WSN中能量有效的数据采集点动态选择算法

在无线传感器网络的数据采集问题研究中引入移动采集的概念,提出了一种能量有效的数据采集点动态选择算法。该算法根据每个数据采集周期中节点的能耗情况,动态计算出数据采集点集合,然后通过移动采集设备到各采集点完成数据采集。实验证明,算法能提高网络能量使用效率,延长网络使用寿命。     

无线传感器网络能耗分析与策略研究

无线传感器网络技术得到了广泛应用,但是该技术一直受能量的制约,因此能量始终是无线传感器网络的核心问题。通过对传感器节点结构与网络体系的分析,得出了传感器网络的能耗特性。为了延长无线传感器网络的生命周期,对传感器节点工作能耗与传感网络能耗做了具体研究,再从低功耗节点设计、网络协议及环境中能量补给三个方面总结出了一套有效的节能策略。     

基于稳定链路的WSNs拓扑优化算法设计及仿真研究

无线传感器网络在物联网中占有重要地位,其能够降低能耗并有效保障数据传输。然而,无线传感器网络底层拓扑结构通常存在网络链路质量低、网络能耗高、网络干扰等问题。针对现有网络链路不稳定的问题,提出具有稳定链路的幂律可调优化算法,并对其生成的拓扑动态性能进行研究。通过网络节点失效容错需求仿真和链路稳定性仿真,验证了幂律可调优化算法的准确性,为无线传感器网络拓扑优化提供参考。     

一种基于博弈论的无线传感器网络拓扑控制算法

无线传感器网络对节能有着很高的要求,拓扑控制能够优化网络拓扑,提高无线信道的空间复用率,是提高无线传感器网络能量效率的有效方法.文中提出了-种基于博弈论的无线传感器网络拓扑控制算法,设计了-个与节点度和发射功率有关的收益函数,使拓扑控制博弈存在纳什均衡,网络总收益函数最大,网络的能量效率最高.     

无线传感器网络的动态电压调节算法研究

对于能量有限的无线传感器网络,研究如何高效地利用有限能量具有重要意义.根据无线传感器网络多跳路由和拓扑易变的特点,提出一种基于任务驱动的含反馈的动态电压调节算法FB-DVS.该算法根据节点的任务集实时地调节节点的工作电压和频率,并通过反馈环节来修正误差,在保证任务实时性的前提下降低节点能耗.通过对仿真结果分析表明,改进的算法能有效地减少节点的能量消耗,延长无线传感器网络的生命周期.     

无线传感器网络时变充电和动态数据路由算法研究

利用无线充电设备周期地为无线传感器节点进行充电,可使传感器节点持续工作,解决无线传感器网络能量问题,但需设计合适的节点充电方案和网络路由方案。以无线充电设备驻站比最大化为优化目标,依次提出了充电及网络路由方案的连续时变模型、离散N+1阶段非线性模型及可解的离散N+1阶段线性模型,在证明模型合理性后最终求解。结论给出了具体网络的最优充电和路由策略。     

无线传感器网络覆盖优化控制技术分析

覆盖控制问题是无线传感器组网的一个基本问题，本文对传感节点的组成、定位方法，网络的拓扑结构等进行了简要的介绍。分析了无线传感器网络覆盖问题的背景，并针对覆盖冗余检测的主要方法和数学模型，讨论了无线传感器网络能量高效覆盖优化与网络连通性的关系。重点阐述了覆盖控制的技术手段，最后对无线传感器网络的覆盖控制关键技术进行了展望。     

一种射频能量捕获网络移动能量源均衡化充电策略

为解决传统电池供电传感器网络存在的电池不易更换、节点能量容易耗尽等问题,射频能量捕获技术已逐步应用于无线可充电传感器网络中.由于不同位置传感器节点的工作负荷不同,捕获能量也有差异,实现节点能量的均衡化分布可以有效地提高节点的存活率.考虑射频能量源移动充电的场景,在已知节点位置信息的条件下,设计合理均衡的路由方案和充电算法.首先将区域基于蜂窝六边形网格划分,分别对网格和节点分层,提出逐层传输的均衡式路由策略,然后给出无线充电小车的移动路径,对相邻两层内节点剩余能量的方差最小化问题建模,由内层向外层依次确定能量源在各停留点的充电时间.仿真结果表明,相比已有的均衡化充电方法,该策略可以明显提高节点剩余能量的均衡性,从而延长网络的生命周期.     

基于改进蚁群优化算法的无线传感器网络路由研究

无线传感器网络能够进行传感器的数据收集和传输,为用户提供多元信息,在不同的领域都具有广阔的应用前景,但无线传感网络的路由研究没有动态的拓扑结构以及全地址机制,并且资源有限,因此需要新的无线传感器路由算法进行支持.本文提出了基于改进蚁群优化算法的无线传感器网络路由算法,将改进的蚁群算法的自组织、动态和多路径等特性结合到无线传感网络路由研究中,用仿真实验进行分析表明该算法在网路平均能耗方面的显著改善,并证明其基本满足无线传感器网络的设计目标.     

面向能耗控制的无线传感器网络节点协议优化

能量受限是无线传感器网络一个显著的特征。对网络进行能耗优化并延长网络生命周期是无线传感器网络研究的重点。提出了面向能耗控制的无线传感器网络节点协议优化方法。针对网络中数据发送所占较大的能耗比重,通过对协议优化,对发送功率的参数设置方法进行改进,改变以往发射功率的固定参数设置法,通过终端节点之间的距离动态调整发送功率的方法,以达到节省能耗并延长网络生命周期的目的。仿真和实验结果表明,改进后的发射功率动态参数设置法较改进之前的固定参数设置方法能更多地节约网络能耗。     

Path planning algorithm for WCE with joint energy replenishment and data collection based on multi-objective optimization

Considering limited energy of the wireless charging equipment (WCE) in wireless rechargeable sensor network,an energy replenishment strategy and a data collection strategy are designed.On the basis of these,a path planning model for WCE with functions of joint energy replenishment and data collection based on multi-objective optimization is constructed with two optimization objectives,maximizing the total energy utility of WCE and minimizing the average delay of data transmission of all the sensor nodes in the network.To deal with it,a multi-objective ant colony optimization algorithm based on elitist strategy was proposed,where the state transition strategy and the pheromone updating strategy were improved.Then,the Pareto set was obtained in terms of this multi-objective optimization problem.The parameter setting of ant colony algorithm’s effects on the proposed algorithm were analyzed under 20 sensor nodes.50 groups of contrastive experiments show that the average number of energy utilization obtained by ES-MOAC algorithm is 4.53% higher than that of NSGA-II algorithm.The average number of average delay of all node data transmission obtained by ES-MOAC algorithm is 5.12% lower than that of NSGA-II algorithm.     

射频能量捕获传感网移动能量源的布置策略研究

射频能量捕获传感网（RF Energy Harvesting Wireless Sensor Network,RFEH-WSN）由专用射频能量源设备（Energy Transmitter,ET）和具有射频能量捕获功能的传感器节点（Energy Harvesting Recevier,简称EHR）组成.该网络解决了传感器网络中电池不易更换与节点能量容易耗尽的问题,使得RFEH-WSN应用前景更加广阔.RFEH-WSN应用中一个值得研究的问题是如何布置ET的充电位置,降低ET能耗且提高覆盖率.已有的工作主要考虑ET布置中单目标优化问题,如最小充电时间、最小功耗、最大覆盖率等.本文以时间最小和覆盖率最大为目标建立多目标优化模型,并提出利用粒子群算法（Particle Swarm Optimization,PSO）求解多目标函数（Multiple Object Program,MOP）的低复杂度近似算法,获得了最优Pareto解集.仿真结果表明,多目标优化可以满足不同情况的需求,提高充电效用.     

Demand‐based charging strategy for wireless rechargeable sensor networks

A wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a widespread research problem. In this paper, we propose a demand‐based charging strategy (DBCS) for WRSNs. We improved the charging programming in four ways: clustering method, selecting to‐be‐charged nodes, charging path, and charging schedule. First, we proposed a multipoint improved K‐means (MIKmeans) clustering algorithm to balance the energy consumption, which can group nodes based on location, residual energy, and historical contribution. Second, the dynamic selection algorithm for charging nodes (DSACN) was proposed to select on‐demand charging nodes. Third, we designed simulated annealing based on performance and efficiency (SABPE) to optimize the charging path for a mobile charging vehicle (MCV) and reduce the charging time. Last, we proposed the DBCS to enhance the efficiency of the MCV. Simulations reveal that the strategy can achieve better performance in terms of reducing the charging path, thus increasing communication effectiveness and residual energy utility.     

无线充电及其在图像采集粮虫检测传感器网络中的应用

随着科技的发展,无线充电技术在各个领域的应用也越来越广泛,比如在手机、电脑、相机、汽车等行业的部分最新产品中已经使用了无线充电技术。介绍了一种简便高效的无线充电方案,应用于埋入式图像采集粮虫检测系统,以确保传感器网络节点能够方便、安全、稳定地工作。该系统的工作环境特殊,无线充电技术有无需物理接触的优势,可以合理有效地替代传统的有线充电方法。使用高度集成的XKT-408A作为系统的发射芯片,再配合极少的外部元件就可以制作可靠稳定的无线充电器,不仅保证了无线充电的传输效率,也大大降低传感器网络节点的体积。     

Visualization of dynamic fault tolerance rerouting for data traffic in wireless sensor network

In wireless sensor network environments, a phenomenon of data concentration may occur in one or certain sensor nodes in the process of transmitting sensing data from a source sensor node to a sink node. In this case, the overhead that occurs in the sensor node affects the performance of the entire sensor network. In addition, in the sensor network, excessive sensing data traffic or data loss may occur depending on the variability of the topology of the sensor network. In this paper, visualization for dynamic rerouting is designed and implemented, which visibly provides packet movement routes between sensor nodes and transmitted packet traffic transmission capacity to Geography Markup Language‐based maps having GPS coordinate information. A mechanism for the visualization for dynamic rerouting to detect sensing data overheads and sensor node faults occurring in sensor networks and dynamically rerouting of data is proposed. In addition, information on rerouting route paths from source sensors to sink nodes is visually provided. Copyright © 2012 John Wiley & Sons, Ltd.     

Multihop data gathering in wireless sensor networks with a mobile sink

Network performance can be improved by using a mobile sink (MS) to collect sensed data in a wireless sensor network. In this paper, we design an efficient trajectory for MS, collecting data from sensor nodes in a multihop fashion, with the aim of prolonging the network lifetime. Considering event‐driven applications, we present an approach to jointly determine the optimal trajectory for MS and data paths and transmission rates from source nodes to MS, without considering any rendezvous points. In these applications, an MS is supposed to harvest the data from source nodes in a given time‐slot. We first show that this problem is in form of a mixed integer nonlinear programming model, which is NP‐hard. Then, to achieve an approximate solution, we divide the mentioned problem into 2 simple subproblems. In fact, after determining an approximate zone for the trajectory of MS, the optimal data paths and transmission rates from source nodes to the MS are obtained through a mathematical optimization model. Finally, to illustrate the efficiency of the proposed approach, we compare the performance of our algorithm to an rendezvous point–based and also the state‐of‐the‐art approach in different scenarios.     

Autonomous Reconfiguration in Free-Space Optical Sensor Networks

This research focuses on the physical and logical control and reconfigurability of network topologies through intelligent and dynamic rearrangement of nodes in an optical wireless sensor network. We address high data rate sensor networks (e.g., infrastructure monitoring; surveillance), which consist of gigabit per second, narrow beam, free-space optical links between fixed and/or mobile nodes. In our approach, the seamless operation of such networks requires maintenance of wireless link connectivity and quality and at all times, amidst, for example, changing atmospheric, and traffic and platform conditions. This is achieved by dynamic reconfiguration through topology control. We address the problem of dynamic formulation of topologies, which contain only two transceivers per communications node or switch. The task of reconfiguration requires the formation of a biconnected graph or a ring topology. The problem is similar to the traveling salesman problem and is NP complete. We address the mixed integer programming formulation of this problem, and show that it does not scale even for a small network. We then focus on heuristics for dynamic, autonomous reconfiguration. Using simulations, we investigate tradeoff between solution quality and computational time. We also investigate the effectiveness of these dynamic reconfiguration heuristics compared to fixed, degraded topologies.     

Energy equalizing routing for fast data gathering in wireless sensor networks

Energy saving and fast responding of data gathering are two crucial factors for the performance of wireless sensor networks. A dynamic tree based energy equalizing routing scheme (DTEER) was proposed to make an effort to gather data along with low energy consumption and low time delay. DTEER introduces a dynamic multi-hop route selecting scheme based on weight-value and height-value to form a dynamic tree and a mechanism similar to token passing to elect the root of the tree. DTEER can simply and rapidly organize all the nodes with low overhead and is robust enough to the topology changes. When compared with power-efficient gathering in sensor information systems (PEGASIS) and the hybrid, energy- efficient, distributed clustering approach (HEED), the simulation results show that DTEER achieves its intention of consuming less energy, equalizing the energy consumption of all the nodes, alleviating the data gathering delay, as well as extending the network lifetime perfectly.