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

Abstract

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.

Real-time military person detection and classification system using deep metric learning with electrostatic loss

“…In recent years, digital military applications have increasingly widespread, both in communication and detection [2]- [4]. Researchers have solved the problem of military object detection in various ways.…”

Protecting Source Location Privacy in IoT-Enabled Wireless Sensor Networks: The Case of Multiple Assets

“…M ilitary strategies are trying to hone their battlefield tactics by adopting Internet of Things (IoT) enabled Wireless Sensor Network (WSNs) [1]- [3]. The deployment of sensors, unmanned aerial vehicles, tiny robots are envisioned to transform the military personnel on the battlefield [4].…”

“…The deployment of sensors, unmanned aerial vehicles, tiny robots are envisioned to transform the military personnel on the battlefield [4]. Since this is the era of the digital age, the US Army Research Laboratory (ARL) is foreseeing the usage of these smart devices by their soldiers so that they all are connected to the militarys digital communication networks [5] and also know their health status [3], [6]. The aim is to develop a connected network to monitor the soldiers.…”

Intrabody Communication Methods – A Short Overview

“…The IEEE 802.15.4. standard for low power Zigbee protocol indicates the output power of 0 dBm (1 mW) for transmission at the maximum rate of 250 kb/s which can use up a normal lithium-ion battery in a few hours [4]. The research on military grade equipment that would provide a very low energy consumption for devices that are a part of wireless body area network (WBAN) has stated that the highest battery usage came from RF communication and that significant savings could be made by performing data fusion on the sensor nodes themselves and by reducing the transmission rate [5]. Furthermore, since standard technologies operate at higher frequencies, the range that those devices have is usually much larger than necessary, which wastes energy and can become a security risk for outside intruder attacks such as eavesdropping on the highly private information and even extracting them while impersonating a legitimate WBAN client [4].…”