Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

The fault diagnosis in wireless sensor networks is one of the most important topics in the recent years of research work. The problem of fault diagnosis in wireless sensor network can be resembled with artificial immune system in many different ways. In this paper, a detection algorithm has been proposed to identify faulty sensor nodes using clonal selection principle of artificial immune system, and then the faults are classified into permanent, intermittent, and transient fault using the probabilistic neural network approach. After the actual fault status is detected, the faulty nodes are isolated in the isolation phase. The performance metrics such as detection accuracy, false alarm rate, false‐positive rate, fault classification accuracy, false classification rate, diagnosis latency, and energy consumption are used to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm gives superior results as compared with existing algorithms in terms of the performance metrics. The fault classification performance is measured by fault classification accuracy and false classification rate. It has also seen that the proposed algorithm provides less diagnosis latency and consumes less energy than that of the existing algorithms proposed by Mohapatra et al, Panda et al, and Elhadef et al for wireless sensor network.     

Composite Fault Diagnosis in Wireless Sensor Networks Using Neural Networks

Wireless sensor networks (WSNs) are spatially distributed devices to support various applications. The undesirable behavior of the sensor node affects the computational efficiency and quality of service. Fault detection, identification, and isolation in WSNs will increase assurance of quality, reliability, and safety. In this paper, a novel neural network based fault diagnosis algorithm is proposed for WSNs to handle the composite fault environment. Composite fault includes hard, soft, intermittent, and transient faults. The proposed fault diagnosis protocol is based on gradient descent and evolutionary approach. It detects, diagnose, and isolate the faulty nodes in the network. The proposed protocol works in four phases such as clustering phase, communication phase, fault detection and classification phase, and isolation phase. Simulation results show that the proposed protocol performs better than the existing protocols in terms of detection accuracy, false alarm rate, false positive rate, and detection latency.

     

An efficient approach for outlier detection in big sensor data of health care

In recent years, wireless sensor networks are pervasive and are generating tons of data every second. Performing outlier detection to detect faulty sensors from such a large amount of data becomes a challenging task. Most of the existing techniques for outlier detection in wireless sensor networks concentrate only on contents of the data source without considering correlation among different data attributes. Moreover, these methods are not scalable to big data. To address these 2 limitations, this paper proposes an outlier detection approach based on correlation and dynamic SMO (sequential minimal optimization) regression that is scalable to big data. Initially, correlation is used to find out strongly correlated attributes and then the point anomalous nodes are detected using dynamic SMO regression. For fast processing of big data, Hadoop MapReduce framework is used. The experimental analysis demonstrates that the proposed approach efficiently detects the point and contextual anomalies and reduces the number of false alarms. For experiments, real data of sensors used in body sensor networks are taken from Physionet database.     

A scalable correlation‐based approach for outlier detection in wireless body sensor networks

Outlier detection is one of the prominent research domain in the field of data mining and big data analytics. Nowadays, most of the data in healthcare centers are remotely monitored and are generated from different wireless sensors. The core objective of outlier detection in this domain is the recognition of the true physiologically anomalous data and the anomalies because of faulty sensors. In real healthcare monitoring scenario, various sensors are related to each other. So, while detecting outliers in wireless body sensor networks (WBSNs), correlation among different sensor nodes is of major concern. Most of the existing outlier detection techniques consider the sensors to be linearly correlated, which may not always be the case in real life applications. The traditional techniques for outlier detection are also not scalable to big data. To address the above issues, in this paper, we propose an approach for outlier detection that is scalable to big data and also handles the nonlinearly correlated attributes efficiently. The proposed approach is implemented on Hadoop map reduce framework for the rapid processing of big data. The evaluation results are validated using the simulated dataset of WBSNs taken from the Physionet library. The results are compared with various existing outlier detection approaches and demonstrated that the proposed approach is more effective in spotting the physiological outliers and sensor anomalies accurately.     

An improved fault detection crow search algorithm for wireless sensor network

In wireless sensor networks (WSNs), the collected data during monitoring environment can have some faulty data, and these faults can lead to the failure of a system. These faults may occur due to many factors such as environmental interference, low battery, and sensors aging etc. We need an efficient fault detection technique for preventing the failures of a WSN or an IoT system. To address this major issue, we have proposed a new nature-inspired approach for fault detection for WSNs called improved fault detection crow search algorithm (IFDCSA). IFDCSA is an improved version of the original crow search algorithm (CSA). The proposed algorithm first injects the faults into the datasets, and then the faults are classified using improved CSA and machine learning classifiers. The proposed work has been evaluated on the three real-world datasets, ie, Intel lab data, multihop labeled data, and SensorScope data, and predicts the faults with an average accuracy of 99.94%. The results of the proposed algorithm have been compared with the three different machine learning classifiers (random forest, k-nearest neighbors, and decision trees) and Zidi model. The proposed algorithm outperforms the other classifiers/models, thus generating higher accuracy and lower features without degrading the performance of the system. Index Terms—big data, crow search algorithm, IoT, machine learning, nature-inspired algorithm, wireless sensor network.     

改进的基于故障特征平面的双星故障识别算法

针对基于识别门限的奇偶矢量法等双星故障识别算法存在较大误警率、漏检率以及故障偏差抵消致使双星故障正确识别率较低的问题,提出了一种改进的用于双星故障识别的接收机自主完好性监测算法.在奇偶矢量法的基础上,构造故障特征平面和改进奇偶矢量,分析二者之间的几何特征与卫星故障的关系,并设计相应算法识别故障卫星.该算法不受识别门限的影响,避免了由识别门限引起的识别率较低的不足.半物理仿真结果显示：改进后的算法故障识别率达到90%以上,与直接利用奇偶矢量法相比,可以显著提高双星故障识别率.     

基于预测方法的无线体域网跨层优化研究

为改善无线体域网的能效和传输可靠性,该文针对其具有资源有限、信道质量波动频繁、所传输数据有异构性等特点,提出一种基于链路质量预测的跨层优化方案。通过对物理层、网络层和MAC层的松散耦合,自适应地选择传感器节点的传输功率,并且建立高效节能的端到端路由。仿真结果显示,该方案相对于已有的单层协议,整体提高了体域网的能量效率和传输可靠性。     

基于学习向量量化的传感器免疫网络诊断模型

为克服传感器免疫网络连接权值获取困难,提出了一种基于学习向量量化的传感器免疫网络诊断模型.该模型将学习向量量化(LVQ)的概念引入到传感器免疫网络模型中,在训练模式下,LVQ用于提取传感器正常工作下的相关性;在诊断模式下,根据LVQ获取的知识即可以确定传感器之间的检测结果,同时给出了诊断模型的性能优化算法.航空发动机传感器的仿真结果表明,所提出的方法能够准确获得网络节点之间的连接权值,保证免疫网络具有较高的检测灵敏度.     

使用自适应阈值的图像篡改检测与定位算法

为了实现对机载移动目标的快速捕获和粗跟踪瞄准,设计了粗跟踪演示系统,完成了外 场飞行实验的 初步验证。本文系统利用GPS数据完成对目标的捕获,通过对姿态数据的校正,方位误差降 到0.60°(1σ),俯 仰误差降到0.40°(1σ),有效缩小了不确定区域;系统还对跟踪算 法进行了优化改进,利用分段式函数等效 非线性调整函数,有效解决动态目标跟踪时快速调整和超调之间的矛盾。飞行实验表明, 本文的粗跟踪演示 系统的捕获时间优于10s,粗跟踪精度优于480μrad,为精跟踪子系统实现最终的目标精确跟踪瞄准提供了 有利条件,实验结果验证了该系统用于激光通信链路快速建立的可行性。     

Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance

Due to the wide range of critical applications and resource constraints, sensor node gives unexpected responses, which leads to various kind of faults in sensor node and failure in wireless sensor networks. Many research studies focus only on fault diagnosis, and comparatively limited studies have been conducted on fault diagnosis along with fault tolerance in sensor networks. This paper reports a complete study on both 2 aspects and presents a fault tolerance approach using regressional learning with fault diagnosis in wireless sensor networks. The proposed method diagnose the different types of faulty nodes such as hard permanent, soft permanent, intermittent, and transient faults with better detection accuracy. The proposed method follows a fault tolerance phase where faulty sensor node values would be predicted by using the data sensed by the fault free neighbors. The experimental evaluation of the fault tolerance module shows promising results with R² of more than 0.99. For the periodic fault such as intermittent fault, the proposed method also predict the possible occurrence time and its duration of the faulty node, so that fault tolerance can be achieved at that particular time period for better performance of the network.     

rDFD: reactive distributed fault detection in wireless sensor networks

Generally, fault detection approaches pursue high detection accuracy, but neglect energy consumption due to the high volume of messages exchanged. Therefore, in this work we propose a reactive distributed scheme for detecting faulty nodes. The scheme is able to detect transient and permanent faulty nodes accurately by exchanging fewer messages. In existing fault detection schemes, nodes exchange too many messages after every specific interval to detect suspicious node. However, in the proposed scheme comparatively much less messages are exchanged within a limited geographical area around the suspicious node only and that too when the node suspects its own readings. In the proposed scheme, each node exploits the temporal correlation in its own readings to detect any suspicious behavior. In order to confirm its status, the suspicious node communicates with its immediate neighbors who may be locally good or possible faulty with a certain level of confidence. Thus, the scheme utilizes the strength of both spatial and temporal correlation to find faulty nodes. Also, a confidence level is assigned to each correlated neighbor of suspicious node in order to enhance the detection accuracy. The ns-2 based simulation results show that our scheme performs better by reducing communication overhead and by detecting faulty nodes with high accuracy as compared to existing approaches.     

基于大脑层状皮质模型的立体图像质量评价

针对利用相机传感器模式噪声的篡改检测在待测 图像纹理复杂区域存在较高的虚警,提出了一种考 虑纹理复杂度的自适应阈值检测算法。根据Nyman-Pierson(N-P)准则,确定不同纹理复杂 度对应的相关性匹配 判定阈值,而得到相关性阈值与纹理复杂度的关系拟合函数。在不重叠分块计算待测 图像噪声残差 和其来源相机传感器模式噪声对应块相关性的基础之上,根据待测图像块不同的纹理复杂度 进行相关性匹 配,确定大致篡改位置;再用快速零均值归一化互相关(ZNCC) 算法计算两噪声图像中大致篡改区域对应点的相关性,实现精确定 位。在手机图像库上的实验表明,与现有的固定阈值方法相比,本文算法的检测率达 到了98.8%,而虚 警率仅为1.897%,有效地降低纹理复杂区域的虚警率,并实现对篡改 区域的精确定位;同 时,与传统的滑动窗口方法相比,本文算法检测效率平均提高了26倍 。     

A Supervised Learning Based Decision Support System for Multi-Sensor Healthcare Data from Wireless Body Sensor Networks

Wireless body sensor network (WBSN) is also known as wearable sensors with transmission capabilities, computation, storage and sensing. In this paper, a supervised learning based decision support system for multi sensor (MS) healthcare data from wireless body sensor networks (WBSN) is proposed. Here, data fusion ensemble scheme is developed along with medical data which is obtained from body sensor networks. Ensemble classifier is taken the fusion data as an input for heart disease prediction. Feature selection is done by the squirrel search algorithm which is used to remove the irrelevant features. From the sensor activity data, we utilized the modified deep belief network (M-DBN) for the prediction of heart diseases. This work is implemented by Python platform and the performance is carried out of both proposed and existing methods. Our proposed M-DBN technique is compared with various existing techniques such as Deep Belief Network, Artificial Neural Network and Conventional Neural Network. The performance of accuracy, recall, precision, F1 score, false positive rate, false negative and true negative are taken for both proposed and existing methods. Our proposed performance values for accuracy (95%), precision (98%), and recall (90%), F1 score (93%), false positive (72%), false negative (98%) and true negative (98%).

     

基于广义判决分析的图像拼接检测最优类色度通道设计方法

针对图像拼接检测问题,该文提出一种基于广义判决分析(GDA)的最优类色度通道设计方法。将最优类色度通道的设计建模为一个以GDA识别力为目标函数,以类色度通道参数范围为约束条件的最优化问题,通过网格搜索和梯度上升法求解最优类色度通道参数。在哥伦比亚图像拼接检测评估库中的实验结果显示,目前4种主流的图像拼接检测方法在最优类色度通道上的识别率均高于已有的颜色通道,验证了该方法的通用性和有效性。     

基于探测圈覆盖的光突发交换网络故障定位

针对光突发交换(Optical Burst Switching,OBS)网络在干扰告警信息情况下定位率低的问题,在最短长度m2圈算法的基础上,提出了相应的故障定位机制。首先,故障探测阶段收集由故障触发的告警信息,并组成相应的二进制告警相关矩阵,然后,故障定位阶段利用告警矩阵进行快速定位,找到可能的故障设备集合。     

TBSA: Threshold-Based Simulation Accuracy Method for Fast Analog DC Fault Simulation

Starting from a good solution approximation has proved to be very efficient to reduce CPU time required by DC simulation of analog circuits. In order to obtain an additional speedup in DC fault simulation, this paper proposes a new criterion to end the Newton-Raphson (NR) iterative algorithm before convergence. In the case where an initial solution approximation is used, the analysis of the NR algorithm behavior until convergence is presented and a threshold-based simulation accuracy (TBSA) method is then proposed. TBSA stops the iterations when the solution at current NR iteration is enough accurate to immediately classify the fault. According to the detection thresholds, a CPU time/accuracy tradeoff is achieved without altering the fault classification results. The proposed method has been validated on 12 MOS and BJT benchmark circuits considering DC fault simulation under process parameter variations. TBSA is compared to two existing methods which are: standard simulation until convergence method which is accurate but requires a large CPU time, and single NR iteration method which is very fast but without any control over the accuracy. All the compared methods reuse the fault-free circuit results as initial solution for each faulty circuit simulation. It is shown that TBSA requires an intermediate number of NR iterations while achieving correct fault classification, especially for parametric faults which take advantage of using a more accurate initial solution.     

Optimal barrier coverage for critical area surveillance using wireless sensor networks

Intrusion detection using barrier coverage is one of many applications existed in wireless sensor networks. The main purpose of using barrier coverage is to monitor the borders of a specific area against the intruders that are trying to penetrate this critical area by ensuring the total coverage with a low cost and extending the lifetime of the network, many solutions have been proposed in the literature in order to solve the coverage problem in wireless sensor networks, which became a vital field of research. In this paper, we present a new technique based on geometric mathematical models, in order to guarantee the total coverage of our deployed barriers with a minimum possible number of sensors. The idea is to calculate the number of sensors adequate to cover a barrier before deployment. We then formulate the problem to minimize the number of sensors to be deployed to properly cover a barrier; the calculation makes it possible to solve this problem in polynomial using our own heuristic. Additionally, we propose a new mechanism for ensuring a fault‐tolerant network by detecting the faulty sensors and select other suited sensors to close the existing gaps inside the barriers and detecting the sensors whose energy is low before the failure. The obtained simulation results prove the effectiveness of the proposed algorithms.     

Energy harvesting,cooperative spectrum sensing,and data transmission improvement in energy‐efficient multichannel cognitive sensor networks

In this paper, we investigate the energy harvesting capability in a multichannel wireless cognitive sensor networks for energy‐efficient cooperative spectrum sensing and data transmission. Spectrum sensors can cooperatively scan the spectrum for available channels, whereas data sensors transmit data to the fusion center (FC) over those channels. We select the sensing, data transmission, and harvesting sensors by setting the sensing time, data transmission time, and also harvesting time to maximize the network data transmission rate and improve the total energy consumption in the multichannel network under global probability of false alarm and global probability of detection constraints. We formulate our optimization problem and employ the convex optimization method to obtain the optimal times and nodes for spectrum sensing, data transmission, and harvesting energy in each subchannel for multiband cognitive sensor networks. Simulation results show that in our proposed algorithm, the network data transmission rate is improved while more energy is saved compared with the baseline methods with equal sensing time in all subchannels.     

A novel nest-based scheduling method for mobile wireless body area networks

Wireless Body Area Networks (WBANs) comprise various sensors to monitor and collect various vital signals, such as blood pressure, pulse, heartbeat, body temperature, and blood sugar. A dense and mobile WBAN often suffers from interference, which causes serious problems, such as wasting energy and degrading throughput. In reality, not all of the sensors in WBAN need to be active at the same time. Therefore, they can be divided into different groups so that each group works in turn to avoid interference. In this paper, a Nest-Based WBAN Scheduling (NBWS) algorithm is proposed to cluster sensors of the same types in a single or multiple WBANs into different groups to avoid interference. Particularly, we borrow the graph coloring theory to schedule all groups to work using a Time Division for Multimodal Sensor (TDMS) group scheduling model. Both theoretical analysis and experimental results demonstrate that the proposed NBWS algorithm performs better in terms of frequency of collisions, transmission delay, system throughput, and energy consumption compared to the counterpart methods.     

Decision Fusion of Ground-Penetrating Radar and Metal Detector Algorithms—A Robust Approach

Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the receiver operating characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper, we present multisensor decision-fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers an expedient, attractive, and much simpler alternative to the design of an algorithm that fuses multiple sensors at the data level, especially in cases of limited training data where it is difficult to make accurate estimates of multidimensional probability density functions. The goal of our multisensor decision-fusion approach is to exploit the complimentary strengths of existing multisensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multisensor decision fusion is based on the optimal signal detection theory using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors: a ground-penetrating radar and a metal detector. A new robust algorithm for decision fusion that addresses the problem in which the statistics of the training data are not likely to exactly match the statistics of the test data is presented. ROCs are presented and compared for field data