无线传感器网络中基于Voronoi图的覆盖和连通综合配置协议

着重研究无线传感器网络随机部署下的覆盖和连通问题的解决方案,尤其是当无线传感器节点的通信半径Rc与感应半径Rs之比小于2时的解决方案.本文提出了无线传感器网络中一个基于Voronoi图的覆盖和连通的综合配置协议(VIP).该协议采用了一种分布式节点冗余判断算法以判断无线传感器网络中节点的冗余性,并让节点据此来对自身进行相应的职能调度.该协议能够在Rc/Rs为任意值时保证网络的覆盖和连通性能.本文还将该协议进行了推广,使得该协议能够满足覆盖度和连通度动态变化的要求,保证网络的k-度覆盖和k-度连通.     

无线传感器网络中一种分布式冗余检测算法

无线传感器网络覆盖控制中现有的大部分冗余检测算法都是针对节点感知半径相同的同构网络的,无法应用于异构网络.提出一种保持网络k级覆盖的适应异构传感器网络的分布式冗余检测算法.该算法根据节点的冗余分布特性设计了有效覆盖邻居选取,通过有效覆盖邻居感知半径关系及交点处的覆盖程度判断检测冗余.仿真表明:算法中有效覆盖邻居选取的设计,大大降低了节点执行冗余计算的时间,算法的运行效率较高;算法在异构WSN中性能优异,冗余检测彻底、充分,有益于节省节点能量,延长网络生存时间.     

一种适用于复杂环境的冗余检测算法

在无线传感器网络中,由于节点能耗的不均衡导致节点感知半径的不同.针对这种复杂环境的应用,提出了一种分布式冗余检测算法,给出了相应的冗余检测准则,并对该准则的合理性进行了理论分析.在保持网络原始覆盖质量的前提下,能够更充分地检测冗余节点.仿真实验表明,复杂环境中节点感知半径相同时,算法激活的工作节点数目少,冗余检测完整;感知半径不同时,算法仍能充分、有效地检测冗余节点,从而节省能量、延长网络生存时间.     

保持覆盖的无线传感器网络簇内节点调度方法

为了延长无线传感器网络的生命周期,提高节点能量利用效率,将簇与节点调度相结合,提出了保持覆盖的无线传感器网络簇内节点调度。首先将网络分簇,然后每个簇内的节点,按节点号自小到大的顺序依次计算每个节点与簇内邻居节点的距离,并判断所有距离小于监测半径的邻居节点能否对自己实现圆周覆盖,从而判断自己是否是冗余节点。利用Matlab仿真表明,一方面该算法可以有效减少网络的能量消耗,延长网络生命周期;另一方面该算法的调度效率与网络节点密度以及节点监测半径都有密切关系。     

面向异构网络的基于 k-覆盖的休眠调度算法

异构无线传感网络WSNs(Wireless Sensor Networks)的多数监测应用要求兴趣区域FoI(Field of Interest)是k覆盖(k-cover),且k≥1.而冗余节点被安排为休眠,进而最小化能量消耗.为此,提出面向异构网络的基于k-覆盖的冗余节点休眠算法k-CRSS(k-cover based sleep Scheduling algorithm for redundant node).k-CRSS算法引用概率方法判断节点是否为冗余节点,并推导判断一个节点是否为冗余节点的概率表述式.然后,引用调度算法识别所有冗余节点,并让它们进行休眠,且在FoI内不出现覆盖空洞.k-CRSS算法属分布式算法,并无需任何地理信息,仅通过少量控制消息收集邻居节点信息.实验数据表明,k-CRSS算法通过调度算法减少了活动节点数,进而延长了网络寿命.     

分布式WSN联合概率覆盖节点调度算法

利用概率覆盖探测模型,提出了一种分布式的基于联合概率覆盖的节点调度算法。节点在本地通过与其一跳邻节点的信息交互,获取本地节点所在区域的所有覆盖匹配集,根据邻节点的工作状态判断本地所在区域被概率覆盖情况;最后,节点将根据判断结果调度本地节点进入工作状态或休眠状态。仿真结果表明,该算法执行效率高于CCP和DPCP算法,能够在保证网络概率覆盖前提下,关闭大量冗余节点,保证网络工作节点数目稳定,延长了网络寿命。     

高效节能的无线传感器网络覆盖保持协议

分析了实际环境中随机部署传感器网络的感知特性,给出了节点感知半径服从正态分布的无须地理位置信息的节点冗余度计算模型,以及保证网络覆盖质量所需要的最少工作节点数的计算模型.在此模型的基础上,提出了高效节能的无线传感器网络覆盖保持协议(energy efficient coverage conserving protocol,简称EECCP),实现了均衡节点能量消耗的分布式协作调度.该协议保留最少的工作节点以保证要求的覆盖质量,从而达到节约网络能量的目的.仿真实验结果表明,EECCP不仅能够保证要求的覆盖质量,而且能够减少网络能量消耗,有效地延长了网络的有效寿命.     

自适应半径调整的无线传感器网络覆盖算法

针对随机分布的无线传感器网络中节点分布不均匀造成的覆盖冗余,以及同时存在的覆盖空洞,提出了一种自适应半径调整无线传感器网络覆盖算法,通过阈值判断监测区域内传感器节点密度,根据监测区域内传感器节点疏密程度,利用节点半径步长系数对监测区域内节点半径进行自适应调整,建立无线传感器节点发射功率与节点发射半径的模型,计算无线传感器发射功率,通过实验和仿真,表明上述方法能够保证网络覆盖率的基础上减少无线传感器网络总功耗,提高网络寿命.     

基于Voronoi图和复合泊松过程的WSN覆盖算法设计

设计了一种基于Voronoi图和复合泊松过程的分布式算法。利用Voronoi图的性质,传感器节点能够同时进行冗余判定和感知半径调节来消除覆盖冗余;利用休眠节点的复合泊松探测过程,能够及时发现工作节点的失效并弥补网络的覆盖漏洞,使网络由单纯的完成监测任务变为动态调整拓扑以适应环境变化。仿真结果表明网络能准确调度节点状态,并且对工作节点失效具有很好的鲁棒性。     

WSN中基于能量的分布式覆盖控制算法

针对无线传感器网络中节点密度过大、节点剩余能量不均等问题,设计一种基于节点剩余能量的分布式覆盖控制算法,基于概率覆盖模型,按目标区域内节点剩余能量从小到大的顺序,依次通过计算各个节点的区域覆盖概率判定其冗余性,并使冗余节点转入休眠状态。仿真结果表明,该算法能有效降低网络中节点冗余度,延长网络生存时间。     

Layered Diffusion-based Coverage Control in Wireless Sensor Networks

Coverage is an important performance metric for many applications, such as surveillance in wireless sensor networks. Coverage control is used to select as few active nodes as possible from all deployed sensor nodes, such that sufficient coverage of the monitored area can be guaranteed while reducing the energy consumption of each individual sensor node to prolong the network lifetime. This paper classifies three types of coverage control protocols based on the available information about nodes’ distances or locations, and reviews several representative protocols for each type. We also propose a new distributed and localized coverage control protocol, called Layered Diffusion-based Coverage Control (LDCC). The LDCC protocol does not require information about the node location coordinates when selecting active nodes. Instead, it exploits hop count information, which is easily obtained in a WSN, to select active sensor nodes. Furthermore, the LDCC protocol is very simple and does not require any sophisticated computation such as distance or covered area computation. Our simulation results show that the LDCC protocol achieves a high coverage ratio while incurring very low message overhead compared with other existing protocols. Furthermore, simulation results suggest that in a large-scale sensor network with medium to large localization errors, LDCC performs even better than location-based coverage control protocols.     

WSN中一种规则区域最优覆盖与连通算法研究

在满足一定覆盖条件下,使用最少传感器节点完成对区域的覆盖与连通已成为无线传感器网络研究的一个具有挑战性的核心问题之一。为此,提出了一种规则区域最优覆盖与连通算法,该算法利用双重正方形将目标节点规划到内正方形区域内,通过传感器节点与目标节点求出关联模型,对于整个覆盖区域则利用概率期望值求出满足覆盖条件的最少传感器节点数,同时给出了外正方形区域边缘节点的连通概率模型及推理过程。实验结果表明,该算法的理论值与模拟结果之间的误差小于5%,验证了该算法是有效的,降低了网络资源的配置,可以更好地评估网络覆盖和连通性能。     

EADEEG:能量感知的无线传感器网络数据收集协议

提出了一种基于簇结构的无线传感器网络数据收集协议EADEEG(an energy-aware data gathering protocol for wireless sensor networks).EADEEG通过最小化网络通信开销以及良好的能量负载平衡方法,可以有效地延长网络寿命.与以前的相关研究相比,EADEEG采用了一种全新的簇头竞争参数,能够更好地解决节点能量异构问题.此外,EADEEG也采用了一种简单而有效的簇内节点调度算法,通过控制活动节点的密度,可以在不增加额外控制开销的条件下关闭冗余节点并保证覆盖要求,因此可以进一步延长网络寿命.模拟实验证明,在节点初始能量同构和异构两种情况下,EADEEG协议都能够满足用户对覆盖率的要求,并在网络寿命上大幅度优于LEACH(low energy adaptive clustering hierarchy),PEGASIS(power-efficient gathering in sensor information systems)和DEEG(distributed energy-efficient data gathering and aggregation protocol)协议.     

基于分布式烟花算法的WSN布局优化方法

无线传感器网络具有广泛的应用,然而如何有效部署无线传感器节点,提高节点利用率和网络覆盖率,仍是一个亟待解决的问题。针对传统无线传感器网络部署方法存在节点冗余率高、覆盖率低等问题,以网络覆盖率为优化目标,将烟花算法良好的结果搜索能力和分布式高效的计算速度相结合,实现对网络覆盖率优化模型的高效求解。实验表明,该算法相比于普通的烟花算法具有更好的计算结果和更快的收敛速度。     

WSN中考虑节点磨损的分布式自稳定网络寿命优化算法

针对无法预估的节点故障影响无线传感器网络寿命的问题,本文提出了一种考虑节点磨损的分布式自稳定优化算法。首先,利用韦伯函数拟合网络生命周期的分布,定期唤醒睡眠节点进行故障检测;然后,采用分布式调度,无需知道传感节点的位置信息,使用多个节点同时工作,从而提高效率;最后,通过较低的网络通信代价,及时使用空闲节点替代故障节点,保持网络的连通性。理论分析和仿真实验验证了本文算法的有效性及可靠性,仿真结果表明,当传感器节点的可靠性随着使用时间与磨损下降时,本文算法可以更好地延长无线传感器网络寿命,相比分布式多目标概率覆盖协议,本文算法在寿命延长、覆盖率、节点唤醒次数等方面均取得更好的性能。     

Multiround Distributed Lifetime Coverage Optimization protocol in wireless sensor networks

Coverage and lifetime are two paramount problems in wireless sensor networks (WSNs). In this paper, a method called Multiround Distributed Lifetime Coverage Optimization protocol (MuDiLCO) is proposed to maintain the coverage and to improve the lifetime in wireless sensor networks. The area of interest is first divided into subregions, and then the MuDiLCO protocol is distributed to the sensor nodes in each subregion. The proposed MuDiLCO protocol works in periods during which sets of sensor nodes are scheduled, with one set for each round of a period, to remain active during the sensing phase and thus ensure coverage so as to maximize the WSN lifetime. The decision process is carried out by a leader node, which solves an optimization problem to produce the best representative sets to be used during the rounds of the sensing phase. The optimization problem formulated as an integer program is solved to optimality through a Branch-and-Bound method for small instances. For larger instances, the best feasible solution found by the solver after a given time limit threshold is considered. Compared with some existing protocols, simulation results based on multiple criteria (energy consumption, coverage ratio, and so on) show that the proposed protocol can prolong efficiently the network lifetime and improve the coverage performance.     

A novel gossip-based sensing coverage algorithm for dense wireless sensor networks

Wireless sensor networks (WSNs) have been widely studied and usefully employed in many applications such as monitoring environments and embedded systems. WSNs consist of many nodes spread randomly over a wide area; therefore, the sensing regions of different nodes may overlap partially. This is called the “sensing coverage problem”. In this paper, we define a maximum sensing coverage region (MSCR) problem and present a novel gossip-based sensing-coverage-aware algorithm to solve the problem. In the algorithm, sensor nodes gossip with their neighbors about their sensing coverage region. In this way, nodes decide locally to forward packets (as an active node) or to disregard packets (as a sleeping or redundant node). Being sensing-coverage-aware, the redundant node can cut back on its activities whenever its sensing region is k-covered by enough neighbors. With the distributed and low-overhead traffic benefits of gossip, we spread energy consumption to different sensor nodes, achieve maximum sensing coverage with minimal energy consumption in each individual sensor node, and prolong the whole network lifetime. We apply our algorithm to improve LEACH, a clustering routing protocol for WSNs, and develop a simulation to evaluate the performance of the algorithm.     

公平的有向传感器网络方向优化和节点调度算法

为了解决有向传感器网络中点目标覆盖控制问题,分别提出了两种方向优化算法和一个节点调度协议:改进的贪婪(enhanced greedy algorithm,简称EGA)、公平的方向优化(equitable direction optimization,简称EDO)算法和邻居节点调度协议(neighbors sensing scheduling,NSS).EGA 基于覆盖最多未覆盖的目标数选取工作方向,其不足是可能忽略临界目标.EDO 优化算法调节节点的工作方向,优先覆盖临界目标,公平分配感知资源,减小目标覆盖度的差异,EDO 算法使用效用值评价每个方向对网络覆盖质量的贡献大小,影响效用值的因素包括每个方向上的目标数、目标的覆盖度和邻居节点的方向决策,EDO 总是选择效用值最大的方向作为工作方向.NSS 协议引入局部覆盖集的概念,通过局部覆盖集判断当前节点是否为冗余节点,并在考虑节点剩余能量时决定节点是否可以转为睡眠,调度协议允许一个节点加入多个覆盖集,覆盖集轮流工作,使网络生存期最大化.仿真实验结果表明,分布式的EDO 算法比EGA 算法具有更好的方向优化性能,临界目标的覆盖质量提高了30%,同时明显地提高了网络生存期.     

Coverage area enhancement in wireless sensor network

In the wireless sensor network, coverage area may be enhanced after an initial deployment of sensors. Though, some research works propose how to decrease the coverage hole by increasing sensing range or movement assisted sensor deployment, these are not suitable for energy constraint wireless sensor network, as longer mobility distance or higher power level consume more energy. In this paper, we address the increasing coverage area through smaller mobility of nodes. We find out the coverage hole in the monitoring region, which is not covering by any sensing disk of sensor. Then, we address the new position of mobility nodes to increase the coverage area. The simulation result shows the mobile nodes can recover the coverage hole perfectly. The coverage holes is recovered by mobility on the existing recovery area, which cannot be lost. Moreover, hole detection time in our proposed protocol is better than existing algorithm.