利用移动内点来修复传感器网络空洞的算法

针对目标区域中存在感知空洞问题,提出了一种空洞修复准则。在此基础上,设计了基于移动节点无需地理信息的修复算法SOI,该算法通过计算空洞边缘可移动的最佳内点,使该边缘节点到达新的位置,实现减少目标区域中空洞面积的目的。分析与仿真实验表明,与VHR算法相比,SOI算法在部署密集的传感网络中需要移动的总距离更少。     

一种三角形网格空洞修复算法

无线传感器网络由大量传感器节点组成,在网络初始化时节点随机部署在目标区域中,导致某一区域未被覆盖而形成覆盖空洞.针对目标区域中存在覆盖空洞问题,设计了一种基于三角形网格的无需地理信息的空洞探测算法ATN和空洞修复算法TNR.利用ATN算法检测节点与其邻居形成的三角形网格是否被完全覆盖,TNR算法以ATN算法理论为基础,向三角形网格中添加节点使目标区域完全覆盖.理论与仿真实验分析表明,ANR算法能够探测出目标区域中所有空洞,TNR算法在部署密集的传感网络中能够快速完成空洞修复.     

移动WSNs中一种覆盖空洞的修复算法

基于无线传感网络的应用依赖于节点对监测区域的可靠覆盖。由于能耗殆尽或故障,节点感测能力可能失效,形成覆盖空洞区域。为此,提出基于模糊推理系统(FIS)的修复节点选取(FISS)算法。FISS算法采用自适应感测半径调整策略,利用节点的移动修复覆盖空洞。将节点的剩余能量和覆盖重叠率作为FIS的输入,并由FIS估计节点成为修复节点的权值;选择具有权值最高的节点作为修复节点,同时依据修复节点的网络条件,估算修复节点的移动方向和移动距离,最终实现对覆盖空洞区域的修复。仿真结果表明,提出的FISS算法有效提高了覆盖率,并控制了能耗。     

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.     

基于感知距离调节的无线传感器网络节能区域覆盖

传感器节点能够感知的物理世界的最远距离称为节点的感知距离。该文研究了基于节点感知距离调节的无线传感器网络节能区域覆盖方案,该方案通过设定合理的节点感知距离,使得传感器网络在满足区域覆盖要求的同时,能量消耗最低。首先将区域覆盖性能和网络能量消耗模化成网络节点感知距离的函数,然后将节能覆盖问题模化成带约束条件的优化问题,最后给出了基于网络区域划分的优化模型求解方法。仿真结果表明,与传统覆盖方案比较,所提方案在满足覆盖要求的同时,有效降低了网络能量消耗。     

移动混合传感网中节点自主部署算法

针对节点感知半径不均衡的移动传感网络节点的部署问题,论文提出一种基于VL(Voronoi Laguerre)图分割的节点自主部署算法(Autonomous Deployment Algorithm, ADA)。ADA先对目标区域做VL图划分,将目标区域的覆盖任务在各个传感器节点之间进行分配。分配到覆盖子区间任务的节点通过构造VL受控多边形来确定下一轮候选目标位置。未分配到覆盖子区间的节点则根据自身与邻居节点感知圆及目标区域边界的几何位置关系计算所受虚拟力,最终确定下一轮目标点坐标。网络各个节点通过逐轮更新自身位置,从而提高网络覆盖。仿真结果表明,ADA算法在网络覆盖率、节点部署速度和节点分布均匀性等方面具有明显的优势。     

基于遗传算法优化的WSN覆盖控制算法

针对无线传感器网络(Wireless Senson Networks,WSN)的覆盖控制和网络寿命问题,基于改进的遗传算法结合提出的局部搜索策略进行优化。根据WSN的特性对遗传算法的适应度函数进行了改进,并与提出的局部搜索策略结合进一步优化遗传算法适应度值,使之可以使用更少的传感器节点来保障监测目标的100%覆盖。仿真实验表明,与遗传算法相比,该算法具有更高的适应度、更长的100%覆盖期和网络寿命。基于遗传算法改进的WSN覆盖控制具有更优秀的性能及提高WSN对感知区域的覆盖控制能力。     

视距-非视距传感网的立体感知优化

基于当前无线传感器网络二维覆盖的研究成果，针对实际的立体空间应用性较弱的困难，给出感知节点覆盖三维复杂场景（CTDCT）的解决方案。强调以立体感知模型刻画三维复杂场景下节点感知质量，结合视距与非视距场景下感知盲区的判定，解决遮蔽覆盖的误判问题；引入点集到场景可行域的映射，初筛节点分布与减小冗余的双增益网络；利用全迭代周期内非线性调整节点位置，前期提升网络多样性，后期优化局部拓扑结构；设计节点移动步长，使其受冗余节点的激励，提升网络与现有环境间的耦合程度。仿真结果表明，CTDCT算法在视距与非视距场景联合作用下，通过减小覆盖误判概率，优化调整节点的坐标位置，可有效降低节点感知重叠区和盲区，最终实现三维复杂场景下区域覆盖质量的增强。     

无线传感器网络中的分布式Voronoi覆盖控制算法

以覆盖部分目标区域的传感器网络为研究背景,在通信半径不小于2倍传感半径的条件下,提出了一种维持网络原有覆盖范围、连通性的分布式Voronoi覆盖控制算法.首先,提出了一种基于局部Voronoi区域的冗余识别规则,其计算复杂度与节点密度无关;然后,提出了一种能量优先的Voronoi调度规则,通信相邻、局部Voronoi不相邻的节点可以同步执行冗余识别,提高分布式调度的收敛性.仿真实验表明,所提算法求解活跃节点的数量、平均覆盖度与集中式算法接近,优于一般的分布式算法,而在活跃节点的平均能量、算法性能等方面更加具有优势.     

一种面向三维感知的无线多媒体传感器网络覆盖增强算法

 覆盖作为无线传感器网络中的基础问题直接反映了网络感知服务质量.本文在分析现有无线多媒体传感器网络覆盖增强算法的基础上,构建节点三维感知模型,提出面向三维感知的多媒体传感器网络覆盖增强算法(Three-Dimensional Perception Based Coverage-Enhancing Algorithm,TDPCA).该算法将节点主感知方向划分为仰俯角和偏向角,并根据节点自身位置及监测区域计算并调整各节点最佳仰俯角,在此基础上基于粒子群优化调整节点偏向角,从而有效减少节点感知重叠区及感知盲区,最终实现监测场景的区域覆盖增强.仿真实验表明:对比已有的覆盖增强算法,TDPCA可有效降低除节点感知重叠区和盲区,最终实现网络的高效覆盖.     

Coverage optimization algorithm based on VF-CS in mobile sensor network

A random placement of large-scale sensor network in the outdoor environment often causes low coverage.An area coverage optimization algorithm of mobile sensor network (MSN) based on virtual force perturbation and Cuckoo search (VF-CS) was proposed.Firstly,the virtual force of the sensor nodes within the Thiessen polygon was analyzed based on the partitioning of Voronoi diagram of the monitoring area.Secondly,the force of polygon vertices and neighbor nodes was taken as the perturbation factor for updating the node’s location of the Cuckoo search (CS).Finally,the VF-CS guided the node to move so as to achieve the optimal coverage.The simulation results demonstrate that the proposed algorithm has higher coverage and shorter average moving distance of nodes than the Voronoi diagram based algorithms in literatures.     

人工鱼群优化的方向传感器覆盖增强

为提高无线多媒体传感器网络区域覆盖率,提出了人工鱼群优化的覆盖增强算法,算法基于三维方向传感器感知模型,优化网络传感器方向角度值,减少重叠覆盖以提升网络覆盖率。仿真实验表明该方法能有效增强网络覆盖率,并就传感器参数对覆盖率影响进行分析,分析结果表明优化后的网络覆盖率更加接近理想覆盖率。     

无线传感器网络基于参数可调增强型覆盖控制算法

覆盖问题是无线传感器网络领域的一个基本问题,也是无线传感器网络特性当中的一个重点问题.如何通过某种算法达到以最少传感器节点对监测区域的有效覆盖已成为目前研究的一项重要课题.因此,提出一种增强型覆盖控制算法(Enhanced Coverage Control Algorithm, ECCA).该算法通过概率理论知识可以有效地求解出对监测区域进行有效覆盖下的最少节点,给出了传感器节点概率的期望值计算方法以及目标节点首次被传感器节点覆盖和多次覆盖后的期望值求解过程,验证随机变量相互之间不独立时的比例函数关系.仿真结果表明,ECCA算法可以使用较少的传感器节点数量完成对监测区域的有效覆盖,提高了对监测区域的覆盖质量.     

Coverage hole problem under sensing topology in flat wireless sensor networks

In flat wireless sensor networks, one fundamental issue is region coverage, which usually addresses whether the given region is sufficiently covered by sensing disks of sensor nodes or not. Although numerous research works have been carried out on region coverage, it still lacks in‐depth understanding on the relations between region coverage and sensing topology defined with the intersections of sensing areas of sensor nodes. In this paper, we consider the region coverage problem by using the sensing topology proposed in our previous work. Based on the notion of sensing topology, we prove that the given region can be partitioned into a number of the smallest cells, each of which is defined by sensing links among sensor nodes. Then, we investigate the sufficient and necessary conditions for the existence of coverage holes for the specific polygon graph residing in the partitioned cells. Further, two polynomial time algorithms are presented for dividing the given region covered by the whole network and detecting the coverage holes existing in the interior area of the partitioned cells, respectively. The experiment results show that our proposed algorithms are effective for detecting the coverage holes. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

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。      

Polytype target coverage scheme for heterogeneous wireless sensor networks using linear programming

Sensing coverage is one of fundamental problems in wireless sensor networks. In this paper, we investigate the polytype target coverage problem in heterogeneous wireless sensor networks where each sensor is equipped with multiple sensing units and each type of sensing unit can sense an attribute of multiple targets. How to schedule multiple sensing units of a sensor to cover multiple targets becomes a new challenging problem. This problem is formulated as an integer linear programming problem for maximizing the network lifetime. We propose a novel energy‐efficient target coverage algorithm to solve this problem based on clustering architecture. Being aware of the coverage capability and residual energy of sensor nodes, the clusterhead node in each cluster schedules the appropriate sensing units of sensor nodes that are in the active status to cover multiple targets in an optimal way. Extensive simulations have been carried out to validate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

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.