In the next few years, fundamental technological transitions are expected both for wireless communications, soon resulting in the 5G era, and for the kind of pervasiveness that will be achieved thanks to the Internet of Things. The implementation of such new communication paradigms is expected to significantly revolutionize people’s lives, industry, commerce, and many daily activities. Healthcare applications are considered to be one of the most impacted industries. Sadly, in relation to the COVID-19 pandemic currently afflicting our society, health remote monitoring has become a fundamental and urgent application. The overcrowding of hospitals and medical facilities due to COVID-19, has unavoidably created delays and key issues in providing adequate medical assistance. In several cases, asymptomatic or light symptomatic COVID-19 patients have to be continuously monitored to prevent emergencies, and such an activity does not necessarily require hospitalization. Considering this research direction, this paper investigates the potentiality of cloud-based cellular networks to support remote healthcare monitoring applications implemented in accordance with the IoT paradigm, combined with future cellular systems. The idea is to conveniently replace the physical interaction between patients and doctors with a reliable virtual one, so that hospital services can be reserved for emergencies. Specifically, we investigate the feasibility and effectiveness of remote healthcare monitoring by evaluating its impact on the network performance. Furthermore, we discuss the potentiality of medical data compression and how it can be exploited to reduce the traffic load.

On cellular networks supporting healthcare remote monitoring in IoT scenarios / Petroni, Andrea; Salvo, Pierpaolo; Cuomo, Francesca. - In: FRONTIERS IN COMMUNICATIONS AND NETWORKS. - ISSN 2673-530X. - 2:(2021), pp. 1-14. [10.3389/frcmn.2021.610182]

On cellular networks supporting healthcare remote monitoring in IoT scenarios

Petroni, Andrea;Cuomo, Francesca
2021

Abstract

In the next few years, fundamental technological transitions are expected both for wireless communications, soon resulting in the 5G era, and for the kind of pervasiveness that will be achieved thanks to the Internet of Things. The implementation of such new communication paradigms is expected to significantly revolutionize people’s lives, industry, commerce, and many daily activities. Healthcare applications are considered to be one of the most impacted industries. Sadly, in relation to the COVID-19 pandemic currently afflicting our society, health remote monitoring has become a fundamental and urgent application. The overcrowding of hospitals and medical facilities due to COVID-19, has unavoidably created delays and key issues in providing adequate medical assistance. In several cases, asymptomatic or light symptomatic COVID-19 patients have to be continuously monitored to prevent emergencies, and such an activity does not necessarily require hospitalization. Considering this research direction, this paper investigates the potentiality of cloud-based cellular networks to support remote healthcare monitoring applications implemented in accordance with the IoT paradigm, combined with future cellular systems. The idea is to conveniently replace the physical interaction between patients and doctors with a reliable virtual one, so that hospital services can be reserved for emergencies. Specifically, we investigate the feasibility and effectiveness of remote healthcare monitoring by evaluating its impact on the network performance. Furthermore, we discuss the potentiality of medical data compression and how it can be exploited to reduce the traffic load.
2021
remote healthcare; cloud services; LTE; data compression; IoT
01 Pubblicazione su rivista::01a Articolo in rivista
On cellular networks supporting healthcare remote monitoring in IoT scenarios / Petroni, Andrea; Salvo, Pierpaolo; Cuomo, Francesca. - In: FRONTIERS IN COMMUNICATIONS AND NETWORKS. - ISSN 2673-530X. - 2:(2021), pp. 1-14. [10.3389/frcmn.2021.610182]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1502635
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