No Soldiers Left Behind: An IoT-Based Low-Power Military Mobile Health System Design

• Published in: IEEE access Vol. 8; pp. 201498 - 201515
• Main Authors: Kang, James Jin, Yang, Wencheng, Dermody, Gordana, Ghasemian, Mohammadreza, Adibi, Sasan, Haskell-Dowland, Paul
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Actuation; Ad hoc networks; Batteries; Biometrics; Body area networks; Data integration; Data reduction; Electronic devices; emergency alarm notification; Emergency equipment; Inference; Inference algorithms; low power wide area network (LPWAN); Military applications; Military communications; military mobile (health) network; Military personnel; mobile health (mHealth); Monolayers; Multilayer inference algorithm (MIA); multilayer inference system (MIS); Multilayers; Nonhomogeneous media; Personnel; Power consumption; Power management; Sensors; Size reduction; Systems design; Wide area networks; wireless body area network (WBAN); Wireless communication; Wireless networks; Wireless sensor networks
• DOI: 10.1109/ACCESS.2020.3035812
• ISSN: 2169-3536

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.