基于遗传算法的无线传感器网络巡航覆盖最小移动节点数研究

被监测环境的状态可以使用无线传感器网络的有效感知.本文使用无线传感器网络巡航覆盖模型实现了建筑能效监测过程.为降低基于无线传感器网络巡航覆盖模型的数据采集系统构造和维护成本并降低系统复杂度,采用MTSP问题对无线感器网络巡航覆盖最小移动节点数问题建模,提出了无线感器网络巡航覆盖最小移动节点数快速求解算法并使用遗传算法实现了快速求解算法.实验结果表明,本文提出的方法快速有效,而基于遗传算法的快速求解算法的实现可以快速确定无线感器网络巡航覆盖最小移动节点数上界.     

基于量子遗传算法的无线传感器网络路径优化

无线传感器网络作为一种有广泛应用前景的新型信息获取和处理技术,引起了国内外学术界和产业界的高度关注;文章采用量子遗传算法对无线传感器网络进行路径优化,将节点能耗和路由恢复时间等实际因素作为路径优化问题的约束条件,按照重要性考虑各种约束条件和多种目标的优先次序,并对量子遗传算法的各个环节进行了细致的分析与设计,包括量子比特编码、适应度函数设计、量子门更新、量子变异等;仿真实验表明,使用量子遗传算法能找到无线传感器网络有效的优化路径,并且优于相关算法。     

无线传感网中基于唤醒机制的覆盖洞修复方法

针对无线传感器网络中节点因能量耗尽或环境破坏而失效导致产生覆盖空洞现象,提出了一种基于唤醒机制的定向最远非活跃邻节点优先覆盖洞修复策略（DFNFP）。该方法通过覆盖洞边界节点从其邻接表中选择距离其中心最远的非活跃节点替换失败节点,并激活替换节点达到修复覆盖洞目的。仿真结果表明,该方法能保证较好的网络覆盖质量,充分利用了能量资源,延长了网络的生存周期,且在修复空洞的所需平均时间和能耗方面,DFNFP优于最佳匹配节点策略（BFNP）。     

遗传算法在无线传感器网络覆盖中仿真研究

在无线传感器覆盖问题的研究中,传统覆盖算法存在节点覆盖区域间重复率高、能耗浪费从而导致网络覆盖效率低、网络寿命短的难题.为了提高无线传感器网络的覆盖率、延长网络生存时间,提出一种遗传算法优化的无线传感网络覆盖方法.首先把拓扑控制思想引入到网络节能覆盖中,无线传感器最大覆盖率为目标,建立一个感知半径之和最小的数学模型,然后利用遗传算法对模型进行求解,从而得到最优网络覆盖.最后进行仿真实验,仿真结果表明,相对于传统算法,不仅节能而且可以用较少的传感器节点获得较高覆盖率、有效地降低信道通讯干扰、提高网络的抗毁性.     

无线传感器网络中基于微粒群算法的优化覆盖机制

建立了无线传感器网络节点覆盖优化数学模型,设计了一种基于二进制随机多目标微粒群优化(SMOPSO)算法.根据最大化覆盖网络目标函数和最小化传感器节点的利用率目标函数进行优化算法操作,以达到降低网络冗余,延长网络生存时间的效果.仿真实验结果表明,本文提出的无线传感器网络优化覆盖方法能够满足节点利用率低、覆盖率高的要求.     

基于目标覆盖的无线传感器网络的连通性优化部署方法

覆盖控制技术研究无线传感器网络空间资源的优化分配,以满足用户的感知需求.覆盖节点的连通性是覆盖研究的关键问题,决定了感知数据能否最终传递给用户.已知的研究并未涉及目标覆盖确定部署应用的连通性问题.因此,分析了目标覆盖中的连通性问题,首次提出针对目标全覆盖与维护节点集连通性关系的连通临界条件;针对连通性条件无法满足的情况,提出了一个维护连通性的优化部署方案.该方案首先建立连通子集间的最小生成树,构造连通候选集;然后,基于连通候选集,利用遗传算法得到优化候选位集.仿真实验表明,提出的优化部署方案既实现了对目标集的全覆盖,又维护了连通性,并使候选位集的元素个数更少.     

无线传感器网络中基于虚拟力的覆盖算法

在无线传感器网络的很多应用场景中,大量的传感器节点被任意播撒在被监测区域内,形成很多覆盖空洞,对无线传感网络的感知、监测和数据采集能力造成很大影响。为了解决无线传感器网络中的覆盖问题,提出了一种基于虚拟引力的覆盖算法。首先,根据虚拟引力产生的约束条件和引力大小,一种扩大网络覆盖范围的算法被提出,算法分析证明这种算法能够减少覆盖空洞;第二,提出了维持邻居节点连通性的方法;第三,提出一种覆盖感兴趣区域的算法。仿真结果表明,这种算法既能提高网络的覆盖能力,又能减少传感器节点的移动距离。     

一种基于遗传算法的无线传感器网络覆盖模型

在无线传感器网络中,传感器节点的分布通常具有随机性和密集性,监测区域会出现覆盖盲区或者覆盖重叠.为此,推导出了无线传感器最优覆盖模型计算最少节点个数的公式,对遗传算法中的适应度函数公式做了改进,将多重覆盖率和覆盖率的组合作为适应度函数.根据遗传算法的相关内容和流程图,利用遗传算法对覆盖策略做了仿真模拟,证明了所选用的方法的正确和优越性.     

无线传感器网络中基于异构节点的覆盖控制算法

研究了无线传感器网络中基于异构节点的优化覆盖控制问题.异构无线传感器网络由两类能力不同的节点组成,包括普通节点和超级节点.对普通节点采用基于状态轮转的覆盖控制算法,对超级节点采用基于路由表的转发策略.通过两类节点的协作使得网络达到覆盖与连通的目的.模拟结果表明,在具有相同初始能量的情况下,该算法与SHHN-HS算法相比能够延长网络生命期.     

无线传感器网络中多重覆盖算法的研究

在无线传感网器络中的硬件资源普遍受限的情况下,形成优秀的覆盖控制策略己成为无线传感器网络研究领域的热点课题。本文提出了一种能量有效的多重物理覆盖算法,在保障覆盖与连通性的前提下,以能量、覆盖度为衡量指标,采用调度机制实现节点轮换活跃与休眠,有效地提高网络生存时间。仿真实验结果表明,与目前典型算法相比,提出的算法在网络生存时间、能量消耗与消亡节点数上具有显著的优势。     

基于节点协同覆盖的传感器网络寿命最大化模型

针对保证网络连通覆盖和最小能量消耗的优化目标,建模了基于节点协同覆盖的传感器网络寿命最大化模型.提出一种基于多目标优化遗传算法的求解方案,设计了基于链路状态的分簇机制以及基于NSGA-Ⅱ的簇内覆盖控制算法.仿真结果表明该方案能快速收敛于最优解,在高密度和低密度布撒环境下表现出优越的性能,且具有良好的适应性.     

Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm

Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were used. Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system. A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper. The minimum number of sensors is selected in a densely deployed environment while preserving full coverage. As opposed to the binary detection sensor model in the previous work, a more precise detection model is applied in combination with the coverage control scheme. Simulation results show that our algorithm can achieve balanced performance on different types of detection sensor models while maintaining high coverage rate. With the same number of deployed sensors, our scheme compares favorably with the existing schemes.     

Connectivity preserving localized coverage algorithm for area monitoring using wireless sensor networks

Efficient network coverage and connectivity are the requisites for most Wireless Sensor Network (WSN) deployments, particularly those concerned with area monitoring. Due to the resource constraints of the sensor nodes, redundancy of coverage area must be reduced for effective utilization of the available resources. If two nodes have the same coverage area in their active state, and if both the nodes are activated simultaneously, it leads to redundancy in network and wastage of precious sensor resources. In this paper, we address the problem of network coverage and connectivity and propose an efficient solution to maintain coverage, while preserving the connectivity of the network. The proposed solution aims to cover the area of interest (AOI), while minimizing the count of the active sensor nodes. The overlap region of two sensor nodes varies with the distance between the nodes. If the distance between two sensor nodes is maximized, the overall coverage area of these nodes will also be maximized. Also, to preserve the connectivity of the network, each sensor node must be in the communication range of at least one other node. Results of simulation of the proposed solution indicate up to 95% coverage of the area, while consuming very less energy of 9.44 J per unit time in the network, simulated in an area of 2500 m².     

Optimal replicator factor control in wireless sensor networks

For TDMA MAC protocols in wireless sensor networks (WSNs), redundancy and retransmission are two important methods to provide high end-to-end transmission reliability. Since reliable transmissions will lead to more energy consumption, there exists an intrinsic tradeoff between transmission reliability and energy efficiency. For each link, we name the number of its reserved time slots in each MAC superframe as a replicator factor. In the following paper, we propose a reliability-lifetime tradeoff framework (RLTF) for WSNs to study replicator factor control problem. First, for the redundancy TDMA MAC, we formulate replicator factor control problem as convex programming. By the gradient projection method, we develop a fully distributed algorithm to solve the convex programming. Second, for the retransmission TDMA MAC, we set the retransmission upper bound for each link according to the optimal replicator factors under the redundancy MAC and compute the total communication overhead for the retransmission MAC. Finally, we compare the communication overhead of these two MAC protocols under different channel conditions.     

基于遗传算法的无线传感器网络重新部署方法

研究了无线传感器网络在受限移动能力条件下的重新部署问题.针对节点的运动模型为跳跃式移动,提出一种基于遗传算法的重新部署算法.算法以节点的跳跃方向为遗传算法的基因,适用度函数同时考虑了最大化覆盖率和最小化移动总距离.仿真实验表明,在各向同性的感测模型中,此算法优于文献[8]提出的FBSD算法,能实现节点的最优运动规划,并且在有向感测模型中,此算法也能有效提高网络覆盖率.     

异构无线传感器网络中基于模拟退火算法的成本最优部署机制

针对异构传感器网络节点的高密度部署和监测目标非均匀分布的情况,提出了一种基于模拟退火算法的成本最优部署方法.算法在保证网络覆盖和容错性的条件下,以异构节点部署成本最小为优化目标进行操作.仿真结果表明该算法能快速收敛于最优解,有效降低网络部署的成本,提高了目标监测质量.     

能量有效的三维无线传感器网络覆盖算法

无线传感器网络通常都工作在三维空间中,因此需要三维空间中的覆盖算法。结合三维空间的特点对二维空间内的覆盖算法SGA进行改进,在此基础上提出一种三维空间的覆盖算法——SSG算法,该覆盖算法的优点是不依赖于节点位置信息,并通过仿真实验给出了覆盖质量分析。     

Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks

In this study, we aim to cover a sensing area by deploying a minimum number of wireless sensors while maintaining the connectivity between the deployed sensors. The problem may be reduced to a two-dimensional critical coverage problem which is an NP-Complete problem. We develop an integer linear programming model to solve the problem optimally. We also propose a local search (LS) algorithm and a genetic algorithm (GA) as approximate methods. We verify by computational experiments that the integer linear model, using Cplex, is able to provide an optimal solution of all our small and medium size problems. We also show that the proposed methods outperform some regular sensor deployment patterns.     

Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy-harvesting sensors

Motivated by recent developments in wireless sensor networks (WSNs), we present several efficient clustering algorithms for maximizing the lifetime of WSNs, i.e., the duration till a certain percentage of the nodes die. Specifically, an optimization algorithm is proposed for maximizing the lifetime of a single-cluster network, followed by an extension to handle multi-cluster networks. Then we study the joint problem of prolonging network lifetime by introducing energy-harvesting (EH) nodes. An algorithm is proposed for maximizing the network lifetime where EH nodes serve as dedicated relay nodes for cluster heads (CHs). Theoretical analysis and extensive simulation results show that the proposed algorithms can achieve optimal or suboptimal solutions efficiently, and therefore help provide useful benchmarks for various centralized and distributed clustering scheme designs.