Toward fast and accurate emergency cases detection in BSNs

Boudargham, Nadine; Sibai, Rayane El; Abdo, Jacques Bou; Demerjian, Jacques; Guyeux, Christophe; Makhoul, Abdallah

doi:10.1049/iet-wss.2019.0134

Cited by 13 publications

(13 citation statements)

References 59 publications

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“…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, their method fails to use a sliding window approach, resulting in poor effectiveness for detecting anomalies in dynamic systems, such as healthcare systems. Boudargham and others [18] presented an anomaly detection technique using the modified cumulative sum (MCUSUM) method for WBANs. The major drawback of this method is a high false alarm rate caused by a lack of consideration for weighted correlations among various sensors.…”

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: 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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“…Early warnings on the detected attacks and their sources must be triggered, which requires highly scalable IDSs. To deal with these issues, and to help the IDS to process all the information gathered during the data fusion from the routers, switches, firewalls, and so on, data summarization of network traffic may be applied 167,168 . Thus, intrusions will be detected based on the aggregated data called “Summary,” as shown in Figure 11.…”

Section: Challenges and Open Issuesmentioning

confidence: 99%

Anomaly‐based intrusion detection systems: The requirements, methods, measurements, and datasets

Hajj

Sibai

Abdo

et al. 2021

Trans Emerging Tel Tech

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With the Internet's unprecedented growth and nations' reliance on computer networks, new cyber‐attacks are created every day as means for achieving financial gain, imposing political agendas, and developing cyberwarfare arsenals. Network security is thus acquiring increasing attention among researchers, practitioners, network architects, policy makers, and others. To defend organizations' networks from existing, foreseen, and future threats, intrusion detection systems (IDSs) are becoming a must. Existing surveys on anomaly‐based IDS (AIDS) focus on specific components such as detection mechanisms and lack many others. In contrast to existing surveys, this article covers the full scope needed by researchers and practitioners alike when studying AIDS. The scope ranges from the intrusion detection techniques to attacks forms and passing through the relevant attack features, most‐used datasets, challenges, and potential solutions. This article provides an exhaustive review of IDSs and discusses their requirements and performance metrics in deep. It presents a taxonomy of IDSs based on four criteria: information source, detection strategy, detection mode, and architecture. Then, in‐depth analysis and a comparison of network intrusion detection approaches based on anomaly detection techniques are given. The article also introduces a classification of computer network attacks, along with their different forms and the relevant network traffic features to detect them, as well as a summary of the popular datasets used by the researchers to evaluate the IDSs. Finally, the article highlights several research challenges and the possible solutions to deal with them.

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“…Detecting outliers is a fundamental step towards enhancing the performance of many systems. It can be applied to a wide variety of fields, such as spotting disease outbreaks [9], healthcare [10,11], detecting soil pollution [12], detecting abnormal traffic [13], and analyzing a network's abnormal and suspicious activities [14,15].…”

Section: Introductionmentioning

confidence: 99%

A New User-Based Incentive Strategy for Improving Bike Sharing Systems’ Performance

et al. 2021

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The benefits of having a Bike Sharing System (BSS) in a city are numerous. Among other advantages, it promotes a cleaner environment with less traffic and pollution. One major problem the users of such services encounter is that of full or empty stations, causing user dissatisfaction. The objective of this work is to propose a new user-based incentive method to enhance BSS performance. The proposed method relies on a spatial outlier detection algorithm. It consists of adapting the departure and arrival stations of the users to the BSS state by stimulating the users to change their journeys in view of minimizing the number of full and empty stations. Experiments are carried out to compare our proposed method to some existing methods for enhancing the resource availability of BSSs, and they are performed on a real dataset issued from a well-known BSS called Velib. The results show that the proposed strategy improves the availability of BSS resources, even when the collaboration of users is partial.

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Toward fast and accurate emergency cases detection in BSNs

Cited by 13 publications

References 59 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

Anomaly‐based intrusion detection systems: The requirements, methods, measurements, and datasets

A New User-Based Incentive Strategy for Improving Bike Sharing Systems’ Performance

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