传感器网络中基于数据融合的栅栏覆盖控制研究

该文采用概率性感知模型,并利用数据融合技术构造虚拟节点来增加节点覆盖区域。在此基础上,提出一种栅栏覆盖控制算法。算法借助分治法构造栅栏,以减少节点间通信开销;并调度传感器使冗余节点睡眠,达到减少能耗和延长网络寿命的目的。分析和实验结果表明,针对所提问题设计的模型和算法可有效增加节点覆盖范围及节点间最大间隔距离,且在栅栏数、网络寿命等性能上均优于基于节点监测数据未融合的栅栏覆盖控制算法。     

基于休眠和扇区的传感器网络拓扑控制算法

拓扑控制算法对节省无线传感器网络节点能量、延长网络寿命具有重要意义。无线传感器网络节省节点能量有两种方法：层次型拓扑结构控制和功率控制；但目前还没有算法将以上两种方法相结合。丈中是结合周期性休眠和拓扑控制的一种新型拓扑控制策略：基于休眠和扇区的无线传感器网络拓扑控制算法，将拓扑控制算法和节点的休眠结合起来。仿真结果表明：算法在不降低网络吞吐量条件下节省了网络能量消耗，有效延长网络寿命。     

智能电网中无线传感器网络的节能节点定位方法

无线传感器网络在智能电网目标检测、监测和定位中得到到越来越多的应用.在无线传感器网络中,能量是一种临界资源,系统寿命通过节能策略得到延长.提出了一种基于能量感知的节点检测和定位方法.这种方法只需要少量的传感器节点信息,由簇头执行定位程序来确定进一步定位所需的传感器子集.这样减少了能量消耗和通信带宽需求,延长了系统寿命.实验结果显示,这种定位方法可以节省高达32％的能量.     

无线传感器网络的WPCS覆盖策略

延长无线传感器网络生存时间的有效方法是让冗余节点进入休眠状态。而现有研究多是基于传感器感知模型为圆形的假设前提。该文集中讨论传感器感知模型非圆时,覆盖与连通性之间的联系,并提出适用性更广的WPCS(Well-Proportioned Coverage Strategy)覆盖策略。WPCS覆盖策略以最小化重叠面积为准则,其目的是最大化网络生存时间。仿真实验表明,WPCS性能优于CCP(Coverage Configuration Protocol),且具有一般性,并能很好地减少工作传感器数目,延长网络寿命。     

无线传感器网络节点密度对网络寿命的影响研究

如何延长网络寿命是无线传感器网络(WSN)的重要课题之一。本文以事件-驱动(Event-Driven)型无线传感器应用模型为基础建立系统模型,研究了节点密度对网络寿命的影响,在理想模型下导出了节点密度对网络寿命影响的算法公式。算法仿真结果表明,网络寿命随着节点密度的增加而增加,并且网络寿命增加的速度比节点密度增加的速度要快。     

面向帕金森病的多模态异构协同感知方法

利用多模态异构传感器组成身体感知网络（body sensing networks）,是连续感知用户日常行为的重要方法之一,但是能源消耗问题一直是限制其发展的主要原因。本文提出了一种面向帕金森病的多模态异构协同感知方法,以降低用户日常行为感知过程中的功耗.该方法将行为感知分为行为识别与状态监测,基于信息论确定识别或监测不同行为的最优传感器组合,进而利用一个多分类器建模的行为识别模型与多个二分类器建模的状态监测模型感知用户行为.通过在公开两个数据集上的实验可以看出,与传统的传感器全部持续工作的方法相比,该方法能够在保证对用户行为有效感知的同时,降低了数据传输和模型计算的功耗（MHEALTH上约40%,PAMAP2上约15%）,从而延长感知网络的寿命,实现长时间持续的用户日常行为感知.     

无线多媒体传感器网络部署控制算法

已有的无线多媒体传感器网络（WMSNs）研究针对传感器放置在目标区域内的情况进行,且没有考虑节点通过云台的转动获得的整个可能感知区域对覆盖率的影响。本文首先针对节点放置点高于目标区域的应用进行研究,综合考虑节点感知区域和可能感知区域,建立了延时和无延时感知模型,并针对不同的感知模型提出了传感器网络部署控制算法（IVPDCA）,算法中改进了虚拟势场算法,定义了节点质量的概念来表示节点间覆盖重叠的大小,建立受力模型,使得节点在合力作用下进行重新部署,同时关闭冗余节点,既延长了网络寿命,又提高了区域覆盖率。仿真结果验证了算法的有效性。     

无线传感器网络自适应目标跟踪节点调度算法

在无线传感器网络中,设计合理的节点调度算法是提高网络感知能力、降低系统能耗的关键。在分析节点能耗模型的基础上,针对移动目标跟踪型网络应用,提出一种高能效的无线传感器网络自适应节点调度算法ANSTT。该算法根据节点对移动目标的感知能力,以及节点的相对剩余能量水平,自动调整节点工作模式。仿真实验表明,ANSTT算法在维持低感知延时、高目标感知率的同时,可有效降低系统能耗,延长网络寿命。     

基于能量与链路稳定性感知的按需路由协议

对于复杂的室外环境,无线传感器网络如何建立可靠的路由,并尽可能延长全网寿命是一个关键问题.在分析总结现有无线Ad Hoe网络路由协议的优缺点的基础上,提出了一种基于能量与链路稳定性感知的多度量的按需路由协议ELSA-AODV.ELSA-AODV协议是AODV协议的增强与扩展,在保证路由可靠性与稳定性的同时,平衡全网的通信负荷与能量,大大延长了全网的工作寿命.通过仿真实验,验证了ELSA-AODV的有效性.     

异构传感器网络中基于闲时能量开销优化的密度控制算法研究

基于闲时能量开销优化目标提出了一种适用于异构传感器网络的密度控制算法(DCA),DCA能寻找到一个闲时能量开销近似最小化的连通覆盖集合,该集合最终映射为活跃节点集合.理论分析和实验数据表明,DCA所生成的拓扑能有效降低网络闲时能量开销,延长了网络的生命剧期.     

Genetic simulated annealing‐based coverage‐enhancing algorithm for multimedia directional sensor networks

Multimedia directional sensor network is one kind of directional sensing systems, whose coverage scheme is quite different from the omnidirectional sensing system. And it is often used in atrocious environmental surveillance, such as nuclear contaminative areas, where people can hardly arrive. In this paper, a genetic simulated annealing‐based coverage‐enhancing algorithm (GSACEA) is proposed as a coverage‐enhancing method in multimedia directional sensor networks. Firstly, GSACEA combines the genetic algorithm and simulated annealing algorithm into an algorithm with new architecture. Then, the proposed GSACEA is applied for the purpose of coverage‐enhancing in the case of directional sensor networks with rotational direction‐adjustable model. Finally, after series actions of genetic simulated annealing, the proposed method can find the approximate solution to the best area coverage rate. And according to the results of simulations, which compared the proposed method with several other classic coverage‐enhancing methods in directional sensor networks, it could be concluded that GSACEA can achieve the highest area coverage rate of directional sensor networks and reduce the iterative computing times simultaneously. Copyright © 2014 John Wiley & Sons, Ltd.     

定向传感器网络中的最小化覆盖间隙和最大化网络生命时间问题的研究

本文研究了定向传感器网络中最小化覆盖间隙和最大化网络生命时间的问题.本文采用的定向感知天线模型,每个传感器有多个感应方向.在无线传感器网络中,最大化网络生命时间和最小化覆盖间隙是两个冲突的目标.为了在两者之间做出权衡,文章研究了在生命时间受约束的情况下最小化覆盖间隙问题(MCBLC)和在覆盖间隙受约束的条件下最大化网络生命时间问题(MLCBC).对于MCBLC问题,我们首先将它模型化为整数规划问题,并提出两个启发式算法(MCBLC-G和MCBLC-G-1).基于MCBLC-G(MCBLC-G-1)算法,利用二分搜索技术得到MLCBC问题的算法.最后,模拟验证了算法的性能.     

基于PSO的无线多媒体传感器网络覆盖增强算法

在无线多媒体传感器网络(Wireless Multimedia Sensor Networks,WMSNs)中,由于节点部署的不合理,往往存在较多的监控盲区,影响了网络的服务质量。为了提高网络的覆盖率,在有向感知模型基础的基础上,提出了一种基于粒子群算法的WMSNs覆盖增强算法PSOCE。PSOCE算法以网络覆盖率为优化目标,以粒子群算法为计算工具,同时对节点的位置与主感知方向进行调整。仿真试验表明,PSOCE算法能够有效地改进WMSNs的覆盖质量,网络的覆盖率能提高6%～12%。     

无线传感网络安全防护路径激光追踪方法

为了提高无线传感网络安全防护能力,需要进行网络安全防护路径设计,提出基于联合节点行为覆盖的无线传感网络安全防护路径激光追踪方法.构建无线传感网络安全防护路径的覆盖关系模型,根据传感器节点与目标节点从属关系进行无线传感网络安全防护的路径空间规划设计,采用最短路径寻优方法进行无线传感网络安全防护路径的激光控制,采用激光扫描...     

一种基于有向感知模型的传感器节点覆盖算法

覆盖控制作为无线传感器网络中的一个基本问题,反映了传感器网络所能提供的“感知”服务质量.优化传感器网络覆盖对于合理分配网络的空间资源,更好地完成环境感知、信息获取任务以及提高网络生存能力都具有重要的意义.针对无线传感器网络方向个数固定的有向感知模型提出一种覆盖增强算法,采用复杂网络社团结构算法划分对网络进行节点子集划分,重新调整节点的感知方向,增强网络的覆盖率,同时有效降低了算法的时间复杂度.     

有向传感器网络覆盖增强问题的贪婪迭代算法

 在有向传感器网络中,可以通过调整节点的感知方向来增强目标区域的覆盖率.提出了有向传感器网络覆盖增强问题的一种贪婪迭代算法,在每次迭代中,调整那些使得覆盖率增加最大的节点的感知方向,重复此迭代过程直至通过调整任一节点的感知方向已不能再增加覆盖率.此外,还提出了一种通过计算几何求解该算法中区域计算问题的方法,以提高计算精度和减少计算时间.大量的仿真实验结果表明,该算法的执行时间较短,收敛速度较快,能够获得比现有算法更高的目标区域覆盖率.     

一种基于网格划分的有向传感网时空覆盖调度算法

针对有向传感器网络中的时空覆盖调度问题进行研究,从有向传感器节点感知模型出发,设计了基于网格划分的网络基本区域生成方法,在此基础上提出了节点最大覆盖调度迭代选择MaxGreedy算法.通过仿真实验验证了网格划分方法的有效性,设计了一系列的时空覆盖算法对比实验,深入评估MaxGreedy算法的性能.对比试验结果表明,MaxGreedy算法可以高效地生成网络的节点调度模式,并在一定程度上提高网络的时空覆盖率.     

Hybrid area exploration–based mobility‐assisted localization with sectored antenna in wireless sensor networks

In common practice, sensor nodes are randomly deployed in wireless sensor network (WSN); hence, location information of sensor node is crucial in WSN applications. Localization of sensor nodes performed using a fast area exploration mechanism facilitates precise location‐based sensing and communication. In the proposed localization scheme, the mobile anchor (MA) nodes integrated with localization and directional antenna modules are employed to assist in localizing the static nodes. The use of directional antennas evades trilateration or multilateration techniques for localizing static nodes thereby resulting in lower communication and computational overhead. To facilitate faster area coverage, in this paper, we propose a hybrid of max‐gain and cost‐utility–based frontier (HMF) area exploration method for MA node's mobility. The simulations for the proposed HMF area exploration–based localization scheme are carried out in the Cooja simulator. The paper also proposes additional enhancements to the Cooja simulator to provide directional and sectored antenna support. This additional support allows the user with the flexibility to feed radiation pattern of any antenna obtained either from simulated data of the antenna design simulator, ie, high frequency structure simulator (HFSS) or measured data of the vector network analyzer (VNA). The simulation results show that the proposed localization scheme exhibits minimal delay, energy consumption, and communication overhead compared with other area exploration–based localization schemes. The proof of concept for the proposed localization scheme is implemented using Berkeley motes and customized MA nodes mounted with indigenously designed radio frequency (RF) switch feed network and sectored antenna.     

Leveraging data fusion to improve barrier coverage in wireless sensor networks

Intruder detection and border surveillance are amongst the most promising applications of wireless sensor networks. Barrier coverage formulates these problems as constructing barriers in a long-thin region to detect intruders that cross the region. Existing studies on this topic are not only based on simplistic binary sensing model but also neglect the collaboration employed in many systems. In this paper, we propose a solution which exploits the collaboration of sensors to improve the performance of barrier coverage under probabilistic sensing model. First, the network width requirement, the sensor density and the number of barriers are derived under data fusion model when sensors are randomly distributed. Then, we present an efficient algorithm to construct barriers with a small number of sensors. The theoretical comparison shows that our solution can greatly improve barrier coverage via collaboration of sensors. We also conduct extensive simulations to demonstrate the effectiveness of our solution.     

Minimal exposure path algorithms for directional sensor networks

As a fundamental problem of wireless sensor networks, the minimal exposure path problem corresponding to the sensor network's worst‐case coverage plays an important role in the applications for detecting intrusions. However, most existing works about minimal exposure path are based on omnidirectional sensors. In contrast, this paper studies the minimal exposure path problem for two different types of directional sensing models: the binary sector model and directional sensitivity model. For the binary sector model, we construct a special Voronoi diagram, called sector centroids‐based Voronoi diagram, to transform the minimal exposure path problem from a continuous geometric problem into a discrete geometric problem. By using the sector centroids‐based Voronoi diagram, we develop an approximate algorithm to find the minimal exposure path in the sensors deployment field. For the directional sensitive model, we formulate the minimal exposure path problem by using two sensing intensity functions: all‐sensor intensity function and maximum‐sensor intensity function and then generate two weighted grids to convert the minimal exposure path problem into two discrete geometric problems. On the basis of the aforementioned weighted grids, we also develop two approximation algorithms to find the minimal exposure path for the directional sensitive model. We conduct extensive experiment simulations to validate and evaluate our proposed models and algorithms. Copyright © 2012 John Wiley & Sons, Ltd.