Wireless Body Area Network Sensor Faults and Anomalous Data Detection and Classification using Machine Learning

Nagdeo, Sumit Kumar; Mahapatro, Judhistir

doi:10.1109/ibssc47189.2019.8973004

Cited by 13 publications

(12 citation statements)

References 13 publications

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“…Figure 2 presents the performance of the SFD algorithm for Dataset 1 in terms of TPR and TNR. One can clearly see that the anomaly detection rate of the proposed scheme is 100%, which matches the results of existing schemes [12–17]. Figure 3 presents the performance of the SFD algorithm for Dataset 1 in terms of FPR and FNR.…”

Section: Resultssupporting

confidence: 73%

“…The proposed SFD algorithm significantly reduces FPR by 91.22%, 84.20%, 37.80%, 26.17%, 28.02%, 32.33%, 29.78%, and 45.80% compared to the existing schemes from Ref. [11–18], respectively. The proposed scheme for Dataset 1 achieves high accuracy, precision, and F 1 score based on its high detection rate and low FPR.…”

Section: Resultsmentioning

confidence: 95%

“…The anomaly detection rates of the existing schemes from Ref. [11–18] are 64%, 97%, 97%, 99%, 98.19%, 98.03%, 99%, and 95.8%, respectively. The proposed SFD algorithm significantly reduces the FPR by 85.88%, 69.91%, 24.21%, 17.32%, 18.78%, 21%, 20.18%, and 31.96%, respectively, when compared to these schemes.…”

Section: Resultsmentioning

confidence: 99%

“…However, this method incurs high computational complexity, a large number of required training data samples for modeling, and a high false alarm rate. Nagdeo and Mahapatro [16] proposed an anomaly detection method for WBANs using artificial neural networks (ANNs) and linear regression. This method has high computational complexity based on the use of a back‐propagation ANN, as well as a low detection rate and high false alarm.…”

Section: Introductionmentioning

confidence: 99%

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A precise sensor fault detection technique using statistical techniques for wireless body area networks

Nair

Ramasamy

2020

ETRI Journal

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One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.

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Section: Resultssupporting

confidence: 73%

Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A precise sensor fault detection technique using statistical techniques for wireless body area networks

Nair

Ramasamy

2020

ETRI Journal

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“…Nevertheless, with an increase in the number of colluding sensors, the detection efficiency degrades to a point that the detection fails when most of the sensors are colluding. Furthermore, researchers (Nagdeo & Mahapatro 2019) have implemented an ML model to separate anomalous data from legitimate sensed data. This research used a combination of ANN with Ensemble LinReg as a detection technique for abnormalities in WBAN sensors.…”

Section: Computer Sciencementioning

confidence: 99%

Peer Review #1 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.1)"

2021

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Background . The Internet of Medical Things (IoMTs) is gradually replacing the traditional healthcare system. However, little attention has been paid to their security requirements in the development of the IoMT devices and systems. One of the main reasons can be the difficulty of tuning conventional security solutions to the IoMT system. Machine Learning (ML) has been successfully employed in the attack detection and mitigation process. Advanced ML technique can also be a promising approach to address the existing and anticipated IoMT security and privacy issues. However, because of the existing challenges of IoMT system, it is imperative to know how these techniques can be effectively utilized to meet the security and privacy requirements without affecting the IoMT systems quality, services, and device's lifespan.Methodology. This paper is devoted to perform a Systematic Literature Review (SLR) on the security and privacy issues of IoMT and their solutions by ML techniques. The recent research papers disseminated between 2010 to 2020 are selected from multiple databases and a standardized SLR method is conducted. A total of 153 papers were reviewed and a critical analysis were conducted on the selected papers. Furthermore, this review study attempts to highlight the limitation of the current methods and aims to find possible solutions to them. Thus, a detailed analysis was carried out on the selected papers through focusing on their methods, advantages, limitations, the utilized tools, and data. Results.It was observed that ML techniques have been significantly deployed for device and network layer security. Most of the current studies improved traditional metrics while ignored performance complexity metrics in their evaluations. Their studies environments and utilized data barely represent IoMT system. Therefore, conventional ML techniques may fail if metrics such as resource complexity and power usage are not considered.

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An Intelligent Vehicular Communication-Based Framework to Provide Seamless Connectivity in WBAN

Karmakar¹,

Saif²,

Biswas³

et al. 2022

Advances in Communication, Devices and Networking

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Wireless Body Area Network Sensor Faults and Anomalous Data Detection and Classification using Machine Learning

Cited by 13 publications

References 13 publications

A precise sensor fault detection technique using statistical techniques for wireless body area networks

A precise sensor fault detection technique using statistical techniques for wireless body area networks

Peer Review #1 of "A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches (v0.1)"

An Intelligent Vehicular Communication-Based Framework to Provide Seamless Connectivity in WBAN

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