一种移动无线视频传感器节点的覆盖算法

在现有的无线传感器网络覆盖算法的研究中,缺乏对移动节点路径规划的研究,而针对具有视频传感器节点的网络仍使用普通传感器圆形覆盖区域的测量方法来计算覆盖面积,并不完全符合实际情况.基于这两方面的原因,本文提出了一种适用于无线视频传感器节点的最大覆盖算法,并提出一种对于视频传感器节点覆盖面积的计量方法.该算法能够使节点在保证网络连通性的前提下,达到最大的有效监测范围.此外,本文建立了相应的仿真实验模型,对该算法的有效性和覆盖面积进行了实验与分析.结果表明,本算法的节点监测面积大约为使用随机运动算法的节点监测面积的1.5倍左右,并可以保证网络的连通性.     

无线传感器网络中能耗均衡的覆盖控制算法

覆盖控制作为无线传感器网络的一个基本问题,对网络的生存时间、部署策略、通信协议和组网等问题的解决具有重要影响。在传感器节点随机冗余部署方式下,传统的方式 是在保证覆盖要求和通信连通的前提下仅将最少量的节点投入活跃工作状态,从而降低网络能耗。但是,若频繁地激活同一批节点,会造成这些节点由于能耗过快而较早失效效,使整个网络的冗余程度降低。然而,冗余度是传感器网络在单个节点性能有限的情况下提高整个网络的可靠性、容错性、精确性等的基础。为此,本文提出了一个能耗均衡ECB的覆盖问题,指出它是NP完全的,并给出了一个集中式近似算法。该算法根据节点的剩余能量赋于每个节点非负权,再基于Voronoi划分和贪心边方法,在保证覆盖要求的同时选择权和最小的节点激活。仿真实验结果表明,ECB算法求得的活跃节点集小,可以达到有效覆盖,并且可以保持网络的冗余度。     

基于传感器网络节点配置优化仿真研究

研究无线传感网络节点配置覆盖优化问题。由于无线传感网络存在着热区问题,对网络的覆盖性能造成严重的影响,同时影响网络配置优化。为了有效的提高无线传感网络的覆盖率,提出了一种改进的粒子群算法优化无线网络节点覆盖。针对粒子群算法存在易陷入局部极值和早熟的缺陷,引入遗传算法中的交叉算子和变异算子,优化传感网络节点的混合粒子群算法,在严格确保无线传感器网络连通性的条件下,传感器节点配置数目达到要求的覆盖度,并进行仿真。仿真结果表明混合粒子群算法能快速收敛到更精确的解,使网络节点配置达到覆盖的优化要求。     

无线传感器网络密度控制研究

无线传感器网络节点能量受限,部署环境恶劣、复杂。在监测区域内部署大量节点,通过节点之间的状态轮换与合作策略延长网络寿命是非常重要的方法。本文论述了设计密度控制算法状态转移条件需要解决的两个基本问题：区域覆盖问题和节点连通性优化问题,并分别以ILP的形式给出了一个集中式算法解决方案。通过节点的覆盖效用范围与连通性效用范围的几何分析,提出了传感器网络密度控制算法中解决两个基本问题的一致性策略。     

On the use of limited autonomous mobility for dynamic coverage maintenance in sensor networks

Sensor networks consist of small wireless sensor nodes deployed randomly over an area to monitor the environment or detect intrusion. The coverage provided by sensor networks is very crucial to their effectiveness. Many of the important applications of sensor networks demand autonomous mobility for the sensor nodes. Early failure of sensor nodes can lead to coverage loss that requires coverage maintenance schemes. In this paper, we propose Dynamic Coverage Maintenance (DCM) schemes that exploit the limited mobility of the sensor nodes. The main objective of coverage maintenance is to compensate the loss of coverage with minimum expenditure of energy. Existing autonomous sensor deployment schemes such as the “potential field approach” are not useful for DCM because the nodes initially distribute themselves such that there is no redundancy in coverage. We propose a set of DCM schemes which can be executed on individual sensor nodes having a knowledge of only their local neighborhood topology. The process of moving a node to a new location for maintenance of coverage is termed “migration”. We propose four algorithms to decide which neighbors to migrate, and to what distance, such that the energy expended is minimized and the coverage obtained for a given number of live nodes is maximized. The decision and movement are completely autonomous in the network, and involves movement of one-hop neighbors of a dead sensor node. We also propose an extension to these algorithms, called Cascaded DCM, which extends the migrations to multiple hops. We have developed a graphical simulator Java Sensor Simulator (JSS) to visually inspect the working of the algorithms. We have also compared the performance of the different algorithms in terms of the improvement in coverage, average migration distance of the nodes, and the lifetime of the network. Cascaded DCM was seen to offer the maximum network lifetime and coverage.     

A novel knowledge-guided evolutionary scheduling strategy for energy-efficient connected coverage optimization in WSNs

One of the most important challenges in designing wireless sensor network is how to construct full-connected network containing least active sensor nodes with satisfied quality of services, such as the coverage rate and energy consumption. This energy-efficiency full-connected coverage optimization problem is modeled as a single-objective optimization problem with constraint. To solve this problem, a knowledge-guided evolutionary scheduling strategy is proposed. Three highlights of this strategy are: (1) Knowledge is defined as the importance of sensor node, which depends on the distance between sensor node and sink node. (2) The genes of an individual correspond to senor nodes in descending order of their importance. (3) Considering sensor nodes’ importance and redundancy rate, knowledge-guided mutation operator and repair strategy are present. Simulation results show that the proposed method can find the optimal full-connected wireless sensor network containing least sensor nodes and consuming less energy for communication by less computation time. Though the coverage rate of the optimum is larger, it still satisfies the coverage constraint. Moreover, this strategy fits for the problems that the communication radius of sensor node is less than two times of its sensing radius.     

RGA-D：一种无线传感器网络覆盖优化方法

研究了无线传感器网络覆盖优化问题,针对传感器节点随机部署、分布不均,传统覆盖优化算法一般只考虑网络部署后单次优化的问题,提出基于节点冗余和覆盖集冗余的计算方法,用网络的局部特征表征全局特征,改进了网络覆盖模型。并在此基础上提出RGA-D算法,利用遗传算法计算覆盖集,同时考虑节点和覆盖集冗余度,对网络整个生存期进行全局优化。仿真实验表明,RGA-D算法能在活跃节点数和网络覆盖率之间达到平衡,解决了网络生存期后期容易出现覆盖盲区的问题。     

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.     

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.     

基于延迟唤醒的无线传感器网络的分布式区域覆盖算法

针对现有无线传感器网络中分布式区域覆盖算法中存在覆盖空洞现象、连通性和蚕食现象等问题,提出了一个保证区域全覆盖与网络全连通的临界条件,在此基础上,提出了一个基于延迟唤醒的分布式区域覆盖算法.该算法采用分轮机制,因此不需要预先了解网络的整体拓扑结构;基于延迟唤醒的活跃节点集选择机制在保证区域全覆盖、避免出现覆盖空洞现象的同时,减少了蚕食现象的发生.仿真实验表明,与现有分布式覆盖算法相比,该算法可在满足用户区域覆盖感知需求的基础上延长网络的生命周期.     

A cellular learning automata-based deployment strategy for mobile wireless sensor networks

One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be addressed by first deploying sensor nodes randomly in some initial positions within the area of the network, and then letting sensor nodes to move around and find their best positions according to the positions of their neighboring nodes. The problem becomes more complicated if sensor nodes have no information about their positions or even their relative distances to each other. In this paper, we propose a cellular learning automata-based deployment strategy which guides the movements of sensor nodes within the area of the network without any sensor to know its position or its relative distance to other sensors. In the proposed algorithm, the learning automaton in each node in cooperation with the learning automata in the neighboring nodes controls the movements of the node in order to attain high coverage. Experimental results have shown that in noise-free environments, the proposed algorithm can compete with the existing algorithms such as PF, DSSA, IDCA, and VEC in terms of network coverage. It has also been shown that in noisy environments, where utilized location estimation techniques such as GPS-based devices and localization algorithms experience inaccuracies in their measurements, or the movements of sensor nodes are not perfect and follow a probabilistic motion model, the proposed algorithm outperforms the existing algorithms in terms of network coverage.     

Coverage and connectivity issues in wireless sensor networks: A survey

Sensing coverage and network connectivity are two of the most fundamental problems in wireless sensor networks. Finding an optimal node deployment strategy that would minimize cost, reduce computation and communication overhead, be resilient to node failures, and provide a high degree of coverage with network connectivity is extremely challenging. Coverage and connectivity together can be treated as a measure of quality of service in a sensor network; it tells us how well each point in the region is covered and how accurate is the information gathered by the nodes. Therefore, maximizing coverage as well as maintaining network connectivity using the resource constrained nodes is a non-trivial problem. In this survey article, we present and compare several state-of-the-art algorithms and techniques that aim to address this coverage–connectivity issue.     

基于虚拟力算法的WMSNs覆盖研究

为了提高无线多媒体传感器网络（WMSNs）区域覆盖率,在传感器节点随机部署后,通过调节传感器节点的感知方向,使节点从感知重叠区域向覆盖盲区转动,提高网络覆盖率。针对现有算法中存在覆盖效率和覆盖率不能统一的问题,提出一种改进的虚拟力覆盖算法（VFARCR）,该算法利用传感器节点感知扇形区域质心点间的斥力调节感知方向,且通过传感器节点间的覆盖冗余度的决定方向调整的大小,虚拟力和覆盖冗余度共同控制传感器的转动。仿真实验表明：该算法提高了覆盖效率和覆盖效果,提高了虚拟力覆盖算法的性能。     

一种水下无线传感器网络的连通性覆盖算法

研究了水下无线传感器网络随机布放条件下的连通性覆盖问题.针对节点通信半径小于2倍的感知半径时,不能达到连通性要求的问题,给出一种连通性最差情况下(通信半径等于感知半径),连通性覆盖算法唤醒机制.唤醒机制中,充分考虑能量以及节点之间的距离信息来选择进入工作状态的最佳节点,使得网络在满足覆盖性要求的基础上,保证了节点之间的连通性要求.最后通过仿真分析,验证方法有效性.     

无线传感器网络的任意覆盖率节点配置

研究了任意覆盖率下的无线传感器网络分布式节点自动配置问题. 首先, 针对正六边形拓扑架构下的网络覆盖, 给出了节点密集分布条件下的覆盖率与相邻工作节点间距的解析关系, 从而得到了理想条件下部分覆盖的最优节点配置. 考虑到实际系统中有限的节点密度和节点的随机分布, 进一步提出了一种可以在此条件下实现任意覆盖率的部分覆盖协同优化算法(Optimized collaborative partial coverage, OCPC). OCPC通过节点间的动态协同唤醒最接近于理想配置的工作节点并使其他节点睡眠以节省能量. 以尽可能少的工作节点达到网络的覆盖和连通需求并降低网络的能耗, 进而达到网络的感知任务和能量消耗的有效折衷. 仿真表明, OCPC可以有效地实现任意期望覆盖率下的网络配置并保持网络连通, 同时, 与经典覆盖算法PEAS (Probing environment and adaptive sleeping)和OGDC (Optimal geographic density control)相比, 在网络的节能方面也具有明显的优越性.     

一种基于顺序博弈的UWSNs覆盖控制算法

近年来,人们对海洋资源愈加重视,水下无线传感器网络在海洋数据监测、海洋军事、辅助导航等方面的应用引起了广泛关注。一个良好的水下无线传感器网络覆盖控制,首先能够降低覆盖冗余,优化网络空间资源的配置;其次可以减少节点的能耗、降低网络的成本并延长节点的生命周期,使水下无线传感器网络可以更好地完成目标水域环境感知任务。提出了一种基于顺序博弈的水下无线传感器网络覆盖控制方法,用于优化水下无线传感器网络的覆盖控制,期望能够降低节点的能耗,均衡节点之间的能量,最终实现延长网络的生命周期的目标。仿真实验表明,该算法能够提高网络覆盖率和延长网络生命周期。     

A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks

A wireless sensor network is composed of a large number of sensor nodes that are densely deployed in a sensing environment. The effectiveness of the wireless sensor networks depends to a large extent on the coverage provided by the sensor deployment scheme. In this paper, we present a sensor deployment scheme based on glowworm swarm optimization (GSO) to enhance the coverage after an initial random deployment of the sensors. Each sensor node is considered as individual glowworms emitting a luminant substance called luciferin and the intensity of the luciferin is dependent on the distance between the sensor node and its neighboring sensors. A sensor node is attracted towards its neighbors having lower intensity of luciferin and decides to move towards one of them. In this way, the coverage of the sensing field is maximized as the sensor nodes tend to move towards the region having lower sensor density. Simulation results show that our GSO-based sensor deployment approach can provide high coverage with limited movement of the sensor nodes.     

无线传感网络交叉覆盖节点路由删除仿真研究

针对无线传感网络中节点的覆盖范围较小,删除无用路由所用时间较长,导致网络覆盖率低和路由删除效率低的问题,提出无线传感网络交叉覆盖节点路由删除方法。建立节点覆盖模型,在节点覆盖模型的基础上将无线传感网络的覆盖率和连通性当做综合评价函数,构建无线传感网络交叉覆盖节点优化布局的数学模型,并采用罚函数结合无约束优化模型代替传统约束优化模型。运用自适应遗传算法求解无约束优化模型,实现无线传感网络中交叉覆盖节点的优化布局,进而删除无线传感网络中存在的无用路由。分析实验结果得出,所提方法的网络覆盖率高、路由删除效率高,说明所提方法实际应用性强。     

无线传感器网络的连通覆盖临界条件分析

连通与覆盖控制作为无线传感器网络中两个最基本的问题,取决于网络配置及节点的传播距离,反映了网络的感知质量与资源的优化分配。为了用最优化数量的传感器节点来改善和确保网络的连通与覆盖,在分析现有研究成果的基础上,提出了满足渐进连通覆盖的临界充分条件及必要条件。理论分析及仿真实验均表明,提出的临界条件更紧凑和规则化,有助于对无线传感器网络进行更细致的研究。     

基于组合加权分簇的三维无线传感器网络覆盖控制方法*

节点部署是无线传感器网络的一个基本问题。针对传感器节点的能量有限,如何在有限的能量下,实现对目标区域的覆盖最大化,本文提出了一种能量有效的三维传感器网络覆盖控制算法,根据节点的可用能量,与邻节点的平均距离以及连通度的组合加权值来确定簇首节点,并通过仿真实验验证了该方法的能量有效性,研究结果表明,基于组合加权分簇的覆盖控制方法在保证一定的覆盖率以及连通性的前提下,降低了节点能耗,均衡了网络能量,延长了传感器网络的生存时间。