无线传感网络布局的虚拟力导向微粒群优化策略

无线传感网络通常由固定传感节点和少量移动传感节点构成,动态无线传感网络布局优化有利于提高无线传感网络覆盖率和目标检测概率,是无线传感网络研究的关键问题之一.传统的虚拟力算法在优化过程中容易受固定传感节点的影响,无法实现全局优化.本文结合虚拟力算法和微粒群算法,提出一种面向无线传感网络布局的虚拟力导向微粒群优化策略.该策略通过无线传感节点间的虚拟力影响微粒群算法的速度更新过程,指导微粒进化,加快算法收敛.实验表明,虚拟力导向微粒群优化策略能快速有效地实现无线传感节点布局优化.与微粒群算法和虚拟力算法相比,虚拟力导向微粒群优化策略不仅网络覆盖率高,且收敛速度快,耗时少.     

移动传感器网络非均匀事件区域节点部署优化

针对移动传感器网络中热点事件监测场景,研究传感器节点的快速优化部署策略.首先假定事件随机产生,针对事件优先模型及节点感知误差函数推导基于Voronoi剖分时感知误差最小,然后定义节点有效覆盖权值,证明了当所有节点有效覆盖权值一致时,整个网络覆盖效能将达到最大.结合虚拟力及节点有效覆盖权提出一种分布式优化部署算法SDOA(Sparse Deployment Optimization Algorithm),其在保证覆盖能效最大化时保证网络连通性.最后仿真比较了本文提出部署策略能够快速有效实现对热点区域部署,并保证较高的覆盖效能.     

基于改进虚拟力算法的水质传感器部署

文中设计了基于网格划分的改进虚拟力算法。将水底表面划分成三维网格,定义的虚拟力包括邻居节点的作用力、网格对节点的吸引力、障碍物的斥力和目标覆盖区域边界斥力,以及补偿虚拟力,综合这些虚拟力使传感器节点移动,求得最佳位置。通过理论论证和仿真实验,该算法能够有效提高水质传感器三维覆盖率和均匀性,节约节点数量和部署成本。     

面向目标跟踪的物联网感知层节点部署优化研究

针对目标跟踪物联网感知层节点动态部署的特点,在人工鱼群算法和虚拟力算法的基础上,设计了融入虚拟力影响的人工鱼群控制算法,给出了算法的参数自适应调整策略,该算法利用节点间的虚拟力来影响人工鱼的觅食行为和追尾行为,指导人工鱼群的进化过程,加快算法的收敛性。仿真实验结果显示,算法能快速有效地实现无线传感器网络节点的部署优化,与人工鱼群算法和虚拟力算法相比,该算法不仅全局寻优能力强,且收敛速度快,可有效提高网络覆盖率,优化网络性能。     

基于Voronoi的无线传感器网络覆盖控制优化策略

针对无线传感器网络运行状态中存在覆盖空洞的问题,提出了一种基于Voronoi有效覆盖区域的空洞侦测修复策略。该策略以满足一定网络区域覆盖质量为前提,在空洞区域内合理增加工作节点以提高网络覆盖率为优化目标,采用几何图形向量方法对节点感知范围和Voronoi多边形的位置特性进行理论分析,力求较准确地计算出空洞面积,找寻最佳空洞修复位置,部署较少的工作节点保证整个网络的连通性。仿真结果表明,该策略能有效地减少网络总节点个数和感知重叠区域,控制网络中冗余节点的存在,同时其收敛速度较快,能够获得比现有算法更高的目标区域空洞修复率,实现网络覆盖控制优化.     

蝙蝠算法优化神经网络的无线传感器网络数据融合

为了降低无线传感器网络数据间的冗余性,提高数据传输量和降低通信能耗,提出一种蝙蝠算法优化神经网络算法的数据融合策略.首先每个簇首节点接收该区域的各传感器节点检测到的数据,然后采用蝙蝠算法优化BP神经网络进行数据融合,最后采用仿真实验对其性能进行测试.仿真结果表明,本文算法节省了感知节点的能量消耗,延长了无线传感器网络的生命周期时间,提高数据融合的精度.     

一种基于网格划分的水下传感器网络多维优化部署策略

针对水下无线传感器网络节点的部署问题,提出一种基于网格划分的多维优化部署策略。首先,将所需探测水下环境划分成相同规格的网格;然后,综合考虑网络节点数量、网络覆盖率、网络冗余度、网络生存率等指标,构建多目标优化数学模型;最后,采用遗传算法对多维优化部署策略加以实现并进行仿真分析。结果显示：所提策略能够有效地减少部署节点数量,提高网络覆盖率和生存效率,降低网络能耗。     

基于无线传感器网络的移动机器人导航方法

为移动机器人在无定位信息的无线传感器网络(WSN)中选择路程短、代价低的导航路径,提出了一种基于无线传感器网络的移动机器人导航方法,包括全网络导航路径规划和局部节点趋近算法。该方法通过结合各节点传感器数据,构造代价函数,在网络中建立伪梯度势场,为移动机器人规划最优路径;移动机器人通过探测接收信号强度指示(RSSI),逐一趋近该路径上的传感器节点到达目标节点。仿真结果表明,该方法能够根据移动机器人的导航要求,引导移动机器人迅速沿最优路径到达目标节点。     

信号强度和运动向量结合的无线传感器网络移动节点定位

无线传感器网络的定位是近年来无线传感器网络研究的重要课题.本文首先介绍了无线传感器网络的来源、重要性以及无线传感器网络定位的分类.然后提出了一种全新定位算法,信号强度和运动向量结合的无线传感器网络移动节点定位,简称SSMV算法,在外围布置四个锚节点,得用信号强度和未知节点在运动中向量的变化,对锚节点在内的未知节点进行定位,并对该算法进行了仿真和总结.通过与凸规划法进行比较,仿真结果表明,该算法有更高的定位精度.     

无线传感器网络中基于效用模型的分布式功率控制机制

本文将效用模型引入无线传感器网络的功率控制设计中,提出了一种基于效用模型的分布式功率控制机制(简称UMDPC).该机制建立了网络中所有传感器节点的功率与效用模型的对应关系,将链路可靠性、网络能耗归纳到统一的网络效用优化框架中,并证明该效用优化问题是凸优化问题,构造基于对偶分解的分布式的优化算法,获得网络效用最大化条件下各节点的优化发射功率.最后,通过模拟实验对所提机制及其实现算法的性能进行比较和评价.实验结果表明,本文所提机制最大化了网络的效用,提高了网络的能量利用效率.     

Sensor Deployment Strategy for Detection of Targets Traversing a Region

Sensing devices can be deployed to form a network for monitoring a region of interest. This paper investigates detection of a target traversing the region being monitored by using collaborative target detection algorithms among the sensors. The objective of the study is to develop a low cost sensor deployment strategy to meet a performance criteria. The paper defines a path exposure metric as a measure of goodness of deployment. It then gives a problem formulation for the random sensor deployment and defines cost functions that take into account the cost of single sensors and the cost of deployment. A sequential sensor deployment approach is then developed. The paper illustrates that the overall cost of deployment can be minimized to achieve the desired detection performance by appropriately choosing the number of sensors deployed in each step of the sequential deployment strategy.     

Coverage analysis for target localization in camera sensor networks

Camera sensor networks have recently emerged as a critical research topic. In this paper, we investigate the coverage problem for camera sensor networks. Specially, compared to the coverage problem for target detection which has been intensively studied, this paper studies the coverage problem from the perspective of target localization. We first propose a novel localization‐oriented sensing model based on the perspective projection of the camera sensors. Then, under the random uniform deployment strategy, we analyze how the probability of the localization‐oriented coverage (L‐coverage for short) changes with the sensors number and the parameters of the proposed sensing model. Finally, we conduct extensive simulations to validate our model and theoretical analysis about L‐coverage probability. The obtained results show that our scheme can be effectively applied for practical scenarios. Copyright © 2011 John Wiley & Sons, Ltd.     

Sparse deployment scheme in mobile sensor networks with prioritized event area

Sensor deployment is a fundamental issue in Internet of things. In this paper, It addressed the problem of how to effective deployment for prioritized event area in mobile sensor networks with low density sensors, which coverage completely cannot be realized. When event areas have been detected, the problem how to make mobile nodes moved to a suitable place with effective deployment is studied. Firstly, a coverage weight by priority function of event area is defined. It deduced conclusion that coverage effectiveness have maximization as if all of nodes coverage weight are identical. A nodes deployed strategy have presented that combined coverage weight and virtual force. Finally, some simulations shown that the proposed deployment algorithm are accurate and effective. Copyright © 2015 John Wiley & Sons, Ltd.     

一种高效覆盖的水下传感器网络部署策略

传感器节点的部署直接关系到水下传感器网络的成本和性能.考虑到传感器节点间具有很强的协同能力,该文提出一种基于检测融合的部署策略.采用Neyman-Pearson准则融合单元网格内所有传感器节点的检测信息,实现正方形和正三角形两种单元网格的高效覆盖,进而分别给出针对两种单元网格的监测区域网格划分方法,从而确定监测区域需要的传感器节点数量以及放置的具体位置.通过仿真实验验证了该部署策略的有效性.结果表明,与不采用检测融合时相比,降低了传感器节点冗余度.使用相同数量的传感器节点,新的部署策略能够在保证一定感知质量的基础之上获得更大的覆盖范围.     

A novel change-detection scheduler for a network of depth sensors

In many monitoring applications such as smart home and surveillance, deployment of multiple depth sensors increases monitoring area and offers better occlusion handling which is not sensitive to illumination condition in comparison with RGB sensors. However, multiple sensors also increase the volume of data associated with signal processing alongside the associated computational complexity and power consumption. In order to address these drawbacks, this paper proposes a novel change detection algorithm that can be used as a part of a sensor scheduler in a centralized (e.g. star) network configuration. Initially, each sensor in the network performs a unique single scan of the common environment in order to detect any incremental changes in the sensed depth signal. This initial change detection is then used as a basis for several follow-up tasks such as foreground segmentation, background detection, target detection, and tracking for monitoring tasks. Here, instead of processing a complete depth frame, we proposed to utilize a collection of 1D scans of the depth frames. A confidence function is defined that can be used to estimate the reliability of the detected changes in each sensor and to reduce any false positive events which can be triggered by the noise and outliers. Analysis of the proposed confidence function is carried out through performance analysis in the presence of sensor noise and other parameters which can affect the reliability of the sensed data of each sensor. Finally, a score function is defined based on the confidence of the detected parameters and sensor resolution in order to rank and match sensors with the associated objects to be tracked. It results in tracking target(s) by a sensor (or sensors) that offer a high tracking score. This approach offers many advantages such as decreasing the overall system power consumption by placing the sensors with a low confidence value on standby mode and reducing the overall computational overheads.     

A Survey of Energy-Efficient Scheduling Mechanisms in Sensor Networks

Sensor networks have a wide range of potential, practical and useful applications. However, there are issues that need to be addressed for efficient operation of sensor network systems in real applications. Energy saving is one critical issue for sensor networks since most sensors are equipped with non-rechargeable batteries that have limited lifetime. To extend the lifetime of a sensor network, one common approach is to dynamically schedule sensors' work/sleep cycles (or duty cycles). Moreover, in cluster-based networks, cluster heads are usually selected in a way that minimizes the total energy consumption and they may rotate among the sensors to balance energy consumption. In general, these energy-efficient scheduling mechanisms (also called topology configuration mechanisms) need to satisfy certain application requirements while saving energy. In this paper, we provide a survey on energy-efficient scheduling mechanisms in sensor networks that have different design requirements than those in traditional wireless networks. We classify these mechanisms based on their design assumptions and design objectives. Different mechanisms may make different assumptions about their sensors including detection model, sensing area, transmission range, failure model, time synchronization, and the ability to obtain location and distance information. They may also have different assumptions about network structure and sensor deployment strategy. Furthermore, while all the mechanisms have a common design objective to maximize network lifetime, they may also have different objectives determined by their target applications. A preliminary was presented in BROADNETS 2006 [29]     

基于进化优化的移动感知节点部署算法

 移动传感器网络中节点部署优化直接影响到网络的能量消耗、对目标区域监控的性能及整个网络的生命周期.本文从网络覆盖和能量消耗两个方面,采用多目标优化对节点部署问题建模,并从集中式角度给出了节点部署问题的遗传算法求解过程.针对一类初始中心部署模型进行实验验证,并和基于向量的算法(VEC)、基于维诺图的算法(VOR)及基于边界扩张虚拟力算法(BEVF)进行性能对比,证明了该算法在大多数情况下可使传感器网络对目标区域的覆盖率最大化,同时保证了网络的连通和网络能耗最小,进而延长了网络的生命周期.     

Research on mobile strategy of anchor node based on weighted virtual force model

The existing mobility strategy of the anchor node in wireless sensor network (WSN) has the shortcomings of too long moving path and low positioning accuracy when the anchor node traverses the network voids area.A new mobility strategy of WSN anchor node was proposed based on an improved virtual forces model.The number of neighbor nodes and the distance between the neighbor nodes to the anchor nodes were introduced as their own dense weight attributes.The unknown nodes intensity was used as weights to improve the traditional virtual force model.Meantime the distance-measuring error ε was taken into account.The optimal distribution,direction selection,shift step length and fallback strategy of anchor node could be analyzed by the trilateration.Using the number of virtual beacon received by the unknown node and the distance between the unknown node to the anchor node calculate the virtual force.Then according to the virtual force,the direction was chosen and the anchor nodes were moved.Simulation experiments show that the strategy can make the anchor nodes move according to the specific circumstances of unknown node distribution.It has a high positioning accuracy and strong adaptability.It can successfully shorten the path of the anchor node movement and reduce the number of virtual beacon.Moreover it can effectively avoid the anchor node to enter the network voids area and reduce the number of collinear virtual anchor nodes.