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1.
IEEE Netw ; 37(6): 141-149, 2023 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-38765241

RESUMO

Wireless connectivity delay, disruption, or failure can significantly affect the performance of wireless-enabled medical devices, which in turn causes potential risks to the patient. Notably, the challenges related to connectivity provisioning are exacerbated in the fifth-generation (5G)-enabled healthcare use cases where mobility is utilized. In this article, we describe relevant 5G-enabled healthcare use cases involving mobility and identify the connectivity challenges that they face. We then illustrate practical implementation considerations, tradeoffs, and future research directions for enabling reliable 5G healthcare transmissions. This is done through simulation of connected ambulances as an example use-case.

2.
Healthcom ; 2022: 81-87, 2022 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-36749675

RESUMO

The fifth generation of cellular network (5G) can facilitate in-ambulance patient monitoring, diagnosis, and treatment by a remote specialist. However, 5G coverage and link quality can vary in time and location. The ambulance route selection can help meet the communication requirements of the in-ambulance applications. In this paper, we propose an innovative ambulance route selection framework which combines the communication requirements along with the network coverage and resources. The framework leverages the minimization of drive test (MDT) data to estimate the network coverage along the ambulance routes. To address the uneven distribution of location-based user-generated MDT data, we examine the performance and trustworthiness of several interpolation techniques to enrich the global MDT map for route selection. A simulated analysis shows that the proposed framework can dynamically adapt to varying application requirements as well as rapidly changing network conditions such as outages. Results also reveal that nearest neighbor and kriging interpolation techniques help complement the proposed framework by addressing the data sparsity problem.

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