Abstract
With the soaring interest in the Internet of Things (IoT), some healthcare providers are facilitating remote care delivery through the use of wearable devices. These devices are employed for continuous streaming of personal medical data (e.g., vitals, medications, allergies, etc.) into healthcare information systems for the purposes of health monitoring and efficient diagnosis. However, a challenge from the perspective of the physicians is the inability to reliably determine which data belongs to who in real-time. This challenge emanates from the fact that healthcare facilities have numerous users who own multiple devices; thereby creating an N x M data source heterogeneity and complexities for the streaming process. As part of this research, we seek to streamline the process by proposing a wearable IoT data streaming architecture that offers traceability of data routes from the originating source to the health information system. To overcome the complexities of mapping and matching device data to users, we put forward an enhanced Petri Nets service model that aids with a transparent data trace route generation, tracking and the possible detection of medical data compromises. The results from several empirical evaluations conducted in a real-world wearable IoT ecosystem prove that: 1) the proposed system's choice of Petri Net is best suited for linkability, unlinkability, and transparency of the medical IoT data traceability, 2) under peak load conditions, the IoT architecture exhibits high scalability, and 3) distributed health information system threats such as denial of service, man-in-the-middle, spoofing, and masking can be effectively detected.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 692-707 |
| Number of pages | 16 |
| Journal | Pervasive and Mobile Computing |
| Volume | 40 |
| DOIs | |
| State | Published - Sep 1 2017 |
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All Science Journal Classification (ASJC) codes
- Computer Science (miscellaneous)
- Software
- Information Systems
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications
- Applied Mathematics
Cite this
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Wearable IoT data stream traceability in a distributed health information system. / Lomotey, Richard Kwadzo; Pry, Joseph; Sriramoju, Sumanth.
In: Pervasive and Mobile Computing, Vol. 40, 01.09.2017, p. 692-707.Research output: Contribution to journal › Article
TY - JOUR
T1 - Wearable IoT data stream traceability in a distributed health information system
AU - Lomotey, Richard Kwadzo
AU - Pry, Joseph
AU - Sriramoju, Sumanth
PY - 2017/9/1
Y1 - 2017/9/1
N2 - With the soaring interest in the Internet of Things (IoT), some healthcare providers are facilitating remote care delivery through the use of wearable devices. These devices are employed for continuous streaming of personal medical data (e.g., vitals, medications, allergies, etc.) into healthcare information systems for the purposes of health monitoring and efficient diagnosis. However, a challenge from the perspective of the physicians is the inability to reliably determine which data belongs to who in real-time. This challenge emanates from the fact that healthcare facilities have numerous users who own multiple devices; thereby creating an N x M data source heterogeneity and complexities for the streaming process. As part of this research, we seek to streamline the process by proposing a wearable IoT data streaming architecture that offers traceability of data routes from the originating source to the health information system. To overcome the complexities of mapping and matching device data to users, we put forward an enhanced Petri Nets service model that aids with a transparent data trace route generation, tracking and the possible detection of medical data compromises. The results from several empirical evaluations conducted in a real-world wearable IoT ecosystem prove that: 1) the proposed system's choice of Petri Net is best suited for linkability, unlinkability, and transparency of the medical IoT data traceability, 2) under peak load conditions, the IoT architecture exhibits high scalability, and 3) distributed health information system threats such as denial of service, man-in-the-middle, spoofing, and masking can be effectively detected.
AB - With the soaring interest in the Internet of Things (IoT), some healthcare providers are facilitating remote care delivery through the use of wearable devices. These devices are employed for continuous streaming of personal medical data (e.g., vitals, medications, allergies, etc.) into healthcare information systems for the purposes of health monitoring and efficient diagnosis. However, a challenge from the perspective of the physicians is the inability to reliably determine which data belongs to who in real-time. This challenge emanates from the fact that healthcare facilities have numerous users who own multiple devices; thereby creating an N x M data source heterogeneity and complexities for the streaming process. As part of this research, we seek to streamline the process by proposing a wearable IoT data streaming architecture that offers traceability of data routes from the originating source to the health information system. To overcome the complexities of mapping and matching device data to users, we put forward an enhanced Petri Nets service model that aids with a transparent data trace route generation, tracking and the possible detection of medical data compromises. The results from several empirical evaluations conducted in a real-world wearable IoT ecosystem prove that: 1) the proposed system's choice of Petri Net is best suited for linkability, unlinkability, and transparency of the medical IoT data traceability, 2) under peak load conditions, the IoT architecture exhibits high scalability, and 3) distributed health information system threats such as denial of service, man-in-the-middle, spoofing, and masking can be effectively detected.
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UR - http://www.scopus.com/inward/citedby.url?scp=85025110244&partnerID=8YFLogxK
U2 - 10.1016/j.pmcj.2017.06.020
DO - 10.1016/j.pmcj.2017.06.020
M3 - Article
VL - 40
SP - 692
EP - 707
JO - Pervasive and Mobile Computing
JF - Pervasive and Mobile Computing
SN - 1574-1192
ER -