ArticleSensors (Basel, Switzerland)2015
Sensor anomaly detection in wireless sensor networks for healthcare.
Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Integrated Edge Deployable Fault Diagnostic Algorithm for the Internet of Things (IoT): A Methane Sensing Application.Sensors (Basel, Switzerland) · 2023Article
- Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques.Sensors (Basel, Switzerland) · 2022Article
- A Correlation-Based Anomaly Detection Model for Wireless Body Area Networks Using Convolutional Long Short-Term Memory Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Constructing LDPC Codes with Any Desired Girth.Sensors (Basel, Switzerland) · 2021Article
- A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches.PeerJ. Computer science · 2021Article
- Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network.Sensors (Basel, Switzerland) · 2020Article
- Multi-Layer IoT Security Framework for Ambient Intelligence Environments.Sensors (Basel, Switzerland) · 2019Article
- An Intelligent Failure Detection on a Wireless Sensor Network for Indoor Climate Conditions.Sensors (Basel, Switzerland) · 2019Article
- A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors.Sensors (Basel, Switzerland) · 2018Article
- Smart Secure Homes: A Survey of Smart Home Technologies that Sense, Assess, and Respond to Security Threats.Journal of reliable intelligent environments · 2017Article
- A Low-Power and Portable Biomedical Device for Respiratory Monitoring with a Stable Power Source.Sensors (Basel, Switzerland) · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.