码设计,设计了相应的BP译码算法。实验结果表明,在相同丢包率条件下,提出的分布式视频编码系统相比LDPCA以及H.26L FGS编码的PSNR性能有1dB左右的优势,且随着丢包率的提高,PSNR性能优势更加明显。提出系统的编码能耗较H.264帧内编码要低30%左右。而H.264帧间编码能耗是提出系统能耗的50-60倍。3)基于交互式编解码,设计了一种能源高效的分布式视频压缩机制。首先基于交互式编解码机制,建立统一的有记忆的有限状态信道模型。随后实现了一种基于LDPC码的线性交互式编解码算法,并设计了信源一边信息对建模为有限状态模型的BP解码算法。实验结果表明提出的基于交互式编解码机制的分布式视频编码系统的率失真性能较传统H.264帧内编码PSNR’性能提升1—3dB,编码功耗较H.264帧内编码降低20%左右,而帧间编码能耗是提出系统的50倍左右。4)搭建实际无线通信系统,并将分布式视频编码及JPEG和H.264的编码在能量受限的PDA上实现,通过WLAN将编码数据传输到服务器端,而服务器端进行相应的译码。在此基础上输入四个不同种类的标准视频序列来对DVC和JPEG及H.264的编解码复杂度及PSNR性能进行了详细测试。测试数据结果表明,DVC编码复杂度相对JPEG及H.264都要低,H.264编码时间为DVC编码时间的10倍左右。因此,DVC编码在能量受限的应用场合具有较大的优势。本论文得到了广东省”211工程”重点学科子项目(粤发改[431])国家自然科学基金(60871025),广东省自然科学基金(8151009001000060)等研究项目资助。更多还原

【Abstract】 As a interdiscipinary research field of new and traditional branch of science, wireless sensor networks (WSNs) are applied widely and valuable in practical, and attracted more and more attention of experts all of world. In China, the research of WSNs was promoted to a national strategy since a research report,’Research on pilot field direction of knowledge innovation engineering’, field of information and automatic, was published in 1999. In this report, WSNs was one of key areas which shall be broken through in the field of information and automatic.Energy saving was the key problem of WSNs. The data access by the traditional WSNs nodes are simple, the energy consumed by data processing in the model of node energy consumed was negligible always. While the energy consumed by data transmission was most part of the total node energy consumed. So the research of WSNs was focused on the problem involved the data transmission, include the flow control, congestion control, routing and energy control, to solve the performance optimization problem under the constraints of energy in data transmission.While the video sensor was added into WSNs, the data accessed by video sensor is extremely huge. This requires the video to be efficiently compressed before transmission, otherwise, the required network bandwidth and power consumption for wireless transmission are tremendous. However, video compression always needs sophisticated computation, the most part of WSNs node energy was consumed by video compression, which far exceed the energy consumed by video transmission. To decrease the energy consumed by WSNs node, some methods must be implement to decrease the compression power and reduce the encode complexity. The video encode standard are faced to application of broadcasting, the video compression are complicated, is not fit for energy control of WSNs. One of methods is to transfer the complexity of encoder to decoder of WSNs, reduce the compression power of WSNs node. The distributed source coding had proved that this transfer was feasible in theory.Based on the theory of distributed source coding, the research was focused on reduce the video compression power while guaranteed the performance of rate distortion. Based on the review of WSNs research of all of world and the distributed video coding in detailed, analyze the main challenges of this research. The study was focused on this problem, the main contents and achievements include:1) Based on the rateless idea, a kinds of low power encode scheme of distributed video coding was presented, corresponding brief propagation decoding algorithm was proposed, the complexity of encode computation was tested in detailed, and then the power of encoder was estimated. Firstly, the limitations of fixed-rate LDPC was discussed, A novel rate adaptive scheme based on rateless LDPC was proposed, rate estimation scheme include encoder rate control (ERC) and hybrid rate control (HRC) were also designed, the corresponding belief propagation (BP) decoding algorithm was presented to enhance its decoding performance. The results of experiments show that the performance of PSNR of proposed methods was increased 1-2dB than the traditional intra encode standard. The computation power reduces about 5-40% than the traditional intra encode. The performance of PSNR of proposed methods was worse than traditional inter encode about 2 dB, but the computation power of inter encode is about 50-60 times of the proposed methods.2) To improve the robust of the video transmission of WSNs node, the digital fountain techniques were used to distributed video coding. In a brief analysis of the basic characteristics of fountain code techniques, then the Raptor codes was discussed which has relatively good performance. The memory-less channel model was used in distributed video coding currently, which was not accorded with the practical cases. So the statistical correlation between the source-side information was modeled as a hidden Markov model, and the joint source-channel design was used. The Raptor was used for distributed video coding, and then the corresponding belief propagation decoding algorithm was designed. The experiments result shows that, on the same packed loss rate, the PSNR performance of proposed system was better than the LDPCA and H.26L FGS 1 dB, and when the packed loss rate increase, the superiority is evident. The power of proposed encoding system reduce about 30% compared with intra encode, while the power of inter encode is 50-60 times than the proposed distributed video coding.3) Based on the interactive encode and decode, a kinds of energy aware distributed video coding scheme was proposed. Using interactive encoding and decoding schemes, establishing universal coding and memory finite state channel model. Then a kinds of linear interactive encode and decode algorithm based on LDPC was proposed, and a new BP decoding algorithm is presented for LDPC decoding also, which applies to the case where the statistical correlation between source-side information can be modeled as a finite state channel. The results of experiments show that the PSNR performance of proposed methods was increased 1-3 dB, the power of proposed encoding system reduce about 20% compared with intra encode, while the power of inter encode is 50 times than the proposed distributed video coding.4) A practical wireless communication system was constructed. The encoder of DVC, JPEG and H.264 was implemented in the energy constrainted PDA, the encoded data was sent by WLAN, the decoding was completed in the server. Four different type standard sequences were used for testing the complexity and the PSNR perfomance of DVC, JPEG and H.264. The results of testing show that the encoding complexity of DVC was lower than the JPEG and H.264, especialy, the encoding complextiy of H.264 was 10 times of DVC. So the DVC is more appreciated for energy limited application. Project was supported by "211"Project of Guangdong Province (Guangdong Development and Reform Commission, No.431), Natural Science Foundation of China (No.60871025), Natural Science Foundation of Guangdong Province, China (No.8151009001000060).更多还原