基于小波变换的传感网簇内数据融合方案

无线传感器网络中同一簇内采集到的数据具有空间相关性,由此文章提出了一种基于小波变换的簇内数据融合方案。在信源编码中,利用空频变换方法,在簇头节点对簇内数据去除空间上的相关性,从而实现数据融合,并使得SINK节点重构恢复的数据与原始数据质量基本相同。经过Matlab仿真分析,该数据融合方案在压缩比和失真度上均表现出良好的特性。     

无线传感器网络中基于区域相关性的自组织成簇算法

无线传感器网络资源有限,信息量大,通常采用分簇压缩减少传输量。针对传感器网络中的小波压缩,提出了一种基于相关区域自组织的成簇算法。该算法利用实际区域数据的相关性进行分簇,在簇头进行小波数据压缩的同时进行相关性检测,动态调整簇结构,保证簇内节点的相关性较好;同时在Sink分析簇间节点数据相关性,形成相关性好的大规模簇,进一步提高较长时间内的压缩效率。理论分析和实验仿真表明,该算法能尽可能地利用节点数据的时间和空间相关性去除冗余数据,提高小波数据压缩效率,降低了网络的能耗。     

基于双簇头的无线传感器网络反馈信任模型

针对无线传感器网络存在恶意或妥协节点导致通信数据被攻击的问题,提出一种基于双簇头的反馈信任模型,以保障数据在传输、感知以及融合等环节的可靠性与完整性。利用节点间直接交互结果评估节点的直接信任,同时考虑通信、数据感知和融合信任,通过贝叶斯公式评估邻居节点的通信信任,使用时间滑动窗将节点历史信任作为直接信任的补充,动态调整直接与间接信任权重使综合信任评价更客观和准确。在此基础上,引入双簇头交互监测和基站信任反馈机制,主簇头和监督簇头根据本地数据空间相关性独立评估成员数据感知信任,基站利用双簇头数据融合结果的时间相关性评估数据融合信任,并将最终信任结果反馈到所有节点。仿真结果表明,该模型能有效检测异常数据和恶意节点,成功抵御选择性转发攻击、伪造本地数据攻击以及伪造融合数据攻击,在网络安全与能耗间实现良好的平衡。     

基于矩阵的无线传感器网络数据融合方案

数据融合是降低无线传感器网络（ WSNs）能耗的重要手段,为了有效地节省WSNs节点的能量,提出一种基于矩阵的层簇式数据融合方案。在簇头选举时,考虑节点的剩余能量和相对密度,并利用矩阵运算对簇内成员节点采集的数据进行数据融合处理。最后将所提方案与传统的LEACH协议进行仿真比较,实验结果表明：相比于传统的LEACH协议,所提方案可以有效降低数据冗余,减少数据传输量,节约节点能耗,进而延长网络生存期。     

无线传感器网络中基于代理的D-S数据融合

提出了一种无线传感器网络中基于移动代理带证据权的D S融合算法。引入证据权对证据进行修正以降低冲突数据对融合结果的影响。采用三级D S组合规则进行融合决策：节点级融合计算单个节点时间域融合检测概率;簇内级融合计算簇内节点间空间域融合检测概率获取局部决策结果;簇间级融合计算簇间的融合检测概率获取最终的全局决策结果。仿真结果表明,本算法能以较小的能耗代价获取准确的融合结果并有效降低冲突数据对融合结果的影响。     

WSN中基于压缩感知的数据收集方案

提出一种基于压缩感知的数据收集方案.依据感知数据的空间相关性分析,计算出事件发生的区域范围.基于剩余能量的成簇算法对区域范围内的节点进行分簇.各个节点将感知到的原始数据,基于压缩感知理论,进行数据的稀疏表示并采用随机高斯矩阵进行观测,将其观测值发送和存储在簇头节点上,当有移动收集者进入簇头的通信范围后,进行数据收集.理论分析和仿真实验结果表明,该方案能有效延长网络生命周期.     

一种基于相关性的分布式异常检测算法

提出了一种传感器网络异常检测算法,与传统的基于相邻节点数据对比的检测算法不同,该算法首先在节点内分析数据的时间相关性,仅在发生异常时,上传至簇头节点,分析空间相关性;对于不确定的异常数据,在相邻分簇内,计算数据的相关性.还给出了采用直方图计算均值和分析相关性的方法.实验表明,该算法减少了通信量,提高了异常检测的准确率.     

基于分簇的无线传感器网络簇内数据融合算法

根据节点剩余能量、通信消耗量和节点IP,设计无线传感器节点分簇算法,减少了无线传感器网络的数据传输总量。分析对以分簇为基础的簇内数据的融合算法误差成因,提出先进行簇内传感器测量修正、再进行簇内传感器数据融合的改进算法。通过TOSSIM仿真平台的实验,改进后的簇内数据融合算法测量误差明显减少。     

一种能量高效的WSN近似数据收集算法

为降低能耗和最大化网络生存期,论文提出了在一定误差范围内的高效近似数据收集算法。首先利用节点感知数据的时间相关性生成局部估计模型,然后根据节点间估计数据的空间相关性进行分簇,在簇首进行相关性检测,动态调整簇结构,并将簇首的模型参数上传给SINK节点,最后在SINK节点进行全局近似数据收集。仿真结果表明,该算法能充分利用节点数据的时空相关性去除冗余数据,在给定误差界限内能显著降低通信成本。     

数据融合技术在无线传感器网络中的应用

在大规模的无线传感器网络中,传输数据量巨大,必然存在着数据传输可靠性、拥塞以及能耗等问题,高效的数据融合技术能够有效的解决这些问题。本文结合分簇路由算法的特征,采用两层融合技术,首先簇内节点与簇首节点的融合,簇内节点根据阈值来判断是否需要发送数据,簇首节点根据接收到的数据,进行数据一致性检验,剔除异常数据,第二层采用BP神经网络算法对簇首节点与基站的融合,得到所需要的结果。实验表明,进行融合后的数据可靠性高,较大减少了数据的传输量与冗余度、降低了能量的消耗,从而提高了整个网络的性能。     

Exploring pixel-value differencing and base decomposition for low distortion data embedding

This paper presents a low distortion data embedding method using pixel-value differencing and base decomposition schemes. The pixel-value differencing scheme offers the advantage of conveying a large amount of payload, while still maintaining the consistency of an image characteristic after data embedding. We introduce the base decomposition scheme, which defines a base pair for each degree in order to construct a two-base notational system. This scheme provides the advantage of significantly reducing pixel variation encountered due to secret data embedding. We analyze the pixel variation and the expected mean square error caused by concealing with secret messages. The mathematical analysis shows that our scheme produces much smaller maximal pixel variations and expected mean square error while producing a higher PSNR. We evaluate the performance of our method using 6 categories of metrics which allow us to compare with seven other state-of-the-art algorithms. Experimental statistics verify that our algorithm outperforms existing counterparts in terms of lower image distortion and higher image quality. Finally, our scheme can survive from the RS steganalysis attack and the steganalytic histogram attack of pixel-value difference. We conclude that our proposed method is capable of embedding large amounts of a message, yet still produces the embedded image with very low distortion. To the best of our knowledge, in comparison with the current seven state-of-the-art data embedding algorithms, our scheme produces the lowest image distortion while embedding the same or slightly larger quantities of messages.     

Which gray level should be given the smallest cost for adaptive steganography?

Currently, the most successful approach to steganography in digital image is distortion-minimization framework, which reduces the steganographers’ work to the design of distortion function with the aid of practical coding schemes. Previous distortion functions for spatial images are all position dependent, in which cost is determined by the relationships between neighboring pixels. Noticing that Gamma encoding is usually involved in image preprocessing in many cameras or image processing software, which causes some pixels to change greatly, we believe these pixels sensitive to Gamma encoding are more suitable for modification, because they are hard to model due to their large variations. Inspired by this idea, we proposed a position independent scheme, where the cost is only linked to the gray level. The effectiveness of our work is verified by extensive experimental results, which reveal an interesting relationship between steganographic costs and gray levels. The speed test shows that the speed of proposed scheme is very high thus suitable to be used in the real-time applications.     

高效可撤销的共享数据云存储审计

共享数据的云存储审计是指对群用户共享的云数据的完整性进行审计. 由于在共享数据云存储审计中, 用户可能因各种原因加入和离开用户群, 因此这种方案通常支持群用户撤销. 在大多数现存的共享数据云审计方案中, 用户撤销的计算开销与用户群要上传的文件块总数成线性关系, 造成很大的计算和通信代价, 如何减少用户撤销产生的计算和通...     

Issues and solution on distortion drift in reversible video data hiding

Different from reversible image data hiding, most reversible video data hiding schemes have the particular problem that the distortion due to hidden data will spread and accumulate. In this paper, the problem of distortion drift caused by reversible data hiding in compressed video is analyzed, and a lossless drift compensation scheme is proposed to restrain the distortion for the first time. In order to ensure the reversibility, drift compensation signals are merged in the quantized DCT (Discrete Cosine Transform) coefficients of P-frames and the corresponding recovery mechanism is presented as well. Experimental results demonstrate that the proposed lossless drift compensation scheme significantly improves the video quality, and the original compressed video can be recovered exactly after the hidden data and compensation signals are removed. In addition, the proposed scheme does not depend on specific reversible data hiding method.     

Hiding data in spatial domain images with distortion tolerance

Data hiding is a technique that is used to embed secret information into a cover media. It has been widely used in protecting copyright and transmitting sensitive data over an insecure channel. Conventional data hiding schemes only focus on how to reduce the distortion when sensitive data is embedded into the cover image. However, the transmitted images may be compressed or occur transmitting errors. If such errors occur, the receiver cannot extract the correct information from the stego-image. In this paper, we proposed a novel hiding data scheme with distortion tolerance. The proposed scheme not only can prevent the quality of the processed image from being seriously degraded, but also can simultaneously achieve distortion tolerance. Experimental results show that the proposed scheme indeed can obtain a good image quality and is superior to the other schemes in terms of its distortion tolerance.     

基于直方图对的MPEG-4无损数据隐藏

目前绝大部分无损数据隐藏方案,只能无损恢复嵌入数据,而在数据提取后,原始的视频载体无法完全恢复,本质上仍是有损隐藏算法.为了实现视频无损认证和隐蔽通信,提出基于直方图对的MPEG-4视频无损数据隐藏方法.通过直方图调整,在MPEG-4视频的帧内视频对象平面(I-VOP)的量化离散余弦变换(DCT)系数中隐藏数据.同时,在提取过程中不需要原视频的参与,获得嵌入数据并且完全恢复原始视频.理论分析与实验结果表明,该方法失真程度小,隐蔽性高,适用性大.     

A new data hiding scheme for binary image authentication with small image distortion

A new data hiding scheme for binary image authentication that has a small distortion of the cover image is proposed in this paper. Using the data-embedding algorithm that is based on Hamming codes, the proposed scheme embeds authentication information into the cover image with flipping only a small number of pixels. A special type of the pixels are selected and flipped by a new algorithm to minimize visual distortion. This new algorithm is based on ELSSM (Edge Line Segment Similarity Measure). Randomly shuffling the bit-order of the authentication information to be embedded, the information can only be extracted by the designated receiver who has the symmetric key. We employ two measurement metrics: miss detection rates for the degree of security and PSNR (Peak Signal-to-Noise Ratio) and ELSSM for the degree of the image distortion to demonstrate the feasibility of the proposed scheme. Using these metrics, we analyze the proposed scheme and the previous schemes. The analysis reveals that the proposed scheme requires less image distortion than the previous schemes whilst achieving the same level of the miss detection rate. Experimental results demonstrate that the proposed scheme is more resilient against recent steganalysis attacks than the previous schemes.     

A high capacity data hiding scheme for binary images based on block patterns

This paper proposes a high capacity data hiding scheme for binary images based on block patterns, which can facilitate the authentication and annotation of scanned images. The scheme proposes block patterns for a 2 × 2 block to enforce specific block-based relationship in order to embed a significant amount of data without causing noticeable artifacts. In addition, two kinds of matching pair (MP) methods, internal adjustment MP and external adjustment MP, are designed to decrease the embedding changes. Shuffling is applied before embedding to reduce the distortion and improve the security. Experimental results show that the proposed scheme gives a significantly improved embedding capacity than previous approaches in the same level of embedding distortion. We also analyze the perceptual impact and discuss the robustness and security issues.     

Security and Robustness Enhancement for Image Data Hiding

In many applications, data hiding can be viewed as a tradeoff between capacity, robustness (against attacks), and embedding induced distortion. In this paper, we consider a fourth parameter: the security of the hidden information. Specifically, we propose a hash-based randomized embedding algorithm (HRE) that increases the security of the hidden data. We then optimize this algorithm against JPEG attacks. We derive a mathematical expression for the security of our algorithm, using which we show that the security of our algorithm can be increased independent of capacity, robustness, and embedding induced distortion. The maximum security depends only on the length of the key sequence, which is limited only by the size of the host image. Using a joint security and capacity measure, we show that the proposed scheme performs better than current secure quantization based data hiding schemes. We also derive the optimal value of distortion compensation factor of the HRE algorithm against JPEG compression attack. Experimental results show that the operating points achieved by the proposed scheme are 7 dB better than current blind data hiding schemes against the JPEG attack