Grey Wolf based compressive sensing scheme for data gathering in IoT based heterogeneous WSNs

Wireless Networks - Sensor node energy constraint is considered as an impediment in the further development of the Internet of Things (IoT) technology. One of the most efficient solution is to...     

基于多层分簇优化的认知无线电网络协作频谱感知策略

针对认知无线电网络(CRN)中基于簇的频谱感知策略的检测性能和能耗问题,提出一种基于多层分簇优化的协作频谱感知策略。首先,将CRN分成多个簇,进而将簇分成多个组,再将组分成多个子组,构建三层分簇结构;然后,利用提出的优化算法获得最优的分簇参数和决策阈值;最后,通过投票机制和K-out-of-N规则对各级决策进行聚合,进行频谱感知。实验结果表明,该方案在获得较高主要用户(PU)频谱占用检测率的同时,能够最大限度地减少信道开销,提高了网络的吞吐量。     

压缩感知图像融合

目前图像融合的方法大多数都是基于小波变换的图像融合方法,通过对小渡变换之后的低频系数和高频系数分别采用不同的融合准则,来达到所需要的图像以进行下一步处理,这些方法需要知道原始图像,也就是对硬件要求较高。采用压缩感知图像融合,即,将压缩感知用于图像融合,使得只知道原始图像在某个变换下的投影值的情况下,通过对已知的投影值使用融合规则得到融合后的投影值,然后用重构算法重构出图像,大大降低了对硬件的要求。在此给出了压缩感知融合方法与基于小波变换的图像融合方法的实验结果,融合结果表明,在不降低融合效果和视觉效果的基础上,该方法能够极大地降低硬件成本。采用熵作为衡量融合效果的指标,并对用两种方法融合的结果图像做了对比,研究结果表明,CS融合方法要优于基于小渡变换的图像融合方法。     

基于压缩感知的信号重构

压缩感知是针对稀疏或可压缩信号,在采样的同时即可对信号数据进行适当压缩的新理论,采用该理论,可以仅需少量信号的观测值来实现精确重构信号。文中概述了CS理论框架及关键技术问题,介绍了信号稀疏表示、观测矩阵和重构算法。最后仿真实现了基于压缩感知的信号重构,并对正交匹配追踪(OMP)重构算法性能作了分析。     

A compressive sensing and unmixing scheme for hyperspectral data processing

Hyperspectral data processing typically demands enormous computational resources in terms of storage, computation, and input/output throughputs, particularly when real-time processing is desired. In this paper, a proof-of-concept study is conducted on compressive sensing (CS) and unmixing for hyperspectral imaging. Specifically, we investigate a low-complexity scheme for hyperspectral data compression and reconstruction. In this scheme, compressed hyperspectral data are acquired directly by a device similar to the single-pixel camera based on the principle of CS. To decode the compressed data, we propose a numerical procedure to compute directly the unmixed abundance fractions of given endmembers, completely bypassing high-complexity tasks involving the hyperspectral data cube itself. The reconstruction model is to minimize the total variation of the abundance fractions subject to a preprocessed fidelity equation with a significantly reduced size and other side constraints. An augmented Lagrangian-type algorithm is developed to solve this model. We conduct extensive numerical experiments to demonstrate the feasibility and efficiency of the proposed approach, using both synthetic data and hardware-measured data. Experimental and computational evidences obtained from this paper indicate that the proposed scheme has a high potential in real-world applications.     

基于压缩感知的多光谱图像去马赛克算法

针对目前多光谱图像去马赛克算法存在计算量大、效率低的缺点,本文提出一种基于压缩感知的多光谱图像去马赛克算法。首先,分析去马赛克与压缩感知问题的等价性,建立基于压缩感知的去马赛克模型;然后,采用离散余弦变换构建压缩感知的稀疏基,将去马赛克问题转化为压缩感知的信号重构问题;最后,采用改进的光滑0范数和修正牛顿法的重构算法求解去马赛克问题,得到重构的多光谱图像。仿真实验表明,相对于基于克罗内克压缩感知和组稀疏两种算法,本文算法提高了重构的多光谱图像的峰值信噪比,能有效减少对比算法重构多光谱图像中出现的锯齿现象,改善了重构图像具有更好的视觉效果。实验结果验证了本文算法的有效性。     

Weighted centroid localization based on compressive sensing

To solve the problem of estimating the locations of sensor nodes in wireless sensor networks where most nodes are without an effective positioning device, a novel range-free localization algorithm—weighted centroid localization based on compressive sensing (WCLCS) is proposed. WCLCS makes use of compressive sensing to get decomposition coefficients between each nonbeacon node and beacon nodes. According to these coefficients, WCLCS algorithm decides the weighted value of each beacon node for Centroid and estimates the locations of nonbeacon nodes. The simulation results show that WCLCS has better localization performance than LSVM.     

HSCA: a novel harmony search based efficient clustering in heterogeneous WSNs

Clustering objective reasons scalability, fault tolerance, data aggregation or fusion, load balancing of cluster heads, stabilized network topology, maximal network lifetime, increased connectivity, reduced routing delay, collision avoidance and utilizing sleeping schemes in wireless sensor networks. Load balanced clustering effectively organize the network into a connected hierarchy. Clustering is a discrete problem that can have more than one solution under different operating constraints. In this scenario, meta-heuristic algorithms are found suitable because they give set of solutions in acceptable time constraints. In the literature, several analytical and meta-heuristic approaches have been developed for load balanced clustering. In this paper, a novel harmony search based energy efficient load balanced clustering algorithm is presented and it is tested on a large sample network. Results demonstrated that the proposed approach has faster convergence and gives reliable and efficient load balanced clustering as compared to conventional harmony search algorithm (HSA) and several other methods in the literature. Moreover, the robustness of the proposed approach is also verified for different cases of fixed and variable parameters of HSA.     

基于压缩传感的光子计数成像系统

提出了一种基于压缩传感理论的光子计数成像系统。该系统以单光子计数器作为探测元件,以期在面元探测技术不甚成熟的现状下用点探测器进行极弱光探测。通过计算机模拟计算,验证了压缩传感理论结合单光子计数器应用于极弱光成像的可行性,讨论了单光子计数器的暗计数率、量子效率和测量噪声对成像质量的影响。介绍了压缩传感理论,为了获得更好的图像质量和更快的计算速度,提出了SpaRSA-DWT稀疏重建算法,并与传统的IWT算法进行对比。给出了两种算法下,迭代次数、测量数、噪声功率分别与获得图像信噪比的关系曲线,证明了SpaRSA-DWT算法的优越性。     

基于压缩感知的数字全息成像技术

提出一种基于压缩感知理论和数字全息术的数字全息压缩成像技术。通过传统相移数字全息技术获取干涉图样,将干涉同样投影到测量矩阵上,通过计算內积直接获取全息压缩数据。这些数据经过传统通信信道传输后,利用压缩感知重构算法获得原始全息数据,然后经过菲涅尔逆变换得到原始图像。实验仿真结果证明了这种压缩全息成像方法的可行性。     

Deterministic binary matrix based compressive data aggregation in big data WSNs

In big data wireless sensor networks, the volume of data sharply increases at an unprecedented rate and the dense deployment of sensor nodes will lead to high spatial-temporal correlation and redundancy of sensors’ readings. Compressive data aggregation may be an indispensable way to eliminate the redundancy. However, the existing compressive data aggregation requires a large number of sensor nodes to take part in each measurement, which may cause heavy load in data transmission. To solve this problem, in this paper, we propose a new compressive data aggregation scheme based on compressive sensing. We apply the deterministic binary matrix based on low density parity check codes as measurement matrix. Each row of the measurement matrix represents a projection process. Owing to the sparsity characteristics of the matrix, only the nodes whose corresponding elements in the matrix are non-zero take part in each projection. Each projection can form an aggregation tree with minimum energy consumption. After all the measurements are collected, the sink node can recover original readings precisely. Simulation results show that our algorithm can efficiently reduce the number of the transmitted packets and the energy consumption of the whole network while reconstructing the original readings accurately.     

WSNs中基于三因素节点评估的安全认证方案

为了减少传感器节点的资源利用并提高网络的安全性,提出了一种基于信任度的认证方案。该方案在计算节点信任度时引入时间片、安全行动系数和交互频度来计算节点信任度,这样使得自私节点很难伪装成正常节点,信任度与当前节点行为紧密相关,并防止节点通过很少的交易次数来达到较高的信任度,再利用信任度来判断一个节点是否可信,有效地提高了应用的安全性,对恶意节点的攻击起到一定的阻碍作用。然后设计了身份标识、密码、智能卡相结合的认证方案,并且用户在与传感器节点认证之前,网关查询网络中节点的信任度,从而找到可信的节点与用户进行认证,实现可信的传感器节点、网关节点和用户三者之间的交互认证,并且用户能方便地更改密码。安全性分析、性能分析及仿真实验的结果表明,与已提出的认证方案相比,该方案能够抵制重放攻击、内部攻击、伪装攻击等,同时计算花费少,适合于对安全性和性能有要求的无线传感器网络。本文网络版地址：http：//www.eepw.com.cn/article/276364.htm     

Radar ECCM based on phase-aid distributed compressive sensing

This paper proposes an electronic counter countermeasure (ECCM) technique to suppress randomly distributed multiple false targets generated by digital radio frequency memory-based electronic warfare equipment. Firstly, we present the modulation behaviors of deceptive multiple false targets jamming. Afterward, we discuss the ECCM potential of distributed compressive sensing (DCS) which not only could eliminate random distributed jamming signals but also could preserve the target echo. Further, an approach is proposed relying on phase-aided DCS to improve the performance against a special case of jamming signals that fall in the same range cell but with random amplitudes and phases. Finally, the suppression performances are evaluated through simulations illustrating the feasibility and validity of proposed algorithm.     

基于压缩感知的空间谱估计

以多重信号分类（Multrple Signal Classification,MUSIC）算法为代表的现代空间谱估计方法,估计的信源数受限于阵列形式,并且需要的采样数据量巨大.文章从压缩感知的基础理论出发,利用目标信号空间分布的稀疏性,建立了基于压缩感知的阵列信号空间谱估计模型.利用压缩感知方法,可以使用较少的阵元数对空间信号进行采样测量,并准确重构信号.相比传统的MUSIC空间谱估计算法,该方法所需阵元数少,采样数据量小,并且能同时进行信号强度和角度的估计.所提方法对推动压缩感知理论在阵列信号空间谱估计中的应用具有一定意义.     

基于压缩感知的数字图像水印算法

压缩感知理论作为新一代信息处理理论基础,在很多领域得到了应用,但在数字水印方面的应用还有待发展。利用压缩感知理论,在图像稀疏化后的观测域中实现水印嵌入过程。用小波变换将原图像稀疏化,选择一个固定高斯随机矩阵对其进行观测,在观测压缩域中嵌入与提取水印,用匹配追踪算法恢复稀疏信号。实验结果表明,该算法满足透明性、鲁棒性和安全性要求;同时抗提取实验表明,该算法能有效抵御非法提取水印信息,增加了安全性。     

Blind single-image super resolution based on compressive sensing

Blind super resolution is an interesting area in image processing that can restore high resolution (HR) image without requiring prior information of the volatile point spread function (PSF). In this paper, a novel framework is proposed for blind single-image super resolution (SISR) problem based on compressive sensing (CS) framework that is one of the first works that considers general PSFs. The fundamental idea in the proposed approach is to use sparsity on a known sparse transform domain as a powerful regularizer in both the image and blur domains. Therefore, a new cost function with respect to the unknown HR image patch and PSF kernel is presented and minimization is performed based on two subproblems that are modeled similar to that of CS. Simulation results demonstrate the effectiveness of the proposed algorithm that is competitive with methods that use multiple LR images to achieve a single HR image.     

A game theory based energy efficient clustering routing protocol for WSNs

The energy constraint is one of the inherent defects of the Wireless Sensor Networks (WSNs). How to prolong the lifespan of the network has attracted more and more attention. Numerous achievements have emerged successively recently. Among these mechanisms designing routing protocols is one of the most promising ones owing to the large amount of energy consumed for data transmission. The background and related works are described firstly in detail in this paper. Then a game model for selecting the Cluster Head is presented. Subsequently, a novel routing protocol named Game theory based Energy Efficient Clustering routing protocol (GEEC) is proposed. GEEC, which belongs to a kind of clustering routing protocols, adopts evolutionary game theory mechanism to achieve energy exhaust equilibrium as well as lifetime extension at the same time. Finally, extensive simulation experiments are conducted. The experimental results indicate that a significant improvement in energy balance as well as in energy conservation compared with other two kinds of well-known clustering routing protocols is achieved.     

A boolean spider monkey optimization based energy efficient clustering approach for WSNs

Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of cluster-based routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. Spider monkey optimization (SMO) is a relatively new nature inspired evolutionary algorithm based on the foraging behaviour of spider monkeys. It has proved its worth for benchmark functions optimization and antenna design problems. In this paper, SMO based threshold-sensitive energy-efficient clustering protocol is proposed to prolong network lifetime with an intend to extend the stability period of the network. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The results demonstrate that the proposed protocol significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.     

基于压缩感知的ISAR高分辨成像算法

针对 ISAR 在短孔径条件下存在的方位向分辨率低、易受噪声干扰等问题,基于压缩感知理论,提出了一种适用于短孔径时间模式下的基于压缩感知的ISAR方位向高分辨成像算法--PH-SL0算法。该算法首先构建部分随机化哈达玛矩阵作为量测矩阵,PH 矩阵具有重构精度高、重构需要量测个数少的优点;然后将运算速度快、重构精度高且稳健性好的平滑0-范数法（SL0, smoothed L0-norm）推广应用到雷达复数域进行信号重构,实现 ISAR的横向高分辨成像;最后对在短 CPI条件下提出的 PH-SL0算法的横向分辨率问题进行了理论分析。仿真和实测数据结果表明,所提算法具有更高的聚焦性能、分辨率以及较好的抗噪性能。     

Compression method for non-stationary signals based on compressive sensing

A new approach to compress non-stationary signals is proposed in this paper. The sparse basis of non-stationary signals is constructed at first and then compressive sensing technique is used to decrease enormously the number of samples in the process. The reconstructed signal can well approximate the original signal in time domain, frequency domain as well as the time-frequency domain. Computer simulation on linear frequency modulated (LFM) signal shows the validity of this novel method.