No Soldiers Left Behind: An IoT-Based Low-Power Military Mobile Health System Design Article Swipe
James Jin Kang
,
Wencheng Yang
,
Gordana Dermody
,
Mohammadreza Ghasemian
,
Sasan Adibi
,
Paul Haskell‐Dowland
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3035812
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.3035812
© 2013 IEEE. There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-Time actuation of IoT equipment, and activation of emergency alarms through the inference of a user's situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.3035812
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/09247977.pdf
- OA Status
- gold
- Cited By
- 51
- References
- 70
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3094904458
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3094904458Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2020.3035812Digital Object Identifier
- Title
-
No Soldiers Left Behind: An IoT-Based Low-Power Military Mobile Health System DesignWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
James Jin Kang, Wencheng Yang, Gordana Dermody, Mohammadreza Ghasemian, Sasan Adibi, Paul Haskell‐DowlandList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2020.3035812Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8948470/09247977.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/09247977.pdfDirect OA link when available
- Concepts
-
Computer science, LPWAN, Wearable computer, Wireless sensor network, Mobile device, Wireless, Computer network, Real-time computing, Embedded system, Wide area network, Telecommunications, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
51Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 16, 2023: 17, 2022: 7, 2021: 5Per-year citation counts (last 5 years)
- References (count)
-
70Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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