Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).

Specific-Scene Oriented Pedestrian Detection in Visual Sensor Network

Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumption. Our method contains two parts: one is an Enhanced Self-Organizing Background Subtraction (ESOBS) based foreground segmentation module to obtain active areas in the observed region from the visual sensors; the other is an appearance model based detection module to detect the pedestrians from the foreground areas. Moreover, we create our own large pedestrian dataset according to the specific scene in the visual sensor network. Numerous experiments are conducted in both indoor and outdoor specific scenes. The experimental results show that our method is effective.

Deployment Optimization Strategy for a Two-Tier Wireless Visual Sensor Network

Wireless visual sensor network (VSN) can be said to be a special class of wireless sensor network (WSN) with smart-cameras. Due to its visual sensing capability, it has become an effective tool for applications such as large area surveillance, environmental monitoring and objects tracking. Different from a conventional WSN, VSN typically includes relatively expensive camera sensors, enhanced flash memory and a powerful CPU. While energy consumption is dominated primarily by data transmission and reception, VSN consumes extra power onimage sensing, processing and storing operations. The well-known energy-hole problem of WSNs has a drastic impact on the lifetime of VSN, because of the additional energy consumption of a VSN. Most prior research on VSN energy issues are primarily focusedon a single device or a given specific scenario. In this paper, we propose a novel optimal two-tier deployment strategy for a large scale VSN. Our two-tier VSN architecture includes tier-1 sensing network with visual sensor nodes (VNs) and tier-2 network having only relay nodes (RNs). While sensing network mainly performs image data collection, relay network only for wards image data packets to the central sink node. We use uniform random distribution of VNs to minimize the cost of VSN and RNs are deployed following two dimensional Gaussian distribution so as to avoid energy-hole problem. Algorithms are also introduced that optimizes deployment parameters and are shown to enhance the lifetime of the VSN in a cost effective manner.

Sensor planning method for visual tracking in 3D camera networks

Most sensors or cameras discussed in the sensor network community are usually 3D homogeneous, even though their2 D coverage areas in the ground plane are heterogeneous. Meanwhile, observed objects of camera networks are usually simplified as 2D points in previous literature. However in actual application scenes, not only cameras are always heterogeneous with different height and action radiuses, but also the observed objects are with 3D features（i.e., height）. This paper presents a sensor planning formulation addressing the efficiency enhancement of visual tracking in 3D heterogeneous camera networks that track and detect people traversing a region. The problem of sensor planning consists of three issues：（i） how to model the 3D heterogeneous cameras;（ii） how to rank the visibility, which ensures that the object of interest is visible in a camera＇s field of view;（iii） how to reconfigure the 3D viewing orientations of the cameras. This paper studies the geometric properties of 3D heterogeneous camera networks and addresses an evaluation formulation to rank the visibility of observed objects. Then a sensor planning method is proposed to improve the efficiency of visual tracking. Finally, the numerical results show that the proposed method can improve the tracking performance of the system compared to the conventional strategies.

EasiSim: A Scalable Simulator for Wireless Sensor Networks

Traditional simulators have deficiencies of scalability, thus they are not so efficient in running simulations with large-scale networks. In this paper, we present a new simulator, namely EasiSim, specifically for evalu-ating sensor net-works on a large scale. EasiSim is featured by organizing its core components, including nodes, topology and scenario, in a hierarchically structured approach. The hierarchically structured organiza-tion enables nodes to process all the concurrent events in one batch, hence reducing the running time by an order of magnitude. Moreover, we propose a visualization scheme based on a Client/Server model which separates the graphical user interface (GUI) from the simulation engine, and therefore the scalability of the simulator will not be decreased by complex GUI. Extensive simulations show that EasiSim outperforms ns-2 in terms of real running time and memory usage.

An Exploratory Study of Experimental Tools for Wireless Sensor Networks

The objective of this contribution is to present expositive review content on currently available experimental tools/services/concepts used for most emerging field Wireless Sensor Network that has capability to change many of the Information Communication aspects in the upcoming era. Currently due to high cost of large number of sensor nodes most researches in wireless sensor networks area is performed by using these experimental tools in various universities, institutes, and research centers before implementing real one. Also the statistics gathered from these experimental tools can be realistic and convenient. These experimental tools provide the better option for studying the behavior of WSNs before and after implementing the physical one. In this contribution 63 simulators/simulation frameworks, 14 emulators, 19 data visualization tools, 46 testbeds, 26 debugging tools/services/concepts, 10 code-updation/reprogramming tools and 8 network monitors has been presented that are used worldwide for WSN researches.

基于Apriori的视觉传感网络关联数据高效识别方法

在视觉传感网络关联数据识别中,由于数据变化的规律难以捕捉,造成识别结果精确度降低,为此,提出了基于Apriori的视觉传感网络关联数据高效识别方法。通过设计视觉传感网络联合监控系统监控视觉传感网络数据变化情况,获取视觉传感网络关联数据。该系统由处理器单元、数据传输单元等四个模块构成。提取图像中的感兴趣区域完成ROI提取,并通过腐蚀处理等方式对视觉传感网络关联数据实施形态学处理。基于Apriori算法实施视觉传感网络关联数据之间关联规则的挖掘,实现视觉传感网络关联数据的识别。布设实验环境后,使用三个数据集测试该方法的识别性能。测试结果表明,所提方法的微平均查全率均高于94.86%,微平均查准率高于93.02%,最高召回率为98.52%,三个项目的测试值均较高,证明了该方法对视觉传感网络关联数据识别比较准确。

基于混合神经网络的多维视觉传感信号模式分类

传感器采集的数字信号分类精度差,导致关键信息的丢失。为了提高传感数据的可靠性和有效性,提出基于混合神经网络的多维视觉传感信号模式分类方法。结合卷积神经网络(CNN)、循环神经网络(RNN)构建混合神经网络,以更有效地表示多维视觉数据中的特征;其中,卷积神经网络负责对多维的空间信号进行去噪处理并提取特征;循环神经网络负责对时域和频域信号进行特征提取;混合神经网络通过联合训练CNN和RNN各自的参数,以调整其权重,并且结合两者从不同层级提取的特征来实现多维视觉传感信号模式的分类。仿真结果表明,使用所提方法进行分类时,信号光滑度保持在0.9以上,传感信号分类结果与实际结果拟合度较高,有效实现多维视觉传感信号模式分类。

基于计算机网络安全技术的态势感知防御方法

针对传统计算机网络安全态势感知精度不高,感知数据的分析侦测在数据体量庞大的情况下难以保持误差数值的稳定,可视化装置数据处理速度缓慢等问题进行系统方案的设计。在硬件方面基于对于传感器和可视化模块进行改进;该研究在算法技术方面是基于回归算法模型相关的卷积神经网络算法和误差逆传播(error BackPropagation,BP)神经网络算法构建网络安全态势感知的数学模型。通过模拟仿真实验,该系统的威胁检测率能达到96.67%,误警率只有1.10%,且在长时间的工作下也能保持稳定。

融合迁移学习与YOLOv5的安全帽佩戴检测算法

针对现有安全帽佩戴检测算法在检测小目标和密集目标时出现漏检、检测准确度低下等问题,本文提出一种基于改进YOLOv5和迁移学习的安全帽佩戴检测新方法。使用K-means算法聚类出更适合检测任务的先验框尺寸以解决默认先验框不适应任务的问题;在特征提取网络后段引入空间通道混合注意力模块,使模型加强对目标权重的学习,抑制无关背景的权重;改进YOLOv5后处理阶段的非极大值抑制(Non-Maximum-Suppression,NMS)算法的判断度量,减少预测框误删和缺失的现象;采用迁移学习的策略对网络进行训练,克服现有数据集不足的缺陷并提升模型泛化能力;最后提出一种适用于视觉传感网络的安全帽佩戴级联判断框架。实验结果表明改进模型的平均准确率(IOU=0.5)达到了93.6%,与原始模型相比提高了5%,性能优于其他同类算法,提高了施工场景下对安全帽佩戴检测的准确率。

无线视频传感器网络全视角弱栅栏构建算法

针对利用无线视频传感器网络(WVSN)全视角弱栅栏进行入侵行为检测的问题,建立了一种WVSN全视角弱栅栏覆盖几何模型,采用网格化方法将其离散化,把WVSN最优全视角弱栅栏覆盖问题表示成整数规划形式,并证明其是NP-hard的,进而提出了一个网格厚度优先的WVSN弱栅栏覆盖的启发式算法GTPFWBC。为验证该算法的有效性,进行了大量仿真实验。实验结果显示GTPFWBC算法能较好地解决WVSN的全视角弱栅栏覆盖问题,其全视角栅栏构造成功率明显优于W-GraProj、SP和DP1算法,且生成WVSN栅栏的平均节点数分别比W-GraProj和SP大约节省30.6%和59.4%,另外GTPFWBC的时间复杂度远小于W-GraProj、SP和DP1,因此在能量受限、实时性要求较高的WVSN中GTPFWBC算法的效果更佳。

无线视频传感器网络β-QoM目标栅栏覆盖构建算法

针对传统无线视频传感器网络(WVSN)目标栅栏因捕获图像宽度过小而导致的入侵检测失效问题,提出一个能确保捕获不小于β监测质量(β-QoM)的无线视频传感器网络β-QoM目标栅栏覆盖构建(WβTBC)算法。首先,建立视频传感器β-QoM区的几何模型,并证明了所有相邻视频传感器β-QoM区相交的目标栅栏捕获的入侵者图像宽度必大于等于β;然后,在对WVSN最优β-QoM目标栅栏覆盖建立线性规划模型的基础上,证明了它是一个NP-hard问题;最后,为了获得该问题的次优解,设计了一个启发式算法WβTBC。根据传感器间的逆时针β邻居关系建立WVSN的有向图,并采用Dijkstra算法在WVSN中搜索β-QoM目标栅栏。实验结果表明,WβTBC算法能有效构建β-QoM目标栅栏,且分别比螺旋外围外覆盖(SPOC)、螺旋外围内覆盖(SPIC)及目标栅栏构建(TBC)算法节省了23.3%、10.8%和14.8%的传感器节点。此外,在满足入侵检测要求的前提下,β值越小,WβTBC算法构建β-QoM目标栅栏的成功率越高,形成栅栏的节点越少,WVSN进行β-QoM入侵检测的工作周期越长。

基于粒子群优化算法的工业机器人定位抓取控制系统设计

为消除工业机器人实际抓取位置与定位位置之间的误差,实现对机器人抓取行为的有效控制,设计基于粒子群优化算法的工业机器人定位抓取控制系统;根据主要机器部件选型情况建立电气网络结构,再联合视觉传感器与定位控制平台,实现对抓取夹爪行动范围的控制,完成工业机器人定位抓取控制系统硬件的总体设计;遵循粒子群优化算法应用需求,实施G best选取与P best更新,并联合所得计算结果,定义三维坐标系表达式,建立基于粒子群优化算法的定位坐标系;根据跟踪点坐标求解结果,确定控制系数优化处理条件,完善了抓取控制原则,再联合相关应用结构,实现a基于粒子群优化算法的工业机器人定位抓取控制系统的设计;实验结果表明,粒子群优化算法作用下,工业机器人实际抓取位置坐标在X轴、Y轴方向上均准确符合定位位置坐标,实验组定位抓取误差为“0”,有效消除了抓取误差,能够实现对机器人抓取行为的有效控制。

智慧农业视域下面向甘蔗生长环境的监测与预警系统设计

随着农业信息化的发展,传统甘蔗种植模式的弊端不断显现,该文立足于农业现代化建设,以智慧农业监测为研究对象,设计面向甘蔗生长的物联网监测系统。该系统分为4层5模块,分别是感知层、传输层、边缘计算层和应用层,以及数据采集模块、数据存储模块、甘蔗生长条件知识库模块、数据可视化模块和数据预警模块,该种架构可以高效地将采集到的数据进行处理和分析,并根据警报规则实时向用户发出警报,为我国进一步发展甘蔗的种植提供必要的支持。

基于智能传感网络的电气关联故障点识别仿真

由于电的不可见性,电气故障大多没有外表特征,且关联性和相似度较强,识别难度较大。提出基于智能传感网络的电气关联故障点识别方法。采用卡尔曼滤波去除智能传感网络数据的噪声,引入支持向量机,融合网络中多传感器采集的数据,构建电气结构模型,提取电气故障信息特征。建立电气关联故障信息网络,结合可视化图谱获取电气关联故障点位置,识别电气关联故障点。实验结果表明,接地过渡电阻为100Ω且相间过渡电阻为30Ω时,所提方法的故障点定位误差低于0.25m,双端数据不同步情况下,所提方法的故障点定位误差低于1m。验证了所提方法能够有效地优化电气故障定位准确性和故障识别率。

基于深度学习的无人机识别算法研究

无人机的广泛运用,在给人们带来便利的同时,也引发了不良影响。比如,无人机飞入禁飞区引发安全问题,由于不正当的使用侵犯公民的隐私等,因此需要构建一个无人机警察系统,对无人机实施监控,遏制乱飞现象。采用传统的识别方法,灵活性不足,精度也不够高。为此提出一种基于深度学习的无人机识别算法,通过训练一个基于卷积神经网络(CNNs)的学习网络,得出一个高效的识别模型,实现无人机和非无人机间的分类。模型的测试结果表明,该方法具有较高的识别率。

视觉传感器网络协作块压缩感知图像传输方法

JPEG2000等现有图像压缩传输算法虽然压缩性能好,但复杂度高,资源受限的视觉传感器节点很难适应。基于压缩感知理论研究视觉传感器网络环境下的图像传输问题。首先设计主-从节点对模型,分块监测图像;然后研究协作块压缩感知理论,基于图像分块和节点协作来减少测量矩阵的维数,降低压缩感知的计算复杂度;对处在发送端的主从节点,提出尽力而为的压缩感知测量值传输方法,以最大化图像重构质量为目标,尽力传送最多的测量值;而在接收端,汇聚节点依据内积准则来判断重构质量,确定传输终止条件,避免多余的测量值传输,节省传输耗能。实验结果表明,和经典压缩感知相比较,4个视觉传感器节点协作的块压缩感知传输方法将测量矩阵维数减少了2个量级,且有更少的网络耗能。

视频传感器网络覆盖问题

视频监控已经被广泛应用于各种场景,为安全防卫提供了有效信息。综述视频传感器网络中覆盖问题的相关研究。视频传感器网络具有有向传感器网络的一般特征。考虑到监测对象通常有面部朝向属性,视频传感器网络的感知模型又不同于一般的有向传感器网络的感知模型。根据感知模型是否考虑监测目标的朝向,分别介绍视频传感器网络中的点覆盖、区域覆盖和栅栏覆盖问题的典型算法。此外,讨论了目前研究存在的问题以及未来可能的研究方向。

视觉传感器网络中基于RANSAC的顽健定位算法

视觉传感器网络由于节点故障或环境变化将导致节点对目标的观测数据出现错误,而基于最小二乘的多视觉信息融合定位方法将因此造成较大的定位误差。针对此问题提出一种基于集中式RANSAC的顽健定位算法,将错误数据进行筛选剔除,从而提高定位精度,进一步针对集中式RANSAC将会导致单个节点的计算复杂度过高而导致网络节点能耗不平衡问题,提出基于分布式RANSAC的顽健定位算法,从而将大量的迭代计算平均分布在各个节点中并行处理,在保证定位过程顽健性的同时保证了网络的计算能耗平衡性。最后通过实验对no-RANSAC、cen-RANSAC和dis-RANSAC算法的定位性能进行了比较,验证了该算法能够依照预定的概率获得良好的定位结果,并对算法的时间复杂度进行了分析。

基于分布式信源编码的果园视频压缩方法研究

分布式信源编码将编码端的复杂度转移至解码端,可有效解决果园无线视频传感器网络采集节点能量和计算能力受限的问题,但其实际性能还不够理想。为了进一步提高编码的率失真性能,针对果园视频时空相关性强的特点,提出一种基于块自适应的分布式视频编码架构,该架构根据视频内容的相关性选择编码方式,对于WZ帧块采用LDPC编码的方式并利用联合比特平面LDPC算法进行译码。以苹果和葡萄果园视频样本进行测试,结果表明,基于块自适应的分布式视频编码架构能充分利用视频的时空相关性,在相同的量化前提下,相比传统架构苹果和葡萄果园视频样本分别节约了6%~10%及9%~13%的码率。