共查询到19条相似文献,搜索用时 61 毫秒
1.
异常数据检测及异常类型识别有助于提高无线传感器网络的数据质量,基于分类的异常检测算法存在传感器数据分类特征提取困难,无法进一步区分异常数据类型等问题,而基于时空特征的异常检测方法存在过度依赖于数据的假设分布等问题。针对这些问题,提出一种融合数据流时空特征和多分类模型的异常检测算法,算法首先基于Markov链提取传感器数据流的时空特征,然后将时空特征作为多分类卷积神经网络模型的输入特征,对数据流进行异常检测及异常类型识别。结果表明:该算法在不同数据集上均表现出较高的检测准确率以及较低的漏检率和误检率,可以有效地检测无线传感器网络中的异常值并判断异常类型。 相似文献
2.
针对无线传感器网络中异常检测误报率高及节点间通信开销大的问题,提出了基于滑动窗口和置信度的无线传感器网络异常检测算法(ADABSWC)。该算法使用环境干扰因子量化监测环境中的不确定性,建立异常数据干扰区间识别滑动窗口中的异常数据。提出了数据异常度的计算方法,用来预判异常来源;然后引入多通信半径划分最佳邻域,利用相对熵计算节点信息置信度;根据节点信息置信度协同判定出节点异常数据的来源。通过仿真实验,ADABSWC算法在不同传感器节点规模下均体现了较好的性能。该算法与KNN-PSOELM、OFN算法相比,事件节点、错误节点的检测率均高于98%,且误报率均低于1.5%。实验结果表明,所提出的算法可保证高检测精度的同时控制误报率在较低水平,算法拥有较好的容错性能。 相似文献
3.
陈怡娜 《计算技术与自动化》2023,(2):178-183
提出了基于深度学习的异常数据检测的方法,精准检测到无线传感器异常数据并直观展现检测结果。基于无线传感器网络模型分簇原理,通过异常数据驱动的簇内数据融合机制,去除无线传感器网络中的无效数据,获取无线传感器网络有效数据融合结果。构建了具有4层隐含层的深度卷积神经网络,将预处理后的无线传感器网络数据作为模型输入,通过隐含层完成数据特征提取和映射后,由输出层输出异常数据检测结果。实验证明:该方法可有效融合不同类型数据,且网络节点平均能耗较低;包含4层隐含层的深度卷积神经网络平均分类精度高达98.44%,1000次迭代后隐含层的训练损失均趋于0,可实现无线传感器异常数据实时、直观、准确检测。 相似文献
4.
5.
6.
7.
在无线传感器网络中,传感器的能量时有限的,如果传感器的能量耗尽,那么无线传感网络的鲁棒性和寿命就会大大降低.因此,提出了基于模糊强化学习和果蝇优化的数据聚合机制,以最大限度地延长网络寿命,并进行高效数据聚合.首先,网格聚类用于簇的形成和簇头的选择,接着评估各个网格簇所有可能的数据聚合节点,然后采用模糊强化学习选取最佳数据聚合节点,最后利用果蝇优化算法动态定位整个无线传感网络的数据汇聚节点.仿真结果表明,提出的数据聚合方案在能耗和网络鲁棒性方面优于对比方案. 相似文献
8.
9.
近年来,无线传感器网络离群数据检测研究越来越受到人们的关注。无线传感器网络离群数据检测在火灾监测、欺诈和入侵检测等诸多领域都有非常重要的作用。针对无线传感器网络集中式离群数据检测算法能量消耗过快的问题,提出了一种基于密度的分布式离群数据检测算法,并通过引入时空关联性有效提高了检测精度。通过NS2仿真实验,验证了该分布式算法节省了能量消耗,同时保持了较高的检测准确率。 相似文献
10.
基于无线传感器网络的湿地水环境远程实时监测系统关键技术研究 总被引:16,自引:0,他引:16
对湿地水环境监测技术的研究现状与进展进行了概述.提出了基于无线传感器网络的湿地水环境远程实时监测系统的系统框架,重点讨论了基于自然光水下照射强度的浊度软测量技术、面向湿地水环境监测的三维无线传感器网络的节点覆盖算法、传感器节点健康状况诊断算法和小水域子网的网内数据聚合点的选举算法等关键问题. 相似文献
11.
Mahmood Safaei Abul Samad Ismail Hassan Chizari Maha Driss Wadii Boulila Shahla Asadi Mitra Safaei 《Software》2020,50(4):428-446
Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time-series-based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well-known algorithms using benchmark real-life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data. 相似文献
12.
在无线传感器网络(wireless sensor network, WSN)节点故障检测领域的研究过程中,故障检测准确率会受节点数据的不确定性和专家知识模糊性的影响。针对这一问题,本文提出了一种基于置信规则库(belief rule base, BRB)的WSN节点故障检测方法。首先,根据WSN工作原理及节点工作特性描述WSN节点故障检测过程;然后,从空间和时间2个维度对节点数据提取特征,建立基于空间和时间相关性的WSN节点故障检测模型;最后,利用Intel Lab Data无线传感器数据集进行案例研究以验证模型的有效性。结果证明,本文方法能够统筹利用专家知识和节点数据实现WSN节点故障检测。 相似文献
13.
“黑河流域生态水文传感器网络”试验是无线传感器网络技术在近地表观测领域的一次成功应用,通过分析该试验所采集的观测数据的特点,首次给出WSN数据的质量元素的规范内容,并设计出每种质量元素的判定算法,用于解决WSN数据的质量控制问题。之后提供了两个评价实例,其结果表明将这些质量元素判定算法应用到黑河观测数据自动综汇系统中,能够高效、合理地完成“黑河流域生态水文传感器网络”野外观测数据的质量控制工作。 相似文献
14.
15.
Deployment is a fundamental issue in Wireless Sensor Networks (WSNs). Indeed, the number and locations of sensors determine the topology of the WSN, which will further influence its performance. Usually, the sensor locations are precomputed based on a “perfect” sensor coverage model. However, in reality, there is an inherent uncertainty and imprecision associated with sensor readings. This issue impinges upon the success of any WSN deployment, and it is therefore important to consider it at the design stage. In contrast to existing work, this paper investigates the belief functions theory to design a unified approach for robust uncertainty-aware WSNs deployment. Specifically, we address the issue of handling uncertainty and information fusion for an efficient WSNs deployment by exploiting the belief functions reasoning framework. We present a flexible framework for target/event detection within the transferable belief model. Using this framework, we propose uncertainty-aware deployment algorithms that are able to determine the minimum number of sensors as well as their locations in such a way that full area coverage is provided. Related issues, such as connectivity, preferential coverage, challenging environments and sensor reliability, are also discussed. Simulation results, based on both synthetic data set and data traces collected in a real deployment for vehicle detection, are provided to demonstrate the ability of our approach to achieve an efficient WSNs deployment by exploiting a collaborative target/event detection scheme. Using the devised approach, we successfully deploy an experimental testbed for motion detection. The obtained results are reported, supported by comparison with other works. 相似文献
16.
基于马尔可夫链的无线传感器网络分布式调度方法 总被引:1,自引:0,他引:1
能量效率是无线传感器网络(Wireless sensor network, WSN)研究中的核心问题之一. 当节点采用电池供电时, 有限的能量限制了网络的生存周期, 从而对无线传感器网络的大规模应用提出了挑战. 本文基于马尔可夫链, 提出了一种实用的、协作分布式的调度方法, 并从理论上证明了该方法的收敛性. 该方法不仅可对节点的休眠/唤醒进行调度, 还可以对节点数据发送进行调度以减少数据冲突的发生. 仿真实验结果表明, 该方法能够有效地减少节点能量的消耗, 且对其他网络性能的影响较小. 相似文献
17.
黄旭 《计算机工程与科学》2015,37(4):711-718
提出了一种适用于无线传感器网络WSN的故障检测方法,该方法运用改进的递归神经网络MRNN为WSN的节点、节点的动态特性以及节点间的关系建立相关模型,对WSN节点进行识别和故障检测。MRNN的输入选择建模节点的先前输出值及其邻居节点的当前及先前输出值,模型基于一种新的改进的反向传播型神经网络,该神经网络的输入以及传感器网络的拓扑结构基于通用的非线性传感器模型。仿真实验将MRNN方法与卡尔曼滤波法进行了全面的比较。实验表明,MRNN在置信因子较小的情况下与卡尔曼滤波方法相比有较高的故障检测精度。 相似文献
18.
Recently, the cyber physical system has emerged as a promising direction to enrich the interactions between physical and virtual worlds. Meanwhile, a lot of research is dedicated to wireless sensor networks as an integral part of cyber physical systems. A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices that use sensors to monitor physical or environmental conditions. These autonomous devices, or nodes, combine with routers and a gateway to create a typical WSN system. Shrinking size and increasing deployment density of wireless sensor nodes implies the smaller equipped battery size. This means emerging wireless sensor nodes must compete for efficient energy utilization to increase the WSN lifetime. The network lifetime is defined as the time duration until the first sensor node in a network fails due to battery depletion. One solution for enhancing the lifetime of WSN is to utilize mobile agents. In this paper, we propose an agent-based approach that performs data processing and data aggregation decisions locally i.e., at nodes rather than bringing data back to a central processor (sink). Our proposed approach increases the network lifetime by generating an optimal routing path for mobile agents to transverse the network. The proposed approach consists of two phases. In the first phase, Dijkstra’s algorithm is used to generate a complete graph to connect all source nodes in a WSN. In the second phase, a genetic algorithm is used to generate the best-approximated route for mobile agents in a radio harsh environment to route the sensory data to the base-station. To demonstrate the feasibility of our approach, a formal analysis and experimental results are presented. 相似文献
19.
在无线传感器网络(WSN)中,容易因为故障节点存在冗余的故障属性、噪声数据以及数据可靠性等问题,从而产生传输错误数据,这将极大地消耗WSN节点中能量和带宽,向用户形成错误的决策。为此,提出了基于蚁群算法和BP神经网络模型的WSN节点故障检测方法。通过使用蚁群算法,使用户通过寻找优化路径来定位WSN节点的位置,通过这种随机搜索算法以及蚁群算法的搜索策略使用户对WSN故障节点的位置进行总体把握。然后又基于BP神经网络模型对获取的WSN故障节点信息进一步学习,在数据训练过程中,依据WSN故障节点预测误差,并进一步调整网络的权值和阈值,增加了故障诊断的精度。采用的算法对检测WSN故障节点具有较好的性能,使无线传感器网络的服务质量大大提高,增强了系统的稳定性,实验结果验证了算法的可行性和有效性。 相似文献