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．

     

能量有效的无线传感器网络协同覆盖

在节点随机分布的无线传感器网络目标覆盖中,考虑到单个节点有时难以完成对目标的感知,利用节点的概率感知模型和漏检率的概念,提出了节点协同覆盖的思想,并建立了协同覆盖模型;详细分析并推导了协同覆盖感知概率、节点数目和节点参与协同覆盖的最低感知概率之间的关系;在协同覆盖模型的基础上,考虑节点能量消耗的因素,设计了优化网络使用寿命的协同覆盖算法ECTC;仿真结果表明,该算法在改善网络感知概率的同时,延长了网络的使用寿命。     

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.     

无线传感器网络中一种延长寿命的覆盖算法

寻找有效的节能方案以提高网络寿命是无线传感器网络中的核心问题。传统的最大化网络寿命致力于对节点进行休眠或激活的调度,在DLM算法的基础上提出一种ASR-DLM算法。其基本思想是对传感器节点的感知半径进行调整,提高能量的利用率,同时ASR-DLM算法改进了DLM算法中一旦探测到覆盖空洞即终止的缺陷,即在探测到覆盖空洞时,对覆盖空洞进行填补,ASR-DLM算法不仅能够保证完全覆盖,还可以在异构网络中进行,大大延长了网络寿命,同时不需要地理位置信息,扩展性很好。     

无线传感器网络正六边形节点覆盖模型研究

定义感知覆盖、通信覆盖和连通覆盖3个基本概念,并给出它们的物理模型和数学模型。提出二维区域上的正六边形节点覆盖模型,证明该模型是重复最少的无漏洞覆盖模型。分析基于正六边形节点覆盖模型的二维区域覆盖,当节点发射半径大于或等于 倍感知半径时,可保证网络的通信覆盖和连通覆盖的最少邻居节点数为6,节点感知覆盖率为82.7%。研究结果表明,正六边形节点覆盖模型更适于二维区域覆盖。     

无线传感器网络中基于移动节点的目标覆盖方法研究

在无线传感器网络覆盖中,由于传感器节点的随机分布和随着工作时间的延长,部分节点可能因为多种原因导致其失效或损毁,从而形成监测区域中的盲区问题。基于以上问题,在研究国内外网络覆盖的基础之上,提出了一种利用可移动的传感器节点对选择性目标的覆盖方法,从理论上验证该方法能很好地解决盲区问题。     

无线传感器网络节点覆盖优化策略研究

无线传感器网络是一种多学科交叉、高度集成的技术,具有成本低、操作便捷、具有一定的感知通信能力等优势,已广泛应用在各个领域的环境监测中。然而在农林、野外等三维环境中,无线传感器的空间覆盖问题一直是研究的热点、难点,理想的二维平面环境中,无线传感器网络的性能、覆盖范围与三维环境截然不同。基于此,提出一种符合三维场景覆盖感知的无线传感器网络模型及算法。     

异构监测传感器网络寿命最大化模型及其求解

对于有两类无线传感器节点组成的异构监测网络,给出了考虑连通覆盖约束条件的求解网络寿命的最优化模型;根据KKT条件,最优解处的不等式约束可以转化为等式约束,得到了模型的最优解,该最优解对于实时调整传感器网络的感知/发射半径具有很强的指导意义.数值结果表明,网络最大寿命值随传感器的感知/发射半径的增加而减小;同时传感器发射半径的调整,可以提高网络寿命.     

混合无线传感器网络节点覆盖优化

为了解决混合无线传感器网络的节点覆盖率低的问题,提出了改进粒子群的混合无线传感器网络节点覆盖迭代优化算法.在该算法中,首先将混合无线传感器网络节点覆盖模型转化为在网络系统中动态的求覆盖率最大值的节点部署位置寻优问题;然后提出利用改进粒子群算法对节点覆盖优化方案进行粒子及其权值映射,并依据粒子粒距聚类度和粒子信息熵对粒子权值进行调整,再依据粒子适应度值对粒子局部最优值和全局最优值进行更新;最后迭代地对粒子的位置和速度进行计算,输出具有最优覆盖率的节点部署方案.仿真结果证明,该算法能够有效的提升网络覆盖率,且算法的收敛速度快.     

无线传感器网络节点自调度冗余覆盖算法

对于能量受限的无线传感器网络,延长网络存活的时间很关键。针对这个问题,提出了一种基于能量均衡的传感器节点自调度冗余覆盖协议（SRCP）,通过仿真实验对该算法的有关性能进行了评价,性能评价表明：这种算法能有效使用节点能力,延长网络存活时间。     

无线传感器网络中基于遗传算法的优化覆盖机制

覆盖作为无线传感器网络应用的一个基本问题．反映了网络监测和实现目标跟踪的质量效果．针对传感器节点的高密度部署情况,研究了工作节点集选取问题．提出两种基于加权遗传算法和基于约束遗传算法的优化覆盖机制．根据生成的适值函数进行遗传算法操作．并计算传感器网络充分覆盖区域所需的近似最优工作节点集．仿真结果表明该算法能快速收敛于最优解．完成工作节点集的优化选取,从而降低网络冗余,延长网络生存时间．     

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.     

Maximizing lifetime in wireless sensor networks with multiple sensor families

Wireless sensor networks are generally composed of a large number of hardware devices of the same type, deployed over a region of interest in order to perform a monitoring activity on a set of target points. Nowadays, several different types of sensor devices exist, which are able to monitor different aspects of the region of interest (including sound, vibrations, proximity, chemical contaminants, among others) and may be deployed together in a heterogeneous network. In this work, we face the problem of maximizing the amount of time during which such a network can remain operational, while maintaining at all times a minimum coverage guarantee for all the different sensor types. Some global regularity conditions in order to guarantee a fair level of coverage for each sensor type to each target are also taken into account in a second variant of the proposed problem. For both problem variants we developed an exact approach, which is based on a column generation algorithm whose subproblem is either solved heuristically by means of a genetic algorithm or optimally by an appropriate ILP formulation. In our computational tests the proposed genetic algorithm is shown to be able to dramatically speed up the procedure, enabling the resolution of large-scale instances within reasonable computational times.     

Tradeoff between utility and lifetime in energy-constrained wireless sensor networks

We study the tradeoff between network utility and network lifetime using a cross-layer optimization approach. The tradeoff model in this paper is based on the framework of layering as optimization decomposition. Our tradeoff model is the first one that incorporates time slots allocation into this framework. By using Lagrangian dual decomposition method, we decompose the tradeoff model into two subproblems: routing problem at network layer and resource allocation problem at medium access control (MAC) layer. The interfaces between the layers are precisely the dual variables. A partially distributed algorithm is proposed to solve the nonlinear, convex, and separable tradeoff model. Numerical simulation results are presented to support our algorithm.     

Data censoring with network lifetime constraint in wireless sensor networks

In wireless sensor networks (WSNs), senor nodes are usually battery-powered with limited energy budget. The network lifetime is directly related to the energy consumption of each node. Online censoring is an effective approach to reduce the overall energy consumption by only transmitting statistical informative data. However, the network lifetime is not proportionally extended with online censoring, since individual sensor may still suffer from energy shortage due to frequent transmission of informative data or transmission over long distance. In this paper, a parameters estimation problem is considered in WSNs, where the goal is to minimize the estimation error under the network lifetime constraint. Two censoring algorithms are developed, which allow sensor nodes to make decisions locally on whether to transmit the sampled data. The proposed algorithms can extend the network lifetime with little performance loss. Simulation results validate their effectivenesses.     

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。     

无线视频传感节点夹角完全覆盖优化模型

针对无线传感网络中视频传感节点的部署问题,形式化定义了一个（k,ω）-夹角完全覆盖问题,为用最少的传感节点去（k,ω）-夹角完全覆盖所有的目标,提出了一个两阶段混合整数线性规划算法来解决传感节点的部署。仿真实验结果显示提出的算法相较于整数线性规划算法和二元整数规划算法,确实能够实现用更少的传感节点完全覆盖所有监控区域的目标。     

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）。     

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.