Outlier Detection Scheme to Handle Wireless Sensor Data

2015 ◽  
Vol 004 (001) ◽  
pp. 28-33
Author(s):  
S. Sumathy ◽  
◽  
M.Sundara Rajan ◽  
Keyword(s):  
Outlier Detection ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Detection Scheme

scholarly journals Outlier-Detection-Based Indoor Localization System for Wireless Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Yu-Chi Chen ◽  
Jyh-Ching Juang
Keyword(s):  
Outlier Detection ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Signal Strength ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Localization Problem ◽  
Detection Scheme ◽  
Localization System ◽  
Detection Techniques

The paper exploits the outlier detection techniques for wireless-sensor-network- (WSN-) based localization problem and proposes an outlier detection scheme to cope with noisy sensor data. The cheap and widely available measurement technique—received signal strength (RSS)—is usually taken into account in the indoor localization system, but the RSS measurements are known to be sensitive to the change of the environment. The paper develops an outlier detection scheme to deal with abnormal RSS data so as to obtain more reliable measurements for localization. The effectiveness of the proposed approach is verified experimentally in an indoor environment.

scholarly journals Outlier Detection Using Improved Support Vector Data Description in Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4712
Author(s):  
Pei Shi ◽  
Guanghui Li ◽  
Yongming Yuan ◽  
Liang Kuang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Relative Density ◽  
Outlier Detection ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description

Wireless sensor networks (WSNs) are susceptible to faults in sensor data. Outlier detection is crucial for ensuring the quality of data analysis in WSNs. This paper proposes a novel improved support vector data description method (ID-SVDD) to effectively detect outliers of sensor data. ID-SVDD utilizes the density distribution of data to compensate SVDD. The Parzen-window algorithm is applied to calculate the relative density for each data point in a data set. Meanwhile, we use Mahalanobis distance (MD) to improve the Gaussian function in Parzen-window density estimation. Through combining new relative density weight with SVDD, this approach can efficiently map the data points from sparse space to high-density space. In order to assess the outlier detection performance, the ID-SVDD algorithm was implemented on several datasets. The experimental results demonstrated that ID-SVDD achieved high performance, and could be applied in real water quality monitoring.

scholarly journals Outlier Detection in Wireless Sensor Networks Using Model Selection-Based Support Vector Data Descriptions

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4328 ◽  
Author(s):  
Zhan Huan ◽  
Chang Wei ◽  
Guang-Hui Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Model Selection ◽  
Outlier Detection ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Quality Data ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data

Wireless sensor networks (WSNs) are often deployed in harsh and unattended environments, which may cause the generation of abnormal or low quality data. The inaccurate and unreliable sensor data may increase generation of false alarms and erroneous decisions, so it’s very important to detect outliers in sensor data efficiently and accurately to ensure sound scientific decision-making. In this paper, an outlier detection algorithm (TSVDD) using model selection-based support vector data description (SVDD) is proposed. Firstly, the Toeplitz matrix random feature mapping is used to reduce the time and space complexity of outlier detection. Secondly, a novel model selection strategy is realized to keep the algorithm stable under the low feature dimensions, this strategy can select a relatively optimal decision model and avoid both under-fitting and overfitting phenomena. The simulation results on SensorScope and IBRL datasets demonstrate that, TSVDD achieves higher accuracy and lower time complexity for outlier detection in WSNs compared with existing methods.

scholarly journals Outlier Detection in Wireless Sensor Networks Data by Entropy Based K-NN Predictor

2019 ◽  
Vol 8 (12) ◽  
pp. 5483-5489
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Outlier Detection ◽  
Window Size ◽  
Environmental Parameters ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Training Dataset ◽  
Time Motion

Anomaly (outlier) detection is plays very significant role in ESN based monitoring application using on large data used for biomedical and defence. Wireless Sensor network monitor environmental parameters (temperature, humidity, pressure, vibration etc). Group of sensor nodes forms a (WSN) and observations collected from these sensor produces low data quality and reliability due to the limited energy, memory, computation capability and bandwidth. The dynamic environment of network and roughness of the working condition are also responsible to generate inaccuracy in measurements. In this paper, an approach for outliers detection based entropy value of received sensor voltages is applied using KNN prediction model .The algorithm development and analysis involves a real time database generated on 14 sets of MICA2 wireless sensor kit with anomaly inserted by real time motion based intrusion in the lab by volunteers from Intel Berkeley lab. On each sensor data pair segmentation is applied by fixed window size in order get large outliers’ measurements training dataset. The analysis demonstrates the measurement accuracy in detection of number of outliers that its 86%. Moreover, the algorithm also provides an analysis in terms of impact of variation in distance types and number of nearest neighbours in the KNN prediction model. This work is helpful in the application in the situations where high amount of noise or distortions are present. The outlier part from distorted data can be figured out and recollected to enhance application accuracy.

scholarly journals DODS: A Distributed Outlier Detection Scheme for Wireless Sensor Networks

2019 ◽  
Vol 161 ◽  
pp. 93-101 ◽  
Author(s):  
Chafiq Titouna ◽  
Farid Naït-Abdesselam ◽  
Ashfaq Khokhar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Outlier Detection ◽  
Wireless Sensor ◽  
Detection Scheme

Why General Outlier Detection Techniques Do Not Suffice for Wireless Sensor Networks

2010 ◽  
pp. 136-158 ◽  
Author(s):  
Yang Zhang ◽  
Nirvana Meratnia ◽  
Paul Havinga
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Outlier Detection ◽  
Environmental Effects ◽  
Measurement Errors ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Detection Techniques ◽  
General Data ◽  
Faulty Sensors

Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at identifying such readings, which represent either measurement errors or interesting events. Due to numerous shortcomings, commonly used outlier detection techniques for general data seem not to be directly applicable to outlier detection in wireless sensor networks. In this chapter, the authors report on the current stateof- the-art on outlier detection techniques for general data, provide a comprehensive technique-based taxonomy for these techniques, and highlight their characteristics in a comparative view. Furthermore, the authors address challenges of outlier detection in wireless sensor networks, provide a guideline on requirements that suitable outlier detection techniques for wireless sensor networks should meet, and will explain why general outlier detection techniques do not suffice.

scholarly journals An edge-cloud collaboration architecture for pattern anomaly detection of time series in wireless sensor networks

2021 ◽  
Author(s):  
Cong Gao ◽  
Ping Yang ◽  
Yanping Chen ◽  
Zhongmin Wang ◽  
Yue Wang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Estimation Method ◽  
Feature Representation ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Data Sets ◽  
Edge Node

AbstractWith large deployment of wireless sensor networks, anomaly detection for sensor data is becoming increasingly important in various fields. As a vital data form of sensor data, time series has three main types of anomaly: point anomaly, pattern anomaly, and sequence anomaly. In production environments, the analysis of pattern anomaly is the most rewarding one. However, the traditional processing model cloud computing is crippled in front of large amount of widely distributed data. This paper presents an edge-cloud collaboration architecture for pattern anomaly detection of time series. A task migration algorithm is developed to alleviate the problem of backlogged detection tasks at edge node. Besides, the detection tasks related to long-term correlation and short-term correlation in time series are allocated to cloud and edge node, respectively. A multi-dimensional feature representation scheme is devised to conduct efficient dimension reduction. Two key components of the feature representation trend identification and feature point extraction are elaborated. Based on the result of feature representation, pattern anomaly detection is performed with an improved kernel density estimation method. Finally, extensive experiments are conducted with synthetic data sets and real-world data sets.

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