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

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

E-Health and Telemedicine ◽

10.4018/978-1-4666-8756-1.ch024 ◽

2016 ◽

pp. 466-486 ◽

Cited By ~ 6

Author(s):

Osman Salem ◽

Alexey Guerassimov ◽

Ahmed Mehaoua ◽

Anthony Marcus ◽

Borko Furht

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Anomaly Detection ◽

False Negative ◽

Patient Data ◽

Machine Learning Algorithms ◽

Sensor Nodes ◽

Sensor Data ◽

Wireless Sensor ◽

Smart Device

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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An adaptive method based on contextual anomaly detection in Internet of Things through wireless sensor networks

International Journal of Distributed Sensor Networks ◽

10.1177/1550147720920478 ◽

2020 ◽

Vol 16 (5) ◽

pp. 155014772092047

Author(s):

Xiang Yu ◽

Hui Lu ◽

Xianfei Yang ◽

Ying Chen ◽

Haifeng Song ◽

...

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Internet Of Things ◽

Anomaly Detection ◽

Adaptive Method ◽

Sensor Data ◽

Wireless Sensor ◽

Detection Model ◽

Implicit Information ◽

Changes Over Time

With the widespread propagation of Internet of Things through wireless sensor networks, massive amounts of sensor data are being generated at an unprecedented rate, resulting in very large quantities of explicit or implicit information. When analyzing such sensor data, it is of particular importance to detect accurately and efficiently not only individual anomalous behaviors but also anomalous events (i.e. patterns of behaviors). However, most previous work has focused only on detecting anomalies while generally ignoring the correlations between them. Even in approaches that take into account correlations between anomalies, most disregard the fact that the anomaly status of sensor data changes over time. In this article, we propose an unsupervised contextual anomaly detection method in Internet of Things through wireless sensor networks. This method accounts for both a dynamic anomaly status and correlations between anomalies based contextually on their spatial and temporal neighbors. We then demonstrate the effectiveness of the proposed method in an anomaly detection model. The experimental results show that this method can accurately and efficiently detect not only individual anomalies but also anomalous events.

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