IoT Healthcare Analytics: The Importance of Anomaly Detection

Ukil, Arijit; Bandyoapdhyay, Soma; Puri, Chetanya; Pal, Arpan

doi:10.1109/aina.2016.158

Cited by 164 publications

(61 citation statements)

References 25 publications

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“…In addition to the studies mentioned above, other reported works on IoT based healthcare include: emergency medical service [36], smart rehabilitation system [37], do-ityourself solution focusing on patient oriented infrastructure development [38], smart hospital system [39], anomaly detection [40], body sensor network based healthcare system [41], cardiac arrhythmia management system [42], and selfaware early warning system [43]. Laplante and Laplante discussed about their view on negetive effects of IoT in healthcare and showcased as an example the dissociation between patient and caregiver resulting in loss of care [44].…”

Section: State-of-the-art Of Ioht Frameworkmentioning

confidence: 99%

Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things

Asif-Ur-Rahman

Afsana

Mahmud

et al. 2019

IEEE Internet Things J.

180

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Rapid developments in the fields of information and communication technology and microelectronics allowed seamless interconnection among various devices letting them to communicate with each other. This technological integration opened up new possibilities in many disciplines including healthcare and well-being. With the aim of reducing healthcare costs and providing improved and reliable services, several healthcare frameworks based on Internet of Healthcare Things (IoHT) have been developed. However, due to the critical and heterogeneous nature of healthcare data, maintaining high quality of service (QoS) -in terms of faster responsiveness and data-specific complex analytics -has always been the main challenge in designing such systems. Addressing these issues, this paper proposes a five-layered heterogeneous mist, fog, and cloud based IoHT framework capable of efficiently handling and routing (near-)real-time as well as offline/batch mode data. Also, by employing software defined networking and link adaptation based load balancing, the framework ensures optimal resource allocation and efficient resource utilization. The results, obtained by simulating the framework, indicate that the designed network via its various components can achieve high QoS, with reduced end-to-end latency and packet drop rate, which is essential for developing next generation e-healthcare systems.

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Section: State-of-the-art Of Ioht Frameworkmentioning

confidence: 99%

Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things

Asif-Ur-Rahman

Afsana

Mahmud

et al. 2019

IEEE Internet Things J.

180

View full text Add to dashboard Cite

show abstract

“…Internet of ings (IoT), which enables a variety of things to connect each other via the Internet or wireless communication, by employing data-collecting devices such as sensors and radio frequency identification (RFID), has a wide range of applications [1,2]. As an indispensable part of IoT, wireless sensor networks (WSNs) can collect data from specific objects and share them with human beings; thus, WSN is widely applied in many application scenarios, like healthcare service [3,4], environment monitoring [5], and habitats [6]. Wireless medical sensor network (WMSN) is a popular application of WSN for healthcare systems, in which wearable sensors gather the patient's physiological information such as blood pressure, body temperature, and heart rate and send them to the medical professionals for diagnosis or further treatment [7].…”

Section: Introductionmentioning

confidence: 99%

Cryptanalysis and Security Improvement of Two Authentication Schemes for Healthcare Systems Using Wireless Medical Sensor Networks

Lin

2020

Security and Communication Networks

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Wireless medical sensor networks (WMSNs) play an important role in collecting healthcare data of the remote patient and transmitting them to the medical professional for proper diagnosis via wireless channel. To protect the patient's healthcare data which is private-related and sensitive, some authentication schemes for healthcare systems using WMSN have been proposed to ensure the secure communication between the medical sensors and the medical professional. Since cryptanalyzing the security defects of authenticated protocols is crucial to put forward solutions and propose truly robust protocols, we scrutinize two state-of-the-art authentication protocols using WMSN for healthcare systems. Firstly, we examine Ali et al.’s enhanced three-factor based authentication protocol and show that although it provides a formal proof and a security verification, it still fails to resist offline dictionary guessing attack, desynchronization attack, and privileged insider attack and contains a serious flaw in the password change phase. Secondly, we investigate Shuai et al.’s lightweight and three-factor based authentication protocol and point out that it cannot achieve high security level as they claimed; it is actually subject to offline dictionary guessing attack and privileged insider attack, and it also has a design flaw in the password change phase. In addition, we suggest several countermeasures to thwart these security weaknesses in these two schemes for WMSN and the similar kinds.

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“…Anomalous data pose a major challenge for health care providers, whereas an erroneous decision can affect the user's life. Therefore, ensuring data accuracy by detecting data anomalies and distinguishing between faults and medical events (e.g., clinical deterioration, heart attack, and respiration failure) is crucial for better decision‐making and represents a fundamental task in mobile health care systems (Mohktar et al, ; Ukil, Bandyopadhyay, Puri, & Pal, ).…”

Section: Introductionmentioning

confidence: 99%

AUDIT: AnomaloUs data Detection and Isolation approach for mobile healThcare systems

2019

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Mobile health care systems highly depend on collected physiological data through medical sensors to provide high‐quality care services. However, inaccurate physiological data from sensors pose a major challenge for health care providers when making decisions, whereas an erroneous decision can affect the user's life. We propose, in this paper, an anomalous data detection and isolation approach for mobile health care systems. Our approach, called AUDIT, detects inaccurate measurements in real time and distinguishes between faults or errors and health events. To do so, we propose reduced time and space complexities algorithms based on dimension reduction within the context of resource constraints. Furthermore, a decision algorithm is proposed while exploring the spatio‐temporal correlation between physiological attributes. First, we describe our approach. Then, we give its implementation details. Finally, to demonstrate the effectiveness of our approach, we show different experiments related to its detection performances and its time and space complexities.

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IoT Healthcare Analytics: The Importance of Anomaly Detection

Cited by 164 publications

References 25 publications

Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things

Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things

Cryptanalysis and Security Improvement of Two Authentication Schemes for Healthcare Systems Using Wireless Medical Sensor Networks

AUDIT: AnomaloUs data Detection and Isolation approach for mobile healThcare systems

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