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.     

监测无线传感器网络休眠调度算法

休眠调度设计是无线传感器网络一种重要的通信节能方法。针对监测典型应用,为了实现长时间的监测应用要求,充分利用冗余部署提供的能量资源,提出了一种能量相关的分布式自适应休眠调度算法。算法利用极大独立集构建思想,结合节点层次级别、实时的能量消耗、连通度等信息动态选择连通支配节点集作为网络骨干,使得网络活跃节点数量最小化。仿真试验分析表明,算法能够有效地利用冗余节点提供的能量资源,扩展了网络的生命周期。     

基于联合感知模型的无线传感器网络连通性覆盖协议

本文研究在没有节点位置信息的情况下,如何有效地保证节点采用联合感知模型且节点通信范围多级可调的无线传感器网络的覆盖质量和连通性.给出了在节点随机部署方式下,网络覆盖质量和网络连通性与工作节点个数、监测区域面积和节点性能参数的关系;设计了一个选取最少工作节点保证网络覆盖质量和网络连通性的调度控制算法(SCA),该算法力求...     

一种无线传感器网络的自适应休眠调度算法

针对数字航道观测场景变化以及传感器节点能量有限的特点,提出一种将无线传感器网络用于数字航道观测时的自适应休眠调度算法.该算法能感知数字航道观测场景的变化,并据此调整节点的状态.仿真结果表明提出的算法与已有的休眠调度算法对比,更加适应观测场景的变化,从而延长网络生命周期.     

无线传感器网络路由协议中休眠调度算法的研究

在分析无线传感器网络节能要求的基础上对无线传感器网络路由协议中使用的休眠调度算法进行了讨论,内容包括各种休眠调度算法的基本实现方法、考虑因素以及涉及到的关键技术.最后指明休眠调度对传感器网络节能的重要性.     

分簇无线传感器网络动态时隙分配MAC协议

为降低大规模无线传感器网络的平均能耗,提出了一种基于动态分配的调度型无线传感器网络MAC协议（SDC-MAC）。该协议簇间使用FDMA方式分配无线信道,簇内通过TDMA方式给各个节点分配可变长的时隙。随着簇结构的变化,簇头通过时隙分配通知,对簇内节点的时隙分配进行动态调整,簇成员节点则根据控制信息进行休眠和唤醒。仿真结果显示,该算法有效地降低了网络的平均能耗,当网络流量高时还可降低平均数据包时延。     

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.     

基于拓扑重构的水下移动无线传感器网络拓扑优化

针对水下移动无线传感器网络（MUWSN, mobile underwater wireless sensor networks）拓扑随洋流动态演化对其网络性能会产生很大影响,提出了一种基于拓扑重构的水下移动无线传感器网络拓扑优化方法,首先通过模拟鱼群行为对传感器节点位置进行调整,优化网络覆盖度;其次,利用冗余节点修复网络中不连通位置,消除关键节点,优化网络连通性,最后,通过仿真对比实验验证了该方法的合理性和有效性。实验结果表明,所提算法能在较低能耗下,保证网络覆盖度长期维持在97%左右,连通率达到89%以上。     

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...     

基于全网休眠的ZigBee网络节能算法

无线传感器网络节点一般由电池供电而且部署后需要长时间工作,这就导致节点的能量成为网络运行的瓶颈。针对无线传感器网络节点能量受限的问题,提出了一种基于全网休眠的节能新算法。该算法使终端节点、路由节点和汇聚节点都能得到休眠,从而为网络中每一种节点节约能量;同时,为路由节点设计了长、短两种休眠策略,在不影响数据收发的前提下减少能耗。基于CC2630芯片的实验结果表明:与现有相关算法相比,该新算法使节点能耗降低10%。     

Polytype target coverage scheme for heterogeneous wireless sensor networks using linear programming

Sensing coverage is one of fundamental problems in wireless sensor networks. In this paper, we investigate the polytype target coverage problem in heterogeneous wireless sensor networks where each sensor is equipped with multiple sensing units and each type of sensing unit can sense an attribute of multiple targets. How to schedule multiple sensing units of a sensor to cover multiple targets becomes a new challenging problem. This problem is formulated as an integer linear programming problem for maximizing the network lifetime. We propose a novel energy‐efficient target coverage algorithm to solve this problem based on clustering architecture. Being aware of the coverage capability and residual energy of sensor nodes, the clusterhead node in each cluster schedules the appropriate sensing units of sensor nodes that are in the active status to cover multiple targets in an optimal way. Extensive simulations have been carried out to validate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Distributed Energy Optimization for Target Tracking in Wireless Sensor Networks

Energy constraint is an important issue in wireless sensor networks. This paper proposes a distributed energy optimization method for target tracking applications. Sensor nodes are clustered by maximum entropy clustering. Then, the sensing field is divided for parallel sensor deployment optimization. For each cluster, the coverage and energy metrics are calculated by grid exclusion algorithm and Dijkstra's algorithm, respectively. Cluster heads perform parallel particle swarm optimization to maximize the coverage metric and minimize the energy metric. Particle filter is improved by combining the radial basis function network, which constructs the process model. Thus, the target position is predicted by the improved particle filter. Dynamic awakening and optimal sensing scheme are then discussed in dynamic energy management mechanism. A group of sensor nodes which are located in the vicinity of the target will be awakened up and have the opportunity to report their data. The selection of sensor node is optimized considering sensing accuracy and energy consumption. Experimental results verify that energy efficiency of wireless sensor network is enhanced by parallel particle swarm optimization, dynamic awakening approach, and sensor node selection.     

基于感知距离调节的无线传感器网络节能区域覆盖

传感器节点能够感知的物理世界的最远距离称为节点的感知距离。该文研究了基于节点感知距离调节的无线传感器网络节能区域覆盖方案,该方案通过设定合理的节点感知距离,使得传感器网络在满足区域覆盖要求的同时,能量消耗最低。首先将区域覆盖性能和网络能量消耗模化成网络节点感知距离的函数,然后将节能覆盖问题模化成带约束条件的优化问题,最后给出了基于网络区域划分的优化模型求解方法。仿真结果表明,与传统覆盖方案比较,所提方案在满足覆盖要求的同时,有效降低了网络能量消耗。     

多媒体传感网中一种基于优化覆盖及压缩代价评估的节能机制

为降低无线多媒体传感网能耗,利用贪婪算法调整节点传感方向,实现优化覆盖并降低了传感开销.同时,对节点数据压缩代价进行评估,使其以最小的能量代价传输数据,并根据多媒体事件流规律,优化网络生存时间.仿真结果表明,本方法在延长网络生存时间方面表现出了较好性能.     

A Pre-Determined Nodes Deployment Strategy of Two-Tiered Wireless Sensor Networks Based on Minimizing Cost

Nodes deployment is a fundamental factor in determining the connectivity, coverage, lifetime and cost of wireless sensor networks. In this paper, a two-tiered wireless sensor networks consisting of sensor clusters and a base station is considered. Within a sensor cluster, there are many sensor nodes and a relay node. We focus on the deployment strategy for sensor nodes and relay nodes to minimize cost under some constraints. Several means are used. The regular hexagonal cell architecture is employed to build networks. Based on the analysis of energy consumption of sensors and cost of network, an integer programming model is presented to minimize the cost. By the model, number of layers of sensor cluster is determined. In order to balance the energy consumption of sensors on the identical layer, a uniform load routing algorithm is used. The numerical analysis and simulation results show that the waste of energy and cost of wireless sensor networks can be effectively reduced by using the strategy.     

Square partition‐based node scheduling algorithm for wireless passive sensor networks

Wireless passive sensor networks play an important role in solving the energy limitation of nodes in the Internet of Things, and node scheduling is a significant method used to improve the energy utilization of nodes. In this work, an unused energy model based on analyzing the energy consumption characteristics of passive nodes is proposed because no unified model of passive sensor nodes is reported in previous studies. A rapid square partition clustering method is proposed according to the analysis of the relation between the sensing and communication radii of nodes, and the secondary grouping and node scheduling in each cluster are implemented to ensure the coverage rate of networks. Experimental results show that the state distribution of nodes in the proposed algorithm is favorable. The performance of the proposed algorithm is significantly affected by the P ratio between the working and charging powers of nodes. When the value of P is less than 100, the network coverage and connectivity rate are maintained at more than 95% and 90%, respectively, and are both higher than the existing algorithm.     

A Green Clustering Protocol for Mobile Sensor Network Using Particle Swarm Optimization

Energy consumption of sensor nodes is one of the crucial issues in prolonging the lifetime of wireless sensor networks. One of the methods that can improve the utilization of sensor nodes batteries is the clustering method. In this paper, we propose a green clustering protocol for mobile sensor networks using particle swarm optimization (PSO) algorithm. We define a new fitness function that can optimize the energy consumption of the whole network and minimize the relative distance between cluster heads and their respective member nodes. We also take into account the mobility factor when defining the cluster membership, so that the sensor nodes can join the cluster that has the similar mobility pattern. The performance of the proposed protocol is compared with well-known clustering protocols developed for wireless sensor networks such as LEACH (low-energy adaptive clustering hierarchy) and protocols designed for sensor networks with mobile nodes called CM-IR (clustering mobility-invalid round). In addition, we also modify the improved version of LEACH called MLEACH-C, so that it is applicable to the mobile sensor nodes environment. Simulation results demonstrate that the proposed protocol using PSO algorithm can improve the energy consumption of the network, achieve better network lifetime, and increase the data delivered at the base station.