RT Journal Article
T1 A TETRA-Based System for Remote Health Monitoring of First Responders: 												Peak AoI Assessment in Direct and Trunked Mode
A1 Farag, Hossam
A1 Vujic, Aleksandar
A1 Kostic, Milos
A1 Bijelic, Goran
A1 Stefanovic, Cedomir
AB In this article, we study peak age of information (PAoI) performance of a novel Internet of Things (IoT) solution for remote health monitoring of first responders over terrestrial trunked radio (TETRA) links. The solution features a set of sensors embedded in a smart garment that periodically records and sends physiological parameters of first responders to a remote agent. The received data are analyzed by the remote agent, which feeds back notifications and warnings to the first responders in the form of electrotactile stimuli. The communication in the system is performed over the TETRA short data service (SDS), which is the default option for the development of third-party applications and which has rather limited capabilities. The choice of PAoI as the parameter of interest is motivated by its suitability to measure data freshness in IoT applications with periodic monitoring. We derive closed-form expressions of PAoI for different packet-management schemes allowed by the TETRA standard and verify the analytical results through extensive simulations under varying message generation rates. Our results provide important insights on the expected PAoI performance, which can be used for the system design guidelines. To the best of our knowledge, this is the first work that analyzes AoI performance of the TETRA networks.
SN 1530-437X
YR 2023
FD 2023-05-15
LK https://hdl.handle.net/11556/3711
UL https://hdl.handle.net/11556/3711
LA eng
NO Farag , H , Vujic , A , Kostic , M , Bijelic , G & Stefanovic , C 2023 , ' A TETRA-Based System for Remote Health Monitoring of First Responders : Peak AoI Assessment in Direct and Trunked Mode ' , IEEE Sensors Journal , vol. 23 , no. 10 , pp. 11034-11045 . https://doi.org/10.1109/JSEN.2023.3263292
NO Publisher Copyright: © 2001-2012 IEEE.
NO This work was supported by the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement 883315.
DS TECNALIA Publications
RD 26 jul 2024