Target Localization Using Ensemble Support Vector Regression in Wireless Sensor Networks

2010 ◽  
Author(s):  
Woojin Kim ◽  
Jaemann Park ◽  
H. Jin Kim
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Support Vector Regression ◽  
Target Localization ◽  
Wireless Sensor ◽  
Support Vector

Target Localization Using Ensemble Support Vector Regression in Wireless Sensor Networks

2013 ◽  
Vol 43 (4) ◽  
pp. 1189-1198 ◽  
Author(s):  
Woojin Kim ◽  
Jaemann Park ◽  
Jaehyun Yoo ◽  
H. Jin Kim ◽  
Chan Gook Park
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Support Vector Regression ◽  
Target Localization ◽  
Wireless Sensor ◽  
Support Vector

scholarly journals Distributed Fault Detection for Wireless Sensor Networks Based on Support Vector Regression

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yong Cheng ◽  
Qiuyue Liu ◽  
Jun Wang ◽  
Shaohua Wan ◽  
Tariq Umer
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
False Alarm ◽  
Support Vector Regression ◽  
Fault Prediction ◽  
Wireless Sensor ◽  
Support Vector ◽  
Detection Accuracy ◽  
False Alarm Ratio

Because the existing approaches for diagnosing sensor networks lead to low precision and high complexity, a new fault detection mechanism based on support vector regression and neighbor coordination is proposed in this work. According to the redundant information about meteorological elements collected by a multisensor, a fault prediction model is built using a support vector regression algorithm, and it achieves residual sequences. Then, the node status is identified by mutual testing among reliable neighbor nodes. Simulations show that when the sensor fault probability in wireless sensor networks is 40%, the detection accuracy of the proposed algorithm is over 87%, and the false alarm ratio is below 7%. The detection accuracy is increased by up to 13%, in contrast to other algorithms. This algorithm not only reduces the communication to sensor nodes but also has a high detection accuracy and a low false alarm ratio. The proposed algorithm is suitable for fault detection in meteorological sensor networks with low node densities and high failure ratios.

scholarly journals Anomaly Detection in Medical Wireless Sensor Networks using SVM and Linear Regression Models

2014 ◽  
Vol 5 (1) ◽  
pp. 20-45 ◽  
Author(s):  
Osman Salem ◽  
Alexey Guerassimov ◽  
Ahmed Mehaoua ◽  
Anthony Marcus ◽  
Borko Furht
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomaly Detection ◽  
Patient Data ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Smart Devices ◽  
Support Vector ◽  
Processing Unit

This paper details the architecture and describes the preliminary experimentation with the proposed framework for anomaly detection in medical wireless body area networks for ubiquitous patient and healthcare monitoring. The architecture integrates novel data mining and machine learning algorithms with modern sensor fusion techniques. Knowing wireless sensor networks are prone to failures resulting from their limitations (i.e. limited energy resources and computational power), using this framework, the authors can distinguish between irregular variations in the physiological parameters of the monitored patient and faulty sensor data, to ensure reliable operations and real time global monitoring from smart devices. Sensor nodes are used to measure characteristics of the patient and the sensed data is stored on the local processing unit. Authorized users may access this patient data remotely as long as they maintain connectivity with their application enabled smart device. Anomalous or faulty measurement data resulting from damaged sensor nodes or caused by malicious external parties may lead to misdiagnosis or even death for patients. The authors' application uses a Support Vector Machine to classify abnormal instances in the incoming sensor data. If found, the authors apply a periodically rebuilt, regressive prediction model to the abnormal instance and determine if the patient is entering a critical state or if a sensor is reporting faulty readings. Using real patient data in our experiments, the results validate the robustness of our proposed framework. The authors further discuss the experimental analysis with the proposed approach which shows that it is quickly able to identify sensor anomalies and compared with several other algorithms, it maintains a higher true positive and lower false negative rate.

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