Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare
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 interventio...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/15/4/8764 |
| _version_ | 1798006098494488576 |
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| author | Shah Ahsanul Haque Mustafizur Rahman Syed Mahfuzul Aziz |
| author_facet | Shah Ahsanul Haque Mustafizur Rahman Syed Mahfuzul Aziz |
| author_sort | Shah Ahsanul Haque |
| collection | DOAJ |
| description | 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). |
| first_indexed | 2024-04-11T12:49:36Z |
| format | Article |
| id | doaj.art-4475f5057b6d4592ac374a0c86cb2072 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:49:36Z |
| publishDate | 2015-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4475f5057b6d4592ac374a0c86cb20722022-12-22T04:23:14ZengMDPI AGSensors1424-82202015-04-011548764878610.3390/s150408764s150408764Sensor Anomaly Detection in Wireless Sensor Networks for HealthcareShah Ahsanul Haque0Mustafizur Rahman1Syed Mahfuzul Aziz2School of Engineering, University of South Australia, Mawson Lakes, SA 5095, AustraliaDepartment of Defence, Defence Science and Technology Organization, SA 5111, AustraliaSchool of Engineering, University of South Australia, Mawson Lakes, SA 5095, AustraliaWireless 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).http://www.mdpi.com/1424-8220/15/4/8764wireless sensor networkshealthcaremedical sensorssensor faultsensor anomaly detectionprediction |
| spellingShingle | Shah Ahsanul Haque Mustafizur Rahman Syed Mahfuzul Aziz Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare Sensors wireless sensor networks healthcare medical sensors sensor fault sensor anomaly detection prediction |
| title | Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare |
| title_full | Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare |
| title_fullStr | Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare |
| title_full_unstemmed | Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare |
| title_short | Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare |
| title_sort | sensor anomaly detection in wireless sensor networks for healthcare |
| topic | wireless sensor networks healthcare medical sensors sensor fault sensor anomaly detection prediction |
| url | http://www.mdpi.com/1424-8220/15/4/8764 |
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