Abstract
Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR).
Highlights
IntroductionWireless Sensor Networks (WSN) are networks of distributed wireless sensors with energy and processing constraints
Wireless Sensor Networks (WSNs) are used in numerous application domains
From the results presented on three medical datasets (221, 052 and 293), it is clear that the sensor anomaly detection approach introduced in this paper presents 100% detection rate for all three datasets and much lower false positive rates for all datasets compared to the other approaches
Summary
WSNs are networks of distributed wireless sensors with energy and processing constraints Their use is perceived to be limited to low data intensive applications. Recent advances in low power hardware architecture and communication protocols [1,2,3,4,5] have demonstrated the use of WSNs even in high data intensive applications, for example, visual sensing and image communication. Wireless medical sensors are small, resource constrained devices and capable of collecting various physiological parameters, such as Heart Rate (HR), Pulse, Oxygen Saturation (SpO2), Respiration and Blood Pressure (BP). These sensors are usually battery operated, attached to the subject’s body and are continuously monitored in hospital or home environments [7]. As the caregiver may not be present all the time to monitor the sensed data, it is important to ensure the accuracy and reliability of the data to raise an alarm in case of emergency
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