基于二次质心的无线传感器网络定位算法

节点的定位是无线传感器网络中的一种重要技术。提出了一种新的无线传感器网络定位算法——基于二次质心算法的定位算法,与以往的基于三边测量的加权质心方法不同,该算法改进了对未知节点位置的估算方法,一定程度上避免了因多次估算质心而产生的累积误差,提高了定位精度。仿真表明,该算法的定位精度较之前的三边测量方法提高了约19%。     

基于几何学的无线传感器网络定位算法

提出一种基于几何学的无线传感器网络(WSN)定位算法。把网络区域中的节点分为锚节点和未知节点,假设在定位空间中有n个锚节点,由于受到几何学的限制,实际可行的锚节点序列是有限的,因此利用一种几何方法判断锚节点间的位置关系,从而选取最优的锚节点序列,能够更精确地确定未知节点的位置,并且分析了待定位节点的邻居锚节点数量对定位精度的影响。仿真结果表明,与已有的APS(Ad-Hoc positioning system)定位算法相比,该算法可有效地降低平均定位误差和提高定位覆盖度。     

Novel DV-Hop localization algorithm for wireless sensor networks

Many improved DV-Hop localization algorithm have been proposed to enhance the localization accuracy of DV-Hop algorithm for wireless sensor networks. These proposed improvements of DV-Hop also have some drawbacks in terms of time and energy consumption. In this paper, we propose Novel DV-Hop localization algorithm that provides efficient localization with lesser communication cost without requiring additional hardware. The proposed algorithm completely eliminates communication from one of the steps by calculating hop-size at unknown nodes. It significantly reduces time and energy consumption, which is an important improvement over DV-Hop—based algorithms. The algorithm also uses improvement term to refine the hop-size of anchor nodes. Furthermore, unconstrained optimization is used to achieve better localization accuracy by minimizing the error terms (ranging error) in the estimated distance between anchor node and unknown node. Log-normal shadowing path loss model is used to simulate the algorithms in a more realistic environment. Simulation results show that the performance of our proposed algorithm is better when compared with DV-Hop algorithm and improved DV-Hop—based algorithms in all considered scenarios.     

基于无线传感器网络时钟同步的定位算法研究

讨论了在无线传感器定位节点(文中称为锚节点)位置已知条件下,基于信号到达时间差(TDOA)的时间同步算法。根据所有传感器定位节点彼此之间周期性地广播信号,测量信号到达时间(TOA)来估计时间频率的偏移。当信源发送信号,由于传感器钟差造成的初始TDOA误差可以通过一系列补偿计算来减少误差。在一个加性高斯白噪声模型里,通过卡尔曼滤波提高钟差和频差的估计精度。在此基础上改善标签节点的定位精度。     

Multi-target localization algorithm based on adaptive grid in wireless sensor network

The RSS-based multi-target localization has the natural property of the sparsity in wireless sensor networks.A multi-target localization algorithm based on adaptive grid in wireless sensor networks was proposed,which divided the multi-target localization problem into two phases:large-scale grid-based localization and adaptive grid-based localization.In the large-scale grid-based localization phase,the optimal number of measurements was determined due to the sequential compressed sensing theory,and then the locations of the initial candidate grids were reconstructed by applying l_p (0＆lt; p＆lt;1) optimization.In the adaptive grid-based localization phase,the initial candidate grids were adaptively partitioned according to the compressed sensing theory,and then the locations of the targets were precisely estimated by applying l_poptimization once again.Compared with the traditional multi-target localization algorithm based on compressed sensing,the simulation results show that the proposed algorithm has higher localization accuracy and lower localization delay without foreknowing the number of targets.Therefore,it is more appropriate for the multi-target localization problem in the large-scale wireless sensor networks.     

新的无线传感器网络覆盖控制算法

首先,设计了节点自适应传感半径调整算法(AASR,adaptive adjustment of sensing radius),通过节点自适应选择最佳的覆盖范围,有效地进行节点覆盖控制,减少节点能量虚耗,提高覆盖效率。其次,从调整效果、能量消耗和覆盖冗余度3个方面对节点自适应传感半径调整算法进行了模拟实验和分析。仿真结果表明,AASR能够有效提高节点生存时间,减少能量消耗,提高覆盖率。     

Malicious attack-resistant secure localization algorithm for wireless sensor network

In hostile environments, localization often suffers from malicious attacks that may distort transmit power and degrade positioning accuracy significantly for wireless sensor network. A robust semidefinite relaxation secure localiza-tion algorithm RSRSL was proposed to improve the location accuracy against malicious attacks. On the assumption of unknown transmit power, which is undoubtedly approximate to the fact of WSN, a novel secure location probability model was introduced for single-target and multi-target sensor networks, respectively. Taking the computational complexity of RSRSL into account, the nonlinear and non-convex optimization problem was simplified into a semidefinite programming problem. According to the results from both simulations and field experiments, it is clearly demonstrated that the proposed RSRSL has better performance on location accuracy, in contrast to the conventional localization algorithms.     

WSN中基于RSSI的加权质心定位算法的改进

自身节点定位是无线传感器网络的关键技术之一。本文对距离无关定位算法中的质心定位算法进行了分析,在基于RSSI的质心定位算法的基础上提出了一种新的校正RSSI测距值的加权定位算法。测距阶段将信标节点之间的距离和信号强度信息同时考虑在内进行RSSI值校正,权值选择阶段采用了修正传统权重的计算方法,权值取距离倒数之和。通过仿真证明,本文提出的算法相对于传统的加权质心定位算法有明显改进,获得较好的定位精度。     

Path loss model based on exponential water cycle algorithm for wireless sensor network

Wireless sensor networks (WSNs) are frequently employed in the agriculture field to improve the quality and crop yield. The WSN might reduce the quality of the communication link because of the absorption, dispersion, and attenuation through the leaves of plants. Therefore, estimating the path loss due to signal attenuation before WSN deployment is crucial for the smooth operation of the network. In this research paper, three innovative path loss models are defined based on the MATLAB curve fitting tool: polynomial water cycle (PWC), exponential water cycle (EWC), and Gaussian water cycle (GWC) algorithm. Here, the path loss between the router node and the coordinator node is modeled on the basis of the received signal strength indicator (RSSI) and time of arrival (TOA) measurements in a sugarcane field. The correlation coefficient between the RSSI measurement and the distance must be increased to create a precise path loss model. This paper integrates the exponential, polynomial, and Gaussian functions with the water cycle algorithm (WCA) to evaluate the optimal coefficients that would lead to precise path loss models. The performance of the proposed models that determines the optimum linear fit between RSSI and distance is validated using the correction coefficient $R^2$. The results show that the proposed path loss model is superior to existing path loss models. The correlation coefficient $R^2$ of the proposed EWC model is 0.9993, whereas the existing PE-PSO, LNSM, and PSO-Exponential models yield 0.98, 0.87, and 0.93, respectively. Also, the proposed models attain the best mean absolute error (MAE) of 0.2187, 0.2951, and 0.3457 dBm for EWC, PWC, and GWC algorithms, respectively.     

基于Q学习的无线传感网络自愈算法

无线传感网络存在关键区域节点能量消耗过快,节点能量供应有限以及通信链路拥塞等问题,容易造成节点故障和路由破坏。为减小上述问题对网络传输造成的影响,提出一种基于Q学习的无线传感网络自愈算法,通过引入Q学习的反馈机制,动态感知网络的状态信息,当故障发生时,自适应地选择恢复路径,保证数据实时顺利传输。仿真结果表明,该算法降低了错误选择故障或拥塞路径的概率,在故障感知、故障恢复和延长网络寿命等方面,表现出了良好的性能。     

高精确度与高覆盖率的传感器网络定位算法

为了对无线传感器网络中随机分布的节点实施有效定位,针对典型Bounding Box算法的定位准确性与覆盖率较低的不足,提出了一种基于三跳通信环带概念的定位算法C3HR(concentric triple-hop of node communication range).理论分析及仿真结果均表明,该算法优化了对待测节点位置的约束,对于锚节点分布稀疏和不均匀的网络,显著提高了定位的准确性及覆盖率,并能够有效降低节点能耗.     

基于WSN的温室智能灌溉系统软件设计

在温室智能灌溉系统硬件基础上,设计并开发温室智能灌溉系统上位机软件。该软件采用Microsoft Visual Studio 2012和数据库进行设计、开发,具有实时数据查询、历史数据查询以及网络拓扑结构显示等功能。该软件主要实现温室大棚环境信息的实时采集以及ZigBee网络拓扑结构的实时绘制;集成了传感数据的数据融合机制,提高了采集精度;人机接口均采用友好的图形化界面。同时开发了智能农业控制微信公众号,为移动终端获取温室信息、发送控制命令等功能提供便利。测试结果表明,上位机软件界面友好、功能完善、人机接口丰富,可以对各种温室数据进行有效管理,能够满足温室智能灌溉系统的需求。     

基于扇形分簇的无线传感器网络路由算法

无线传感网络中低功耗自适应聚类分簇(LEACH)路由算法等概率选取簇首节点,容易导致整个网络节点能量损耗出现极端化,减少网络生存时间。为此,提出一种针对簇首节点选取和分簇的改进LEACH算法。该算法把整个网络区域分为四个扇形区域,在每个区域内独立进行分簇路由;然后基站根据节点剩余能量和与基站的距离进行簇首节点选择,节点根据簇首节点和基站接收信号强度选择路由方式,以均衡网络能量消耗。仿真结果表明,改进LEACH算法的网络寿命是原有LEACH算法的150%,数据吞吐量提升了3倍。     

基于Zigbee无线传感器网络定位问题的研究

在充分研究现有Zigbee技术的基础上,主要分析了Zigbee无线传感器网络定位系统的通信冲突和定位精度.采用组团协议,选择最先接收到场强信号指示的节点为团长,并提出采用RSSI近似度和选择邻居节点数K选取团员的方法.用以避免定位系统通信冲突并增强系统的健壮性,同时提高定位精度.     

无线传感网中三维节点定位算法优化研究

传统的无线传感网(WSN)三维节点定位研究中,存在定位精度不足,收敛速度慢等问题,特别是当节点存在奇异矩阵时,传统三维节点定位算法的局限性尤为明显。文中针对三维节点定位存在奇异矩阵的情况,提出LMWCA算法。算法在加权质心定位算法的基础上,通过克服奇异矩阵,减小了锚节点自身定位的误差,然后通过修正节点间的权重系数,在一定程度上优化了三维节点的定位精度并提高了收敛速度。仿真结果表明,对比常规的3DLM算法和3DLLSE算法,LMWCA算法在节点存在奇异矩阵的3D定位环境中,定位精度更高,收敛速度更快。     

基于无线传感器网络的K均值算法研究

传统无线传感网一般由大量密集的传感器节点构成,存在节点计算能力、能源和带宽都非常有限的缺点,为了有效节能、延长网络寿命,介绍了基于聚类的K均值算法.该算法通过生成的簇头节点散播到网络的各个区域中,减少了每个区域内通信的能耗和可能会出现的一般节点过早死亡的情况,从而避免了网络对该区城提早失去监控.实验证明,该算法对各节点...     

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。     

基于矢量的无线传感器网络节点定位综合算法

基于DV-hop设计了一种节点的定位综合算法,并将其应用于移动节点.利用节点间估计距离和测量距离的差异构建位置校正矢量;通过改进的粒子群优化方法得到节点的校正步长;节点将其与位置校正矢量的乘积作为自身位置的校正值.通过仿真进行算法验证并分析了复杂度和有效性,结果证明该算法可以将DV-hop的定位误差下降75%,并且适用于稀疏网络.     

基于RSSI无线传感器网络空间定位算法

RSSI测距技术在实际应用环境中,由于多径、绕射、障碍物等因素,无线电传播路径损耗使得定位过程中产生距离误差.通过对三维空间定位过程中产生距离误差区域进行分析,提出了基于RSSI新的空间定位算法ERSS,该定位算法计算简单,定位过程中节点间不增加通信开销,无需硬件扩展.仿真实验表明该算法较普通的基于RSSI的测距方法定位精度和响应时间有了明显的改进,适合在通信开销小、硬件要求低的传感器网络节点上应用.     

传感器网络中基于树的感知器分布优化

无线传感器网络中,感知节点的合理分布对于提高网络的感知能力和信息收集能力以及提高网络的生存期限都具有重要的作用。对于随机分布方式产生的感知网络,可以利用节点的移动性对特定感知节点的位置进行调整从而改善网络整体的感知覆盖范围。为此,利用 Voronoi 图以及相关 Delaunay 三角网定义了传感器网络中以sink 节点为中心的伸展树,并提出了基于遗传算法的感知节点分布优化算法。仿真结果表明,算法能够以较小代价对传感器网络进行节点的分布优化,从而有效提高网络整体的感知能力。