A GEP-Based Anomaly Detection Scheme in Wireless Sensor Networks

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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Anomaly Detection using Machine Learning Techniques in Wireless Sensor Networks

Journal of Physics Conference Series ◽

10.1088/1742-6596/1743/1/012021 ◽

2021 ◽

Vol 1743 ◽

pp. 012021

Author(s):

Samir Ifzarne ◽

Hiba Tabbaa ◽

Imad Hafidi ◽

Nidal Lamghari

Keyword(s):

Machine Learning ◽

Wireless Sensor Networks ◽

Sensor Networks ◽

Anomaly Detection ◽

Machine Learning Techniques ◽

Wireless Sensor ◽

Learning Techniques

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An Efficient Distributed Anomaly Detection Model for Wireless Sensor Networks

AASRI Procedia ◽

10.1016/j.aasri.2013.10.052 ◽

2013 ◽

Vol 5 ◽

pp. 9-14 ◽

Cited By ~ 7

Author(s):

Murad A. Rassam ◽

Anazida Zainal ◽

Mohd Aizaini Maarof

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Anomaly Detection ◽

Wireless Sensor ◽

Detection Model

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

International Journal of E-Health and Medical Communications ◽

10.4018/ijehmc.2014010102 ◽

2014 ◽

Vol 5 (1) ◽

pp. 20-45 ◽

Cited By ~ 32

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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