基于TIN的无线传感器网络节点调度

针对场监测应用的特点提出了一种基于不规则三角网（TIN）的无线传感器网络节点调度方法。不同于已有的调度方法,此方法综合考虑了感知数据的动态性和空间性,减少了无线传输的电量消耗,同时保证了生成数据场的精度。模拟结果显示,在保证精度要求的前提下,基于TIN的调度方法比传统方法节省10%左右的通信电量消耗,并且具有良好的可扩展性。     

无线传感器网络中近似事件检测节点调度问题

在异构的无线传感器网络中,组成事件的不同类型的数据之间具有较强的相关性.为了减少能量的消耗,可以通过收集组成事件的部分感知数据来近似地检测该事件.提出近似事件检测的节点调度问题.将网络中的节点分成若干个集合,每个集合轮流工作,在保证收集到的部分数据能够有效地近似检测目标事件的同时,最大化网络的生命周期.证明了近似事件检测的节点调度问题是NP完全问题,并给出了基于贪心策略的近似比为1/2的近似算法.实验结果表明,方法能够有效延长网络的生命周期.     

一种基于时空相关性和异常检测的改进WSN节能策略

针对降低无线传感网能耗和保证数据精度之间的矛盾,提出了自适应采样数据并利用压缩感知进行压缩的方法.传统的基于压缩感知的无线传感器数据压缩,只采样部分节点的数据,对于未被采样节点感知到的突发事件很有可能发生漏检情况.本文方法检测所有节点上传的数据再进行压缩,可以有效避免漏检情况的发生.根据信号具有时间相关性的特点,本文采用基于方差分析ANOVA(Analysis of Variance)原理改进的传感器自适应采样频率方法,并考虑节点剩余能量,减少平稳信号的采集次数,均衡网络节点能耗.在LEACH协议基础上,对簇内数据进行压缩感知的方法对数据进行压缩从而减少数据的空间相关性并传输到汇聚节点,以减少网络整体的能量消耗.针对可能的漏报情况,提出一种改进的局部事件监测算法-滑动窗口局部事件监测SW-LED(Sliding Window-Local Event Detection)算法,实现了实时准确的异常检测和预警.实验结果表明本文方法既可以有效的均衡网络节点能耗以提高网络生存周期,同时保证了数据的精度,对于异常情况的识别率也有很大的提高.     

有向传感网分块区域p-覆盖节点调度算法研究

本文研究了分块区域p-覆盖的有向传感网节点调度问题,并提出了一种有效延长网络生存时间的节点调度方案。将区域划分为拥有不同监测需求的子区域,从有向传感器节点感知模型出发,设计了基于网格划分的节点感知范围度量方法,并在此基础上提出了分布式分区域节点调度算法DSSA( Distributed Subarea Sensor-schedule Algorithm),该算法是一个选取最少数量的节点去对每一个子区域进行p-覆盖的分布式贪心算法。算法同时还考虑了整体网络的连通。通过仿真深入评估了DSSA算法的性能。对比实验结果表明,DSSA算法可以显著延长网络生存时间。     

Storm集群下基于性能感知的负载均衡策略

Storm计算框架具有为多源异构大数据提供高效、快速、实时处理的能力.然而因Storm默认的调度策略使用了简单的轮询方法,无法根据集群动态的负载状态调整其任务的分配.针对该问题,提出了基于性能感知的负载均衡策略,根据节点的处理效率计算其性能感知值,并通过贪心调度保证节点的任务量与节点处理能力相匹配,以达到负载均衡的目的.通过与默认调度算法实验比较,结果表明该算法能够有效降低Storm处理时延,提高吞吐量和实现集群负载均衡.     

基于线性回归的无线传感器网络分布式数据采集优化策略

事件监测是无线传感器网络中最重要的应用之一,部署在监测区域内的传感器节点通过对感知数据信息的采集、处理和传输等基本操作完成具体的监测任务,在各种操作中,节点之间的数据传输是最消耗能量的.为了减少节点之间的通信数据量,达到降低网络能耗和延长网络生命周期的目的,该文提出了一种能量高效的基于线性回归的无线传感器网络分布式数据采集优化策略,通过应用线性回归分析方法构建感知数据模型,保持感知数据的特征,使节点仅传输回归模型的参数信息,代替传输实际监测的感知数据信息.仿真实验结果表明,文中提出的数据采集优化策略能通过较小的通信量有效地实现事件监测区域感知数据的预测和估计,降低网络的总能量消耗,延长网络的生命周期.     

基于预测的移动目标跟踪节点协同调度算法

节点调度是分布式传感网降低节点能耗、延长网络寿命的重要手段。针对基于传感网的目标跟踪应用,提出目标运动和节点感知模型,并在此基础上提出一种基于预测的移动目标跟踪节点协同调度算法。算法利用粒子滤波构造节点感知功效函数,利用一种均衡节点剩余能量的方法构造感知能效函数,选举出参与目标跟踪过程的感知组成员。仿真结果表明,该算法具有较高的目标跟踪和定位精度,且可有效平衡节点之间的能耗分布。     

有向感知可充电传感器节点的动态激活策略

在采集环境能量供电的有向感知传感器网络中,节点的激活、休眠调度及感知方向选择关系到对目标点的覆盖和监测成功率.基于节点采集能量速度慢于耗能速度及采集能量速度快于耗能速度两种情况,设计了逐次贪婪节点激活算法SGA和逐次贪婪节点休眠算法SGI.SGA算法逐次将能够给系统带来最大覆盖率增益的感知节点分配给其对应的时隙及感知方向;在为所有感知节点在所有时隙选择最佳感知方向激活的基础上,SGI算法逐次选择令系统覆盖率损失最小的感知节点,令其在相应的时隙休眠.理论证明,SGA算法及SGI算法至少能获得最优激活调度算法50%的系统平均覆盖率性能,且将最大化系统覆盖率的NP难问题转化为简单的多项式时间方法.最后,仿真验证了SGA算法和SGI算法的性能.     

基于改进免疫遗传算法的K覆盖异构传感器节点调度

为了实现无线传感器网络监测区域目标点的多重覆盖,设计了一种基于改进免疫遗传算法的异构传感器节点调度算法实现目标点的K重覆盖;首先,在传统的概率感知模型中加入剩余能量和感知能力因素,得到改进的概率感知模型,并设计了以最小化节点数并满足覆盖度约束的目标函数;然后,采用改进的免疫遗传算法对节点进行调度,最后,给出了具体的采用改进免疫遗传算法实现WSN异构节点调度的具体算法;仿真实验表明:文中方法能在满足K覆盖约束前提下实现监测区域的节点调度,与其他方法相比,活动节点数平均多7%,具有较长的网络生命周期和较少的网络能耗。     

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.     

Energy-efficient distributed wireless sensor network scheme for cluster detection

Detection of an environmental phenomenon, e.g. air pollution and oil spills, occurs when a group of sensors continuously produces similar readings (i.e. data streams) over a period of time. Thus, detection of environmental phenomena is basically a process of clustering the sensors' data streams, which commonly involves the processing of hundreds and maybe thousands of data streams in real time. Since the sensor network environment is wireless, energy conservation of the sensors would be the main concern. Thus in this paper, we propose an efficient and energy friendly distributed scheme to detect phenomena in a wireless sensor network (WSN). To achieve fast response, the proposed algorithms reduce the dimensionality of the streams. Then, each stream is represented by a point in a multi-dimensional grid. The algorithm uses a grid-based clustering technique to detect clusters of similar stream values. The processing of the algorithm is distributed among different elements of the WSN in a hierarchical topology for more energy efficiency. The paper shows the feasibility of the proposed fully distributed scheme by comparing it with three other WSN schemes in terms of clustering accuracy and energy consumption.     

基于网格耦合的数据流异常检测

基于网格的数据分析方法以网格为单位处理数据,避免了数据对象点对点的计算,极大提高了数据分析的效率。但是,传统基于网格的方法在数据分析过程中独立处理网格,忽略了网格之间的耦合关系,影响了分析的精确度。在应用网格检测数据流异常的过程中不再独立处理网格,而是考虑了网格之间的耦合关系,提出了一种基于网格耦合的数据流异常检测算法GCStream-OD。该算法通过网格耦合精确地表达了数据流对象之间的相关性,并通过剪枝策略提高算法的效率。在5个真实数据集上的实验结果表明,GCStream-OD算法具有较高的异常检测质量和效率。     

Sleep–wake up scheduling with probabilistic coverage model in sensor networks1

Energy optimisation is one of the important issues in the research of wireless sensor networks (WSNs). In the application of monitoring, a large number of sensors are scattered uniformly to cover a collection of points of interest (PoIs) distributed randomly in the monitored area. Since the energy of battery-powered sensor is limited in WSNs, sensors are scheduled to wake up in a large-scale sensor network application. In this paper, we consider how to reduce the energy consumption and prolong the lifetime of WSNs through wake-up scheduling with probabilistic sensing model in the large-scale application of monitoring. To extend the lifetime of sensor network, we need to balance the energy consumption of sensors so that there will not be too much redundant energy in some sensors before the WSN terminates. The detection probability and false alarm probability are taken into consideration to achieve a better performance and reveal the real sensing process which is characterised in the probabilistic sensing model. Data fusion is also introduced to utilise information of sensors so that a PoI in the monitored area may be covered by multiple sensors collaboratively, which will decrease the number of sensors that cover the monitored region. Based on the probabilistic model and data fusion, minimum weight probabilistic coverage problem is formulated in this paper. We also propose a greedy method and modified genetic algorithm based on the greedy method to address the problem. Simulation experiments are conducted to demonstrate the advantages of our proposed algorithms over existing work.     

一类多模块网格存储系统分散式控制方法

针对多模块网格存储系统中的多分拣点货物排列问题,提出一种解耦的分散式控制方法,将问题解耦为各存储模块的独立调度和模块之间的协调调度.模块内采用循环移动方式进行独立调度,模块间通过交换需求和模块状态控制协调过程.在不同参数条件下进行仿真实验,并与集中式控制方法进行对比仿真,结果表明分散式控制方法能够在各种工况下稳定有效地求解,且相比于集中式控制方法的结果更优,求解时间更短,验证了所提方法的有效性和通用性.     

基于双簇头网格调度的WSNs能量空洞缓解

设计了基于双簇头网格调度反馈结构的无线传感器网络（WSNs）非均布节点能量空洞缓解机制,并设计了主副簇头网格聚类算法,形成网格单元;依据节点身份（ID）与网格ID,定义鉴定规则,确定网格中的WSNs节点;构造了网格单元中心点的计算数学模型,依据该中心点坐标确定每个网格单元的簇头,调度网格内的节点;构建了主-副-相邻簇头的数据调度传输结构,有效分散了节点所承担的负载,并对本机制性能进行理论分析.仿真结果表明：与其他机制相比,在非均布节点环境下,该算法更能有效避免网络能量空洞,其节点持续时间最长,显著消除了“漏斗效应”.     

A new multiple decisions fusion rule for targets detection in multiple sensors distributed detection systems with data fusion

Currently, multiple sensors distributed detection systems with data fusion are used extensively in both civilian and military applications. The optimality of most detection fusion rules implemented in these systems relies on the knowledge of probability distributions for all distributed sensors. The overall detection performance of the central processor is often worse than expected due to instabilities of the sensors probability density functions. This paper proposes a new multiple decisions fusion rule for targets detection in distributed multiple sensor systems with data fusion. Unlike the published studies, in which the overall decision is based on single binary decision from each individual sensor and requires the knowledge of the sensors probability distributions, the proposed fusion method derives the overall decision based on multiple decisions from each individual sensor assuming that the probability distributions are not known. Therefore, the proposed fusion rule is insensitive to instabilities of the sensors probability distributions. The proposed multiple decisions fusion rule is derived and its overall performance is evaluated. Comparisons with the performance of single sensor, optimum hard detection, optimum centralized detection, and a multiple thresholds decision fusion, are also provided. The results show that the proposed multiple decisions fusion rule has higher performance than the optimum hard detection and the multiple thresholds detection systems. Thus it reduces the loss in performance between the optimum centralized detection and the optimum hard detection systems. Extension of the proposed method to the case of target detection when some probability density functions are known and applications to binary communication systems are also addressed.     

入侵目标视觉检测与识别的研究进展

入侵目标视觉检测与识别是无人机感知与规避技术领域中重点研究的课题,关键任务是无人机在飞行过程中通过机载传感器获取光学机载图像中并判断是否存在入侵目标,并对入侵目标进行检测识别和定位。入侵目标视觉检测与识别是将无人机安全集成到国家空域并保证无人机和有人机飞行安全的关键技术之一。本文主要围绕无人机感知与规避技术中的入侵目标视觉检测与识别技术方面,分析检测与识别入侵目标所面临的一些难点问题,综述当前入侵目标视觉检测与识别的主要处理方法,并指出了该领域存在的尚未解决的问题和展望未来的发展趋势。     

光传感器中劣化绝缘子非接触式检测技术研究

传统的检测技术在检测光传感器中的劣化绝缘子时,检测范围小,成本高,且工作人员必须要直接接触传感器。为了解决上述问题,提出了一种新的光传感器中劣化绝缘子检测技术。利用驱动控制电机、LD光源、调节电路、光学传感头和电源设定检测通道,通过光纤自动判断绝缘子串的空间分布特性,将分析数据与数据库中的标准数据进行对比,得到检测结果。为验证所提检测技术的工作效果,与传统技术进行实验对比,结果表明,运用所提技术对劣化绝缘子进行检测时,检测成本远低于传统方法,说明该技术能够实现非接触式检测,检测范围广,成本低,对于保障电网安全运行有重要的意义。     

Job shop bottleneck detection based on orthogonal experiment

A new prior-to-run bottleneck detection method based on orthogonal experiment (BD–OE) is proposed for job shop from the perspective of scheduling. It is built according to a new bottleneck definition which is proposed based on the principle of “Bottlenecks determine the performance of manufacturing systems” in TOC. The method takes the scheduling objective as estimated index, and constructs orthogonal trials by orthogonal array and dispatching rules to detect the bottleneck machine which has the greatest effect on the estimated index. It can detect the bottleneck machine before manufacturing systems run, and guide the following production process for the improvement of the performance of manufacturing systems. In order to evaluate the performance of the proposed method, different scales of job shop scheduling instances and two existing bottleneck detection methods are selected for simulation. The results show that the prior-to-run bottleneck detection method is feasible, efficient and easily implemented.     

网格和密度聚类算法在入侵检测中的应用

针对现有入侵检测算法中普遍存在的对输入顺序敏感的问题,提出了将网格和密度相结合的聚类算法应用到入侵检测中。该算法在CLIQUE基础上进行了改进,将非密集单元向密集单元移动,克服了CLIQUE算法聚类结果精确性不高的缺点。该算法结合了网格聚类的低时空复杂度和密度聚类的良好抗噪性的特点。仿真实验中采用了KDD-CUP99的测试数据集,实验结果证实了该算法的有效性和可行性。