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.     

无线传感器网络分簇拓扑的覆盖区域节点调度优化算法研究

利用区域分割的方法建立了一种覆盖区域冗余节点的优化调度机制,实现对完全覆盖区域内冗余节点的休眠调度,并将该机制引入无线传感器网络的分簇结构中,提出一种基于分簇拓扑的节点调度优化算法。算法通过控制簇内冗余节点进行休眠,减少簇首的数据通信量和簇成员中工作的冗余节点个数,降低了网络能耗。仿真结果表明,与未考虑冗余节点休眠调度的分簇算法相比,该算法有效提高了网络能量利用率,延长了网络生命期。     

一种三维无线传感器网络节点调度算法

在三维无线传感器网络中为了满足指定的覆盖率要求,同时使活动节点数最少,提出了一种基于邻节点分类的调度算法。将邻居节点按距离分类,分析了每类邻居节点的网络覆盖率与活动节点数[k]之间的约束关系,根据此约束关系判断节点是否冗余,并通过回退机制休眠冗余节点。理论分析和仿真结果表明,该算法能在满足指定覆盖率的条件下有效判别和休眠冗余节点,从而延长网络的寿命。     

基于相对局部密度的三维节点调度算法

针对目前大多数节点调度算法在冗余节点退避休眠过程中能耗大的问题,提出了一种基于相对局部密度的三维节点调度方法。算法定义了节点相对局部密度的概念,首先根据冗余法则判断节点是否冗余,然后根据节点的剩余能量和相对局部密度让冗余节点以概率竞争的方式休眠,从而避免了节点退避休眠的能耗。仿真实验表明基于相对局部密度的三维节点调度方法能在满足指定的覆盖率的前提下,降低节点的能耗,延长网络的生存时间。     

基于闭合包围方法的传感器网络节点调度

在没有地理位置信息的情况下,针对存在大量冗余传感器节点的传感器网络的节点调度问题,设计了一种基于闭合包围的冗余节点调度算法(RNSCE算法).该算法首先定义了闭合包围的概念,并给出了其相关性质;然后,提出了基于闭合包围方法的冗余节点以及覆盖空洞边界节点的判定规则;最后,提出了冗余节点调度方法,包括节点的休眠调度和覆盖空洞的修复两个方面.仿真结果表明,本文算法可以很好地维持网络初始覆盖性能,并有效降低活跃节点的数量.     

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。     

物联网感知层高能效覆盖优化节点调度算法

为了更好地节约能量并有效解决节点不均等休眠问题, 探讨了基于容忍覆盖区域的节点调度算法, 并在此基础上进行了两方面改进：通过引入相对剩余能量水平参数Eremain优化覆盖冗余判断策略, 并通过增加“预活动”和“回退”两种节点状态来改进节点状态分布情况, 从而提出一种高能效覆盖优化节点调度算法ECO-NS。最后运用MATLAB对该算法进行了验证, 结果表明, 相对于同类算法, 该算法有效提升了网络覆盖质量, 延长了网络寿命。     

无线传感网络中覆盖保持的K-连通子集构造算法

构造无线传感网络中具有连通覆盖特性的节点子集是实现网络休眠调度、延长网络生命周期的关键技术之一,具有重要的研究意义.已有的研究大多侧重于k覆盖节点子集构造问题,由于k覆盖子集在一定条件下便满足k连通,故人们对k连通子集的构造问题研究较少,但通过构造k覆盖节点子集来实现k连通会耗费过多的节点,代价较大.因此,本文提出一个直接构造k连通1覆盖节点子集的算法-CPC,能够用较少的节点构造出一个既能满足网络的覆盖特性又能够满足k-连通特性的节点子集,使得在任意k-1个节点发生故障时,网络能够仍然保持连通.本文还对算法的正确性进行了严格证明,并通过仿真实验与相关算法进行了性能比较.结果表明,与已有的k覆盖算法相比,CPC算法能够节省约55%的节点数.     

一种基于扫描线的传感器节点调度算法

系统生命期对无线传感器网络至关重要。在保证监控性能的前提下，通过调度传感器节点，使得部分节点工作，部分节点休眠，可以有效延长系统生命期。现有调度算法的性能不是很好，该文提出了一种基于扫描线的传感器节点调度算法，该算法将节点的监控区域近似为一组扫描线，通过这些扫描线判断是否为冗余节点。仿真实验表明，此算法的性能良好，调度得到的活动节点数约为现有算法的91%，平均覆盖度约为1.84。     

基于Memetic算法的WSN分簇协议的研究 *

针对无线传感器网络的寿命和覆盖优化,提出了一种基于Memetic算法和节点休眠-唤醒调度策略的复合文化基因聚类协议(Composite Memetic Algorithm Clustering Protocol,CMACP)。算法首先运行文化基因算法初始化需要激活的节点并规划相邻冗余节点,其中遗传算法和局部搜索算法能保障得到最优的初始节点分布。随着网络的运行,当某个节点因能量耗尽而丢失覆盖目标时,休眠调度策略选择激活最优相邻节点弥补覆盖漏洞。仿真实验表明,与其他WSN分簇协议相比,CMACP能较好的延长WSN稳定周期生存时间,并且提高WSN对感知区域的覆盖能力。     

基于能量均衡的最大化覆盖目标算法

目标覆盖问题是无线传感网络WSNs(Wireless sensor networks)最重要的问题之一.每个目标至少被一个传感节点覆盖,为此提出基于能量均衡的最大化覆盖目标EMNL(Energy-balance-based Maximizing Network Lifetime)算法.EMNL算法将所有传感节点划分不同的传感节点覆盖区SC(Sensor Cover),致使每个SC能够维持对所有目标监测一个固定时间.通过有选择性选择一个SC活动,而其他SC休眠,进而提高能量利用率,延长了网络寿命.EMNL算法构建了不同不相邻SC,进而最大化网络寿命.最后,建立仿真环境,并进行性能仿真.此环境下的数据表明,在EMNL算法有效地扩延生存时间,也提升了覆盖率.     

On redundant coverage maximization in wireless visual sensor networks: Evolutionary algorithms for multi-objective optimization

Wireless visual sensor networks can provide valuable information for a variety of monitoring and control applications. Frequently, a set of targets must be covered by visual sensors, as such visual sensing redundancy is a desired condition specially when applications have availability requirements for multiple coverage perspectives. If visual sensors become rotatable, their sensing orientations can be adjusted to optimize coverage and redundancy, bringing different challenges as there may be different coverage optimization objectives. Actually, the specific issue of redundant coverage maximization is inherently a multi-objective problem, but usual approaches are not designed accordingly to compute visual sensing redundancy. This article proposes two different evolutionary algorithms that exploit the multi-objective nature of the redundant coverage maximization problem: a lexicographic ”a priori” algorithm and a NSGA-II ”a posteriori” algorithm. The performance of both algorithms are compared, using a previously proposed single-objective greedy-based algorithm as a reference. Numerical results outline the benefits of employing evolutionary algorithms for adjustments of sensors’ orientations, potentially benefiting deployment and management of wireless visual sensor networks for different monitoring scenarios.     

含障碍物区域的移动传感器网络部署算法*

针对感知区域内含障碍物的移动传感器网络(MSN)的优化问题,提出一种基于免疫算法与维诺图的移动传感器部署算法。共分为两个阶段：第一阶段,使用多目标免疫算法最大化网络覆盖率并最小化MSN移动与感知的能耗,使用维诺图调节传感器的感知范围;第二阶段：采用基于二值抗体的免疫算法调节传感器的状态,在保持高覆盖率的前提下,最小化节点感知与冗余覆盖引起的能耗。多组仿真实验结果显示,本算法对于有、无障碍的两种场景均获得了较好的覆盖率与能耗指标,并实现了较低的处理时间。     

A simple coverage-evaluating approach for wireless sensor networks with arbitrary sensing areas

We present a simple and fast deterministic solution to the area coverage problem in wireless sensor networks. The task is to determine whether all points in a region are covered by a given set of sensors, where each sensor may have any arbitrary sensing shape. We transform the area coverage problem to the intersection points' coverage problem, which is simpler and more suitable for evaluating the area coverage problem than previous approaches in our view.     

传感器网络中数据驱动的睡眠调度机制

在能量受限的传感器网络中,尽量延长网络寿命同时保证服务质量(如感知覆盖和数据完整)是关键的研究问题.节点睡眠调度能有效延长网络寿命.研究数据驱动的睡眠调度机制,利用感知数据的时空相关性识别冗余节点.核心思想是用非参数回归方法为节点建立预测模型,求解最大支配数的节点支配集,调度多个支配集轮流工作.睡眠节点的数据可以由支配集节点恢复.分别给出集中式、半分布式和分布式3个睡眠调度方法.据知,这是第1个将统计回归模型用于睡眠调度并扩展到大规模网络的研究.实验结果表明,该方法能够有效地减少活跃节点个数,节省能耗从而延长网络寿命,同时在用户指定误差范围内保证数据的完整性.     

C3: an energy-efficient protocol for coverage,connectivity and communication in WSNs

The lower layer of ubiquitous and pervasive systems consists of wireless ad hoc and sensor networks. In wireless sensor networks (WSNs), sensors consume most of their energy in data transmission and idle listening. Hence, efficient usage of energy can be ensured by improved protocols for topology control (i.e., coverage and connectivity), sleep scheduling, communication, and aggregation and compression of data. Though several protocols have been proposed for this purpose, they are not energy-efficient. We propose an integrated and energy-efficient protocol for Coverage, Connectivity, and Communication (C3) in WSNs. The C3 protocol uses received signal strength indicator to divide the network into virtual rings, defines clusters with clusterheads more probably at alternating rings, defines dings that are rings inside a cluster and uses triangular tessellation to identify redundant nodes, and communicates data to sink through clusterheads and gateways. The proposed protocol strives for near-optimal deployment, load balancing, and energy-efficient communication. Simulation results show that the C3 protocol ensures partial coverage of more than 90 % of the total deployment area, ensures one connected network, and facilitates energy-efficient communication while expending only one-fourth of the energy compared to other related protocols such as the coverage and connectivity protocol, and the layered diffusion-based coverage control.     

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.     

HACH: Heuristic Algorithm for Clustering Hierarchy protocol in wireless sensor networks

Wireless sensor networks (WSNs) require energy management protocols to efficiently use the energy supply constraints of battery-powered sensors to prolong its network lifetime. This paper proposes a novel Heuristic Algorithm for Clustering Hierarchy (HACH), which sequentially performs selection of inactive nodes and cluster head nodes at every round. Inactive node selection employs a stochastic sleep scheduling mechanism to determine the selection of nodes that can be put into sleep mode without adversely affecting network coverage. Also, the clustering algorithm uses a novel heuristic crossover operator to combine two different solutions to achieve an improved solution that enhances the distribution of cluster head nodes and coordinates energy consumption in WSNs. The proposed algorithm is evaluated via simulation experiments and compared with some existing algorithms. Our protocol shows improved performance in terms of extended lifetime and maintains favourable performances even under different energy heterogeneity settings.     

气味标记法优化免疫算法的覆盖策略

覆盖问题一直是无线多媒体传感器网络研究的重点领域。为了能够达到对目标区域有效覆盖的同时,减少网络能耗,延长网络寿命的目的,提出了一种气味标记法优化的免疫算法SMOIA（Scent Marking Optimization Immune Algorithm）。该方法利用改进的气味标记算法,在被覆盖区域设置必要的气味标记点,在这些点设置传感器节点能够有效提高对目标区域的覆盖率,减少冗余节点数量;使用免疫算法来避免一般算法容易陷入局部最优的问题。仿真实验表明,该算法能够有效提高网络覆盖率,减少网络中传感器节点数量,延长了网络寿命,并且收敛迅速。