Document Type

Journal Article

Publication Title

IEEE Access

Volume

8

First Page

201498

Last Page

201515

Publisher

IEEE

School

School of Science / School of Nursing and Midwifery

RAS ID

32401

Funders

Edith Cowan University - Open Access Support Scheme 2020

Comments

Kang, J. J., Yang, W., Dermody, G., Ghasemian, M., Adibi, S., & Haskell-Dowland, P. (2020). No soldiers left behind: An IoT-based low-power military mobile health system design. IEEE Access, 8, 201498-201515. https://doi.org/10.1109/ACCESS.2020.3035812

Abstract

© 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.

DOI

10.1109/ACCESS.2020.3035812

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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