Sensors Grouping Model for Wireless Sensor Network

The grouping of sensors is a calculation method for partitioning the wireless sensor network into groups, each group consisting of a collection of sensors. A sensor can be an element of multiple groups. In the present paper, we will show a model to divide the wireless sensor network sensors into groups. These groups could communicate and work together in a cooperative way in order to save the time of routing and energy of WSN. In addition, we will present a way to show how to organize the sensors in groups and provide a combinatorial analysis of some issues related to the performance of the network.

A target group tracking algorithm based on a hybrid sensor network

Traditional tracking algorithms based on static sensors have several problems. First, the targets only occur in a part of the interested area; however, a large number of static sensors are distributed in the area to guarantee entire coverage, which leads to wastage of sensor resources. Second, many static sensors have to remain in active mode to track the targets, which causes an increase of energy consumption. To solve these problems, a target group tracking algorithm based on a hybrid sensor network is proposed in this paper, which includes static sensors and mobile sensors. First, an estimation algorithm is proposed to estimate the objective region by static sensors, which work in low-power sensing mode. Second, a movement algorithm based on sliding windows is proposed for mobile sensors to obtain the destinations. Simulation results show that this algorithm can reduce the number of mobile sensors participating in the tracking task and prolong the network lifetime.

Wireless Sensor Network Routing Based on Sensors Grouping

Due to the limited communication range of WSN, the sensor is unable to establish direct connection to the data collection station, therefore the collaborative work of nodes is highly necessary. The data routing is one of the most fundamental processes exploring how to transmit data from the sensing field to the data collection station via the least possible number of intermediate nodes. This paper addresses the problem of data routing based on the sensors grouping;it provides a deep insight on how to divide the sensors of a network into separate independent groups, and how to organize these independent groups in order to make them work collaboratively and accomplish the process of data routing within the network.

A target group tracking algorithm for wireless sensor networks using azimuthal angle of arrival information

In this paper, we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network. Since a group of targets moves collectively and is restricted within a limited region, it is not worth consuming scarce resources of sensors in computing the trajectory of each single target. Hence, in this paper, the problem is modeled as tracking a geographical continuous region covered by all targets. A tracking algorithm is proposed to estimate the region covered by the target group in each sampling period. Based on the locations of sensors and the azimuthal angle of arrival （AOA） information, the estimated region covering all the group members is obtained. Algorithm analysis provides the fundamental limits to the accuracy of localizing a target group. Simulation results show that the proposed algorithm is superior to the existing hull algorithm due to the reduction in estimation error, which is between 10% and 40% of the hull algorithm, with a similar density of sensors. And when the density of sensors increases, the localization accuracy of the proposed algorithm improves dramatically.

DEGREE:一种基于Delaunay三角的任意群目标外形识别方法

集群目标相比单一甚至多目标表现出复杂时变集群特性,其外形估计与评价颇具挑战性。针对任意形状的集群目标外形估计与评价难题,本文提出了一种基于数据驱动的多传感器集群目标群形状建模与识别方法,以及一种群目标外形拟合度评判指标。所提算法由三个部分组成:首先,采用信息洪泛(Flooding)方法实现强连接的多传感器对视场中目标信息的采集与传播;其次,采用密度峰值聚类实现观测数据的聚类;最后,采用改进Delaunay三角网络算法实现群目标外形的拟合。所提群外形拟合度指标可用于对群目标外形估计准确度定量评价。通过与超曲面、随机矩阵等经典方法进行比较,证实了所提出算法的有效性和可靠性。

Group Target Tracking in WSN Based on Convex Hulls Merging

When a mass of individual targets move closely, it is unpractical or unnecessary to localize and track every specific target in wireless sensor networks (WSN). However, they can be tracked as a whole by view of group target. In order to decrease the amount of energy spent on active sensing and communications, a flexible boundary detecting model for group target tracking in WSN is proposed, in which, the number of sensors involved in target tracking is adjustable. Unlike traditional one or multiple individual targets, the group target usually occupies a large area. To obtain global estimated position of group target, a divide-merge algorithm using convex hull is designed. In this algorithm, group target’s boundary is divided into several small pieces, and each one is enclosed by a convex hull which is constructed by a cluster of boundary sensors. Then, the information of these small convex hulls is sent back to a sink. Finally, big convex hull merged from these small ones is considered as the group target’s contour. According to our metric of precision evaluation, the simulation experiments confirm the efficiency and accuracy of this algorithm.

Location Data Fusion Based on Group Consensus

A new method of multi sensor location data fusion is proposed.The method is based on group consensus approach, which constructs group utility function (or its density) based on uncertainty of each sensor, and the location estimation is obtained based on the group utility function (or its density). The simulation results show that the method is better than those of mean and median estimation, and outlier and sensor failure can not affect the location estimation.

WSNs中基于占空比间隔优化的延迟约束路由算法

针对WSNs中现有路由算法存在的各种不足,提出了一种基于占空比间隔优化的延迟约束路由算法;具体来说,在提出的算法中,首先,将端到端延迟分布估计为占空比间隔和潜在转发器数量的函数,在给定的网络模型和参数下,其分布可以近似地估计;然后,选择满足延迟约束成功率要求的占空比间隔最大值,每个节点独立地调度其休眠和唤醒时间,发送端节点将数据包转发给潜在转发器中最先唤醒的节点,从而确保数据包以要求的概率即DCSR到达接收器,同时最大化占空比间隔;仿真实验结果表明,提出的路由算法不仅能够满足要求的DSCR,并在ETE延迟、数据包交付率和实际得到的DCSR方面都优于现有的先进算法。

An Adaptive Key Management Framework for the Wireless Mesh and Sensor Networks

Wireless sensor networks (WSNs) and wireless mesh networks (WMNs) are popular research subjects. The interconnection of both network types enables next-generation applications and creates new optimization opportunities. Currently, plenty of protocols are available on the security of either wireless sensor networks or wireless mesh networks, an investigation in peer work underpins the fact that neither of these protocols is adapt to the interconnection of these network types. The internal cause relies on the fact that they differ in terms of complexity, scalability and network abstraction level. Therefore, in this article, we propose a unified security framework with three key management protocols, MPKM, MGKM, and TKM which are able to provide basic functionalities on the simplest devices and advanced functionalities on high performance nodes. We perform a detailed performance evaluation on our protocols against some important metrics such as scalability, key connectivity and compromise resilience, and we also compare our solution to the current keying protocols for WSNs and WMNs.

《智能家电用金属氧化物MEMS气体传感器》团标解读

金属氧化物MEMS气体传感器是近几年兴起的一种新型传感器,具有尺寸小、功耗低和易于集成的特点,已经被广泛的应用到了日常生活之中。本文分析了金属氧化物MEMS气体传感器检测认证现状并对团体标准《智能家电用金属氧化物MEMS气体传感器》进行解读,为广大消费者和相关企业提供检测认证方面的指导。

无线传感网络混合密钥安全分簇方法

无线传感网络安全性在很大程度上依赖簇首,对该节点的维护和分配对其安全性具有重要意义。为此,基于混合密钥分配提出了一种无线传感网络安全分簇方法。该方法在建立无线传感网络模型和攻击者模型的基础上,根据赋权能力将无线传感网络节点分为高级节点和普通节点,并利用混合密钥分配技术,分别提升两类节点的安全性能。通过将节点映射至粒子传感器的工作区域内,实现无线传感网络安全分簇。测试结果表明,所提方法“死亡节点”留存时间低于0.2 s,数据包传输总量高于900 bit。

一种改进的体素化邻近点搜索算法

针对多传感器同时建图与定位SLAM(simultaneous localization and mapping)系统,在室外大面积特征过多环境中,因为特征不匹配而导致的精度误差、系统缺失鲁棒性等问题,提出一种改进的体素化邻近点搜索(voxelized generalized iterative closest point)算法。首先利用激光-惯导-视觉各传感器分别对不同环境感知可以优势互补的特点,提出一种特征可信性筛选方法,给予系统一个精确的初始猜测。然后将视觉特征子集通过深度信息与点云数据关联,通过加入视觉约束筛选可观测性高的体素化的目标点云群,在减少计算复杂度的情况下,使定位建图更加精确。仿真实验证明,当运用此算法的多传感器融合SLAM系统在特征点云较多的环境中建图时,与LVI-SAM系统对比定位误差减少12.335%;在运行线速度超过10 m/s时,提升了系统的鲁棒性,具有较强的可行性。

基于平衡生成对抗网络的海洋气象传感网入侵检测研究

针对海洋气象传感网(MMSN)环境下海洋移动终端资源受限和网络流量不平衡导致网络入侵难以被准确检测的问题,提出了一种基于移动边缘计算的MMSN物理架构和一种基于平衡生成对抗网络的入侵检测模型。首先,利用改进的平衡生成对抗网络对不平衡数据进行数据增强。其次,利用基于分组卷积的轻量级网络对入侵数据进行分类。最后,通过计算机仿真证明了所提模型较传统数据增强模型具有更高识别各类攻击的能力,尤其是针对MMSN的少数类样本攻击。

一种新型的放电源空间定位用特高频传感器

采用特高频传感器阵列对放电源进行空间定位可极大地提高电力设备状态检修的效率,该文在研究确立空间定位用特高频传感器性能要求的基础上,基于单极天线理论和曲流技术,设计了一种球面锥形结构的新型特高频传感器。通过仿真及实验检测,对该传感器的性能进行了系统的研究,并对影响传感器各项性能的因素进行了探讨。实验室搭建的局放检测定位平台对传感器阵列的空间定位性能进行了验证。实验结果表明:曲流技术的应用可实现传感器的小型化,本文研制的传感器在500~2 000MHz带宽内具有驻波比低、全向、高灵敏度、及群时延一致性好等特点,基于特高频传感器阵列的定位系统能够对放电源进行准确定位,方位角误差小于2°,能够满足局放源空间定位的要求。

基于多传感器融合的自主移动机器人测距系统

针对自主移动机器人的路径规划需求,设计了红外传感器与超声波传感器融合的测距系统;首先使用红外传感器信号对移动范围内的障碍物进行探测,确定其位置界限;然后使用超声波传感器信号进行补充,并对两类测距数据用证据理论进行融合,提高了定位精度;为了避免多超声波传感器之间的回波干扰,设计了多超声波传感器分组循环发射测距系统,避免了因使用单个传感器进行多次测量而降低系统的实时性和产生信号串扰的缺陷;实验结果表明,该方法使移动机器人的测距相对误差平均降低了30.2%。

运动状态下多传感器群目标精细航迹起始算法

为解决多传感器群内目标精细航迹起始的难题,提出一种基于运动状态的集中式多传感器群目标精细航迹起始算法,算法首先基于循环阈值模型和群中心点完成群的预分割、预互联,然后将预互联成功的群按传感器不同分成多个子群,基于非抢占式修正逻辑法和同状态航迹子群获取模型剔除单传感器形成的虚假航迹,并基于多传感器同状态群关联模型消除各传感器虚假的同状态航迹子群,最后基于加权法实现同状态关联群内航迹的精细互联及合并。仿真数据表明,与分布式多传感器修正逻辑法、基于聚类和Hough变换的集中式多传感器多编队航迹起始算法相比,算法在起始真实航迹、抑制虚假航迹及杂波鲁棒性等方面综合性能更优。

基于时域参数的局部放电特高频传感器性能表征方法

特高频(ultra-high frequency,UHF)传感器的时域输出信号对局部放电宽带检测系统的检测和定位结果具有显著影响,因此评估传感器检测性能时,若能从其时域输出特性出发,将更贴近于宽带检测系统的工程需求。现有的有效高度表征体系不适用于具有非平坦群时延的特高频传感器,在描述传感器时域输出特性时具有局限性。提出了一种基于时域参数的特高频传感器的性能表征方法,通过构造解析脉冲响应函数,从中提取包络峰值、包络宽度和振荡时间三种时域参数,用于描述传感器在局部放电脉冲信号下时域输出波形的峰峰值、上升时间和振荡。设计了有效高度幅频特性与相频特性的测试系统并给出时域参数的测试方法。通过比较两种传感器的时域参数测试结果和实际检测结果对该表征方法进行了检验。研究结果表明:该表征方法能有效地反映UHF传感器的时域输出特性,弥补了现有方法的不足。

基于最优分组原则的多传感器分组加权融合算法

多传感器对某一状态进行测量时,将多传感器分组后进行加权融合处理,可实现对待测状态较为准确的估计,但多传感器的组合方式不同,估计效果的差异很大。本文针对分组融合算法中如何选择传感器的分组组合方式进行了研究,依据分组中传感器的个数和各组间传感器测量方差的大小关系,提出了使融合估计值最优的分组原则。并将这些原则加以数学抽象,从理论上证明了基于最优分组原则的分组融合算法的最小方差特性。最优分组原则为评价和选择传感器的分组组合方式提供了一个理论依据。实例分析表明:该分组原则极大地缩小了寻找最优分组组合方式的范围,有效地解决了在如何分组问题上的盲目性,从而使得多传感器测量时的在线优化分组得以实现。

有害气体监测中无线传感网络节点的设计与实现

本文详细介绍了无线传感网络节点的软、硬件设计,并给出了一个新颖的有毒气体SO2和H2S的浓度检测电路实例。无线传感网络节点硬件上采用了高性能的ATmega128处理器和CC2420射频芯片,软件上基于有限状态机采用C语言设计了ZigBee协议栈。无线传感网络具有分布式处理、自组织、低功耗和以数据为中心的特点,非常适合有害气体监测领域的应用。

液压挖掘机智能化设计研究

液压挖掘机技术发展到今天已极为成熟,但其智能化及自动化技术发展空间还极为广阔。对于液压挖掘机挖掘工作,可通过自动化技术实现挖掘机自动作业,省去操作人员繁琐的操作工序,减少劳动工作量,同时可提高效率,且具有比人更高的操作精度。从液压挖掘机智能控制系统和三维立体智能视觉系统构建、液压挖掘机智能挖掘、液压挖掘机智能装载、液压挖掘机智能防倾覆5个方面对挖掘机的智能化予以设计研究,可为今后的液压挖掘机的智能化设计提供一定参考价值。