A Bayesian network model for data losses and faults in medical body sensor networks

Medical body sensor network (BSN) is a promising and flexible platform for person monitoring under natural physiological status. Due to limited resources, noise and unreliable links, sensor faults and data losses are common in BSNs. Most available works adopted schemes originated from traditional wi...

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Bibliographic Details
Published in:Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2018-10, Vol.143, p.166-175
Main Authors: Zhang, Haibin, Liu, Jiajia, Pang, Ai-Chun
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian methods
Body area networks
Body sensor networks
Data integrity
Data loss
Datasets
Experiments
Fault detection
Losses
Machine learning
Medical diagnosis
Missing data
Parameter estimation
Probability
Reconstruction
Redundancy
Reliability
Remote sensors
Sensors
Wireless sensor networks
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Summary:Medical body sensor network (BSN) is a promising and flexible platform for person monitoring under natural physiological status. Due to limited resources, noise and unreliable links, sensor faults and data losses are common in BSNs. Most available works adopted schemes originated from traditional wireless sensor networks (WSNs) to detect faults and reconstruct data. However, these works either focused only on fault detection or failed to achieve a satisfactory reconstruction accuracy due to the lack of information redundancy in BSNs. In light of this, a Bayesian network based data reconstruction scheme is proposed in this paper, which rebuilds data using conditional probabilities of body sensor readings to recover missing data and sensor faults, rather than the redundant information collected from a large number of sensors. Note that the limited number of sensors in BSNs significantly reduces the complexity of Bayesian learning and thus enables efficient structure and parameter estimation of Bayesian network. Experiments on extensive online data sets have been conducted and our results show that the performance of our scheme outperforms all available data reconstruction schemes.
ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2018.07.009