基于DV-HOP的改进算法及仿真

节点定位技术是无线传感器网络中的关键技术之一。为了提高节点定位精度,在DV-HOP算法的基础上提出两种改进措施:锚节点规划部署和加权算法,改进后的算法统称为加权DV-HOP。在新算法的基础上建立Matlab仿真模型。仿真结果表明加权DV-HOP算法,在相同的锚节点数量和锚节点无线覆盖范围下,节点定位精度约提高10%。     

一种基于虚拟锚节点策略改进的Bounding Box定位算法

针对Bounding Box算法定位误差大、覆盖率低的缺点,提出了一种采用虚拟锚节点策略的改进定位算法。首先未知节点利用其通信范围内的锚节点进行定位;其次,已定位的节点根据升级策略有选择性的升级为虚拟锚节点;最后,无法定位的节点利用虚拟锚节点实现定位。另外,在离散网络模型的基础上,通过建立双半径网络节点模型从而进一步约束了未知节点的位置。理论分析及仿真结果均表明,该算法在显著提高定位覆盖率的同时,有效地提高了定位精度。     

无线传感器网络中DV-Hop定位算法的改进

无线传感器网络中基于无需测距的节点定位算法定位精度不高,一般应用在粗精度定位中。为了提高基于无需测距的DV-Hop算法定位精度,利用最小均方差准则改进算法,通过修改指数值精化平均每一跳距离,提出不同通信半径、不同锚节点覆盖率下的最佳指数值概念,并应用在一种锚节点均匀分布环境中,进一步提高定位精度。MTLAB仿真结果表明,在最佳指数值下,改进的算法在不同锚节点覆盖率、不同通信半径下能提高定位精度,同时不会增加节点能量消耗与硬件成本。     

基于改进单锚节点的无线传感器网络节点定位算法

节点定位是无线传感器关键技术之一,针对固定多锚节点方法定位精度低的缺陷,为了提高无线传感器的定位精度,提出了一种基于改进单锚节点的无线传感器网络节点定位算法(SFOA-SVM)。首先采用单移动锚节点在无线传感器网络中移动,构建无线传感器定位模型的学习样本,然后采用SVM构建节点定位模型,并采用渔夫捕鱼算法模拟渔夫捕鱼行为找到最优SVM参数,最后采用仿真实验测试节点的定位性能。结果表明,相对于其它定位算法,SFOA-SVM提高了无线传感器节点的定位精度,具有一定的实际应用价值。     

基于移动锚节点的无线传感网络节点定位算法

在无线传感网络定位算法中,锚节点位置决定了节点定位精度。为此,提出基于高斯-Markov模型的移动锚节点的节点定位(GM-MAL)算法。GM-MAL算法基于高斯-Markov移动模型,提出自适应锚节点的移动路径规划,通过速度调整策略、垂直平分线策略、虚斥力策略以及虚引力策略规划路径。在定位阶段,将非凸优化问题转化为双凸形式,再利用交替最小算法(AMA)求解,进而获取更短的锚节点移动路径。实验数据表明,引入虚引力策略提高了路径规划精度,覆盖了更多的监测区域。此外,相比于线性算法,GM-MAL的定位精度得到提高。     

基于定位导航系统的改进无线节点空间定位算法

提出了一套针对无线节点空间定位的修正算法.首先提出了一种抽样后求点估计的方法,消除了GPS信息误差造成的影响,然后通过分治法的策略来选取锚节点,保证锚节点的可靠性,普通节点可以通过锚节点与本身进行定位求概率分布,继而修正拓扑,最后通过一个竞争机制来进行有序的网络拓扑坐标修正.实际测试达到了预期的效果.通过该算法,可以在较低功耗下将无线传感网络的定位精度提高,使得无线传感网络能够适应高定位精度的要求.     

分布式无线感知网络节点部署算法研究

在无线感知网络节点部署中,目标区域的覆盖率大小对信号检测的效果具有重要的意义,通过智能优化算法来提高区域覆盖率已成为当前无线感知网络节点部署领域的研究热点之一。为了提高分布式无线感知网络对目标区域内的重点区域的覆盖率和减少冗余感知节点的投放,论文提出了一种分布式无线感知网络节点部署算法。该算法首先通过随机部署满足连通性的少量感知节点后初次工作来定位和估计出重点区域,然后将估计出的重点区域融入到粒子群算法的目标函数和粒子更新方程中实现对感知节点的重新部署,从而更好的优化了重点区域的覆盖率和减少冗余感知节点数量。仿真结果表明,与标准粒子群算法及其他优化算法相比,论文所研究的算法有更高的覆盖率和更低的迭代次数。      

一种基于可移动锚节点的DV-HOP改进定位算法

当节点不均匀分布时,DV-Hop的定位精度较差。针对DV-Hop定位算法的缺陷,提出一种基于移动锚节点的改进DV-Hop定位算法。在网络中引入具有一定移动能力的锚节点,并构建锚节点之间的虚拟力模型,锚节点受到虚拟力作用发生移动,从而均匀的分布于整个网络,修正了DV-Hop对不均与分布网络适应性差的特点。仿真实验表明,与原始算法相比改进后的算法定位精度有较大提高。     

传感器网络节点定位精度的几何稀释分析

通过分析时间到达(Time of Arrival,TOA)算法的定位原理,利用定位精度的几何稀释(Geometrical Dilution ofPrecision,GDOP),描述定位误差与锚节点群几何布局关系,并给出基于测距的算法中GDOP的计算方法。采用蒙特卡罗仿真方法,仿真次数100,设定锚节点的测距误差相同,取锚节点数为3、5,对基于锚节点群内点、外点的未知节点定位误差的归一化GDOP值和GDOP均值求解,结合质心算法原理,验证了自身节点定位精度与确定该节点位置的锚节点的几何关系密切相关,得到锚节点群内点定位精度高的结论。     

基于图论的光传感器节点部署优化研究

图论在合理部署光传感器节点领域取得一定成果,优化部署光传感器节点是延长传感网络使用寿命的有效途径,为此,对光传感器节点进行部署优化。基于图论构建光传感器网络节点模型,将光传感器网络划分成多个网格,每个网格配置一个活动节点、多个冗余节点,计算光传感器节点负载情况;考虑节点负载量,基于萤火虫算法(GSO)部署光传感器节点,将传感器节点等同于萤火虫,覆盖信号强度为荧光素浓度,计算网格内光传感器节点移动概率、判断节点移动方向,实现光传感器节点的优化部署。光传感器仿真部署结果如下:该方法部署的光传感器节点覆盖率广、节点移动距离和较短,有效延长光传感器网络寿命。     

Performance evaluation of sensor deployment using optimization techniques and scheduling approach for K-coverage in WSNs

In the wireless sensor networks, sensor deployment and coverage are the vital parameter that impacts the network lifetime. Network lifetime can be increased by optimal placement of sensor nodes and optimizing the coverage with the scheduling approach. For sensor deployment, heuristic algorithm is proposed which automatically adjusts the sensing range with overlapping sensing area without affecting the high degree of coverage. In order to demonstrate the network lifetime, we propose a new heuristic algorithm for scheduling which increases the network lifetime in the wireless sensor network. Further, the proposed heuristic algorithm is compared with the existing algorithms such as ant colony optimization, artificial bee colony algorithm and particle swarm optimization. The result reveals that the proposed heuristic algorithm with adjustable sensing range for sensor deployment and scheduling algorithm significantly increases the network lifetime.     

Distributed Energy Optimization for Target Tracking in Wireless Sensor Networks

Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrics are calculated by grid exclusion algorithm and Dijkstra's algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combining the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection.     

A Pre-Determined Nodes Deployment Strategy of Two-Tiered Wireless Sensor Networks Based on Minimizing Cost

Nodes deployment is a fundamental factor in determining the connectivity, coverage, lifetime and cost of wireless sensor networks. In this paper, a two-tiered wireless sensor networks consisting of sensor clusters and a base station is considered. Within a sensor cluster, there are many sensor nodes and a relay node. We focus on the deployment strategy for sensor nodes and relay nodes to minimize cost under some constraints. Several means are used. The regular hexagonal cell architecture is employed to build networks. Based on the analysis of energy consumption of sensors and cost of network, an integer programming model is presented to minimize the cost. By the model, number of layers of sensor cluster is determined. In order to balance the energy consumption of sensors on the identical layer, a uniform load routing algorithm is used. The numerical analysis and simulation results show that the waste of energy and cost of wireless sensor networks can be effectively reduced by using the strategy.     

无线传感器网络一种改进的convex定位算法

以convex(凸规划)定位算法为基础,针对range-free定位算法中anchor(已知节点)比例低带来的定位精度低、网络覆盖率低的问题,提出了二跳信息改进定位算法。该算法中,未知节点在通信中加入自身邻居anchor的ID和位置信息并发送给邻居节点,相应的邻居节点从中确定自己的二跳邻居anchor,并利用二跳邻居anchor的二跳通信范围来减小未知节点的可能存在区域,进而提高未知节点的定位精度。仿真表明,二跳信息改进定位算法在anchor节点比例较低情况下能有效提高定位精度,而在anchor节点比例较高时接近原convex算法定位精度,并且网络规模越大这种提高越显著。     

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%。     

A fast handover protocol for 6LoWPAN wireless mobile sensor networks

Node localization is one of the most critical issues for wireless sensor networks, as many applications depend on the precise location of the sensor nodes. To attain precise location of nodes, an improved distance vector hop (IDV-Hop) algorithm using teaching learning based optimization (TLBO) has been proposed in this paper. In the proposed algorithm, hop sizes of the anchor nodes are modified by adding correction factor. The concept of collinearity is introduced to reduce location errors caused by anchor nodes which are collinear. For better positioning coverage, up-gradation of target nodes to assistant anchor nodes has been used in such a way that those target nodes are upgraded to assistant anchor nodes which have been localized in the first round of localization. For further improvement in localization accuracy, location of target nodes has been formulated as optimization problem and an efficient parameter free optimization technique viz. TLBO has been used. Simulation results show that the proposed algorithm is overall 47, 30 and 22% more accurate than DV-Hop, DV-Hop based on genetic algorithm (GADV-Hop) and IDV-Hop using particle swarm optimization algorithms respectively and achieves high positioning coverage with fast convergence.     

无线传感器网络中分阶段的无需测距定位算法

为了提高无线传感器网络中基于无需测距算法定位精度,改进质心算法和DV-Hop算法,定位过程分为两个阶段:第一阶段在最佳通信半径与最佳阈值下,用基于阈值的优先质心算法定位部分节点;第二阶段在最佳通信半径与最佳指数值下用DV-Hop算法定位剩余节点。将算法应用在一种锚节点人工部署环境下,并与其他算法对比。MTLAB仿真结果表明,改进的算法在不增加泛洪次数、计算量和网络硬件成本下能提高定位精度,同时实现100%定位。     

Barrier coverage with line-based deployed mobile sensors

Barrier coverage of a wireless sensor network is a critical issue in military and homeland security applications, aiming to detect intruders that attempt to cross the deployed region. While a range of problems related to barrier coverage have been investigated, little effort has been made to explore the effects of different sensor deployment strategies and mechanisms to improve barrier coverage of a wireless sensor network after it is deployed. In this paper we study the barrier coverage of a line-based sensor deployment strategy and explore how to exploit sensor mobility to improve barrier coverage. We first establish a tight lower bound for the existence of barrier coverage under the line-based deployment. Our results show that the barrier coverage of the line-based deployment significantly outperforms that of the Poisson model when the random offsets are relatively small compared to the sensor’s sensing range. To take advantage of the performance of line-based deployment, we further devise an efficient algorithm to relocate mobile sensors based on the deployed line so as to improve barrier coverage. The algorithm finds barrier gaps and then relocates mobile sensors to fill the gaps while at the same time balancing the energy consumption among mobile sensors. Simulation results show that the algorithms can effectively improve the barrier coverage of a wireless sensor network for a wide range of deployment parameters. Therefore, in wireless sensor network applications, the coverage goal, possible sensor deployment strategies, and sensor mobility must be carefully and jointly considered. The results obtained in this paper will provide important guidelines and insights into the deployment and performance of wireless sensor networks for barrier coverage.