无线传感器网络的覆盖优化机制研究

如何实现最优覆盖是无线传感器组网的一个基本问题.文章分析了传感器覆盖问题的背景,给出了节点调度方案的主要方法和技术原理,探讨了基于网络能量高效的覆盖优化与网络连通性之间的关系,重点阐述了实现区域覆盖和点覆盖的机制.对于覆盖薄弱地区,文章提出了采用分簇方式将覆盖地区划分成许多子区域或簇,用动态移动修复机制提供细粒度的网络监测与覆盖控制.文章认为调度传感器节点在休眠和活动模式之间进行切换,是一种重要节能方法;对于资源受限且拓扑动态变化的无线传感器网络,宜采用分布式和局部化的覆盖控制协议和算法.     

基于节点调度策略的能量有效覆盖算法

对监测区域进行有效地覆盖以及最大限度延长网络生存周期是无线传感器网络研究的重点课题之一。为此,提出了一种基于节点调度策略的能量有效覆盖算法,该算法通过泊松分布模型构造节点概率密度公式,依照节点密度公式对所关注的目标进行有效覆盖;另一方面,通过节点自身状态调度机制更新以及对邻居节点进行匹配调度的动态转换,使得传感器节点剩余能量与节点消耗能量趋于平衡,从而达到延长网络生存周期。仿真实验结果表明,该算法可实现对监测区域的有效覆盖,同时优化了网络资源的配置,延长了网络生存周期。     

无线传感器网络中的分布式Voronoi覆盖控制算法

以覆盖部分目标区域的传感器网络为研究背景,在通信半径不小于2倍传感半径的条件下,提出了一种维持网络原有覆盖范围、连通性的分布式Voronoi覆盖控制算法.首先,提出了一种基于局部Voronoi区域的冗余识别规则,其计算复杂度与节点密度无关;然后,提出了一种能量优先的Voronoi调度规则,通信相邻、局部Voronoi不相邻的节点可以同步执行冗余识别,提高分布式调度的收敛性.仿真实验表明,所提算法求解活跃节点的数量、平均覆盖度与集中式算法接近,优于一般的分布式算法,而在活跃节点的平均能量、算法性能等方面更加具有优势.     

一种基于网格划分的有向传感网时空覆盖调度算法

针对有向传感器网络中的时空覆盖调度问题进行研究,从有向传感器节点感知模型出发,设计了基于网格划分的网络基本区域生成方法,在此基础上提出了节点最大覆盖调度迭代选择MaxGreedy算法.通过仿真实验验证了网格划分方法的有效性,设计了一系列的时空覆盖算法对比实验,深入评估MaxGreedy算法的性能.对比试验结果表明,MaxGreedy算法可以高效地生成网络的节点调度模式,并在一定程度上提高网络的时空覆盖率.     

基于粒子群的无线传感网络节点休眠调度算法

针对传统节点休眠调度算法中覆盖率低、活跃节点数量多以及能量消耗不均匀的问题,基于可信信息覆盖模型,提出一种基于粒子群优化算法(Particle Swarm Optimization,PSO)的无线传感网络节点休眠调度算法.算法充分利用可信信息覆盖模型的优势构建最优的可信信息覆盖集合和簇头候选集合,从可信信息覆盖集合和簇...     

无线传感器网络基于参数可调增强型覆盖控制算法

覆盖问题是无线传感器网络领域的一个基本问题,也是无线传感器网络特性当中的一个重点问题.如何通过某种算法达到以最少传感器节点对监测区域的有效覆盖已成为目前研究的一项重要课题.因此,提出一种增强型覆盖控制算法(Enhanced Coverage Control Algorithm, ECCA).该算法通过概率理论知识可以有效地求解出对监测区域进行有效覆盖下的最少节点,给出了传感器节点概率的期望值计算方法以及目标节点首次被传感器节点覆盖和多次覆盖后的期望值求解过程,验证随机变量相互之间不独立时的比例函数关系.仿真结果表明,ECCA算法可以使用较少的传感器节点数量完成对监测区域的有效覆盖,提高了对监测区域的覆盖质量.     

紧凑型多业务传送设备(MSTP)技术

MSTP在网络中的主要作用是完成传送功能,节点设备带宽调度能力和组网能力是光网络组织的基础.随着光网络节点覆盖区域不断向末端延伸,节点数目急剧增加,设备带宽调度和管理能力、组网能力成为网络组织的焦点.鉴于此,文章给出了一种紧凑型多业务综合接入设备的硬件设计及其软件网管监控设计.     

基于感知数据分析的传感器网络覆盖控制

提出“决策覆盖”的覆盖控制理论和方法.以保证有效覆盖度为目标,通过对传感器网络局部感知数据的实时分析,计算节点的工作优先级并且动态地调整局部物理覆盖度,实现节点状态的反馈调度.“决策覆盖”克服了现有覆盖控制在灵活性和适应性方面的缺陷,突破了在节点发生故障和网络遭受攻击后服务质量难以保证的瓶颈.结合传统的覆盖区域计算及概率分析的结论,本文对现有覆盖控制算法进行了改进,相对于原始算法,改进后的算法在覆盖有效性和健壮性上均有明显提升.     

传感器网络中基于数据融合的栅栏覆盖控制研究

该文采用概率性感知模型,并利用数据融合技术构造虚拟节点来增加节点覆盖区域。在此基础上,提出一种栅栏覆盖控制算法。算法借助分治法构造栅栏,以减少节点间通信开销;并调度传感器使冗余节点睡眠,达到减少能耗和延长网络寿命的目的。分析和实验结果表明,针对所提问题设计的模型和算法可有效增加节点覆盖范围及节点间最大间隔距离,且在栅栏数、网络寿命等性能上均优于基于节点监测数据未融合的栅栏覆盖控制算法。     

传感器网络高阶模糊覆盖分析

覆盖是传感器网络的一个基本问题.在网络节点部署后,人们往往想知道监控区域是否被部署的节点所充分覆盖.本文建立了传感器网络的模糊覆盖模型,并在此基础上试图在监控区域内找到一组关键点使得仅仅判断这些点的覆盖情况即可回答该区域是否被完全模糊覆盖,分析了高阶Voronoi图并给出了区域被完全模糊覆盖的充分条件.所提出的相应的判别算法运行时间为O(K2N+NlogN).     

传感器网络的任务双效节能调度研究

能源供应有限性是局限传感器网络的性能和存活寿命的重要因素,本文从传感器网络节点的任务调度出发,提出动态能量管理DPM和动态电压/频率调节DV/FS的双效处理器节能调度算法,即DV/FS-RM和DV/FS-EDF调度算法;在DPM动态控制空闲任务进入休眠的同时,在保证节点的实时性的前提下,通过DV/FS-RM或DV/FS-EDF算法降低处理器频率,达到更好的节能效果.实验显示,该节能任务调度算法使以电池为能源的传感器网络节点的生存期成倍地延长.     

基于改进微粒群算法的WSN节点调度

为了降低无线传感器网络监测区域节点能耗和延长网络生命周期,设计了一种基于改进微粒群算法的节点调度方法.首先,以网络覆盖率和休眠工作节点数为目标建立了系统的数学模型,然后设计了粒子的编码方式、适应度函数以及自适应动态惯性权重,并定义了使用改进的微粒群算法对传感器网络节点调度的具体算法.仿真实验表明,该方法能正确地实现无线传感器网络监测区域的节点调度,在迭代次数较少时,就能以较少的节点获得较高的网络覆盖率,且与其他方法相比,具有收敛速度快和全局寻优能力强的优点.     

Sleep scheduling with expected common coverage in wireless sensor networks

Sleep scheduling, which is putting some sensor nodes into sleep mode without harming network functionality, is a common method to reduce energy consumption in dense wireless sensor networks. This paper proposes a distributed and energy efficient sleep scheduling and routing scheme that can be used to extend the lifetime of a sensor network while maintaining a user defined coverage and connectivity. The scheme can activate and deactivate the three basic units of a sensor node (sensing, processing, and communication units) independently. The paper also provides a probabilistic method to estimate how much the sensing area of a node is covered by other active nodes in its neighborhood. The method is utilized by the proposed scheduling and routing scheme to reduce the control message overhead while deciding the next modes (full-active, semi-active, inactive/sleeping) of sensor nodes. We evaluated our estimation method and scheduling scheme via simulation experiments and compared our scheme also with another scheme. The results validate our probabilistic method for coverage estimation and show that our sleep scheduling and routing scheme can significantly increase the network lifetime while keeping the message complexity low and preserving both connectivity and coverage.     

异步传感器网络随机事件捕获在线调度方案

针对已有异步传感器网络中依据随机事件的随机特性进行节点休眠调度离线问题,给出描述事件随机特性随机变量分布参数的Bayes估计。讨论了瞬时捕获概率、传感器捕获事件能量效率和分布参数的Bayes估计值之间的关系,得到了传感器休眠调度和分布参数Bayes估计值之间的关系式。由此得到了传感器节点在线实时调整的休眠周期调度方案。最后进行了在线调度方案和离线调度方案模拟实验,对相应结果进行了对比分析。实验结果表明相较于离线调度方案,在线调度方案具有更好的适应性。     

Energy saving with node sleep and power control mechanisms for wireless sensor networks

Energy efficiency sleep scheduling in wireless sensor networks is one of the most crucial technologies.In this paper,we propose a simple and feasible synchronous node sleeping and waking mechanisms for small scale wireless sensor networks.Sensor nodes are divided into forwarding nodes and listening nodes.Beacon frame containing sleep command from the coordinator can be forwarded to listening nodes via forwarding nodes.All the nodes in the network can enter sleep at about the same time.Through such network s...     

Maximizing Lifetime of Target Coverage in Wireless Sensor Networks Using Learning Automata

In wireless sensor networks, when each target is covered by multiple sensors, we can schedule sensor nodes to monitor deployed targets in order to improve lifetime of network. In this paper, we propose an efficient scheduling method based on learning automata, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the proposed scheduling method can better prolong the lifetime of the network in comparison to similar existing methods.     

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

     

Energy‐Efficient Adaptive Dynamic Sensor Scheduling for Target Monitoring in Wireless Sensor Networks

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy‐efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption.     

Improving Low-Energy Adaptive Clustering Hierarchy Architectures with Sleep Mode for Wireless Sensor Networks

A wireless sensor network (WSN) is composed of sensor nodes whose energy is battery-powered. Therefore, the energy is limited. This paper aims to improve the energy efficiency of sensor nodes in order to extend the lifetime of WSNs. In this paper, we propose four new hierarchical clustering topology architectures: random cluster head and sub-cluster head (RCHSCH), random cluster head and max energy sub-cluster head (RCHMESCH), random cluster head and sub-cluster head with sleep mode (RCHSCHSM) and random cluster head and max energy sub-cluster head with sleep mode (RCHMESCHSM). Our proposed architectures involve three-layers and are based on low-energy adaptive clustering hierarchy (LEACH) architecture. Notably, RCHSCH can improve upon cluster head death within the LEACH architecture. In addition, we develop a sleep mode for sensor nodes based on correlations among sensor data within sub-clusters in RCHSCHSM. Thus, we can reduce the energy consumption of the sensor node and increase energy efficiency. From the simulation results, our proposed RCHSCH, RCHMESCH, RCHSCHSM and RCHMESCHSM architectures perform better than the LEACH architecture in terms of initial node death, the number of nodes alive and total residual energy. Furthermore, we find the performance of RCHMESCHSM architecture to be optimal in the set of all available architectures.     

An energy‐efficient node‐scheduling scheme for wireless sensor networks based on minimum dominating sets

One of the major requirements for new wireless sensor networks is to extend the lifetime of the network. Node‐scheduling techniques have been used extensively for this purpose. Some existing approaches rely mainly on location information through global positioning system (GPS) devices for designing efficient scheduling strategies. However, integration of GPS devices with sensor nodes is expensive and increases the cost of deployment dramatically. In this paper we present a location‐free solution for node scheduling. Our scheme is based on a graph theoretical approach using minimum dominating sets. We propose a heuristic to extract a collection of dominating sets. Each set consists of a group of working nodes which ensures a high level of network coverage. At each round, one set is responsible for covering the sensor field while the nodes in other sets are in sleep mode. We evaluate our solution through simulations and discuss our future research directions. Copyright © 2008 John Wiley & Sons, Ltd.