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. 2023 Nov 1;23(21):8885.
doi: 10.3390/s23218885.

Toward QoS Monitoring in IoT Edge Devices Driven Healthcare-A Systematic Literature Review

Muhammad Irfan Younas  1   2 , Muhammad Jawed Iqbal  2 , Abdul Aziz  3 , Ali Hassan Sodhro  4
Affiliations

Toward QoS Monitoring in IoT Edge Devices Driven Healthcare-A Systematic Literature Review

Muhammad Irfan Younas et al. Sensors (Basel). .

Abstract

Smart healthcare is altering the delivery of healthcare by combining the benefits of IoT, mobile, and cloud computing. Cloud computing has tremendously helped the health industry connect healthcare facilities, caregivers, and patients for information sharing. The main drivers for implementing effective healthcare systems are low latency and faster response times. Thus, quick responses among healthcare organizations are important in general, but in an emergency, significant latency at different stakeholders might result in disastrous situations. Thus, cutting-edge approaches like edge computing and artificial intelligence (AI) can deal with such problems. A packet cannot be sent from one location to another unless the "quality of service" (QoS) specifications are met. The term QoS refers to how well a service works for users. QoS parameters like throughput, bandwidth, transmission delay, availability, jitter, latency, and packet loss are crucial in this regard. Our focus is on the individual devices present at different levels of the smart healthcare infrastructure and the QoS requirements of the healthcare system as a whole. The contribution of this paper is five-fold: first, a novel pre-SLR method for comprehensive keyword research on subject-related themes for mining pertinent research papers for quality SLR; second, SLR on QoS improvement in smart healthcare apps; third a review of several QoS techniques used in current smart healthcare apps; fourth, the examination of the most important QoS measures in contemporary smart healthcare apps; fifth, offering solutions to the problems encountered in delivering QoS in smart healthcare IoT applications to improve healthcare services.

Keywords: Internet of Things (IoT); artificial intelligence (AI); cloud computing; machine learning; quality of service; smart healthcare.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
QoS requirements at different layers of smart healthcare applications [7].
Figure 2
Figure 2
KCM of Scopus results as per keyword co-occurrence frequency [8].
Figure 3
Figure 3
KCM of Scopus results after removing synonyms, abbreviations, and different spellings using thesaurus.
Figure 4
Figure 4
Keywords mentefacto indicates the hierarchy of included and excluded keywords [10].
Figure 5
Figure 5
The search hierarchy shows the straightforward and mixed search terms that were used to look for the aforementioned data sources (using AND and OR).
Figure 6
Figure 6
The SLR flow diagram outlines the selection process and selections taken at various stages of the systematic review as well as how the articles that were found were screened [13].
Figure 7
Figure 7
Model for a smart healthcare architecture that uses wearables, cloud, fog, and edge computing for healthcare applications [18].
Figure 8
Figure 8
The IoT architecture consists of sensors and actuators for the perception layer, network layer protocols, and application layer components [25].
Figure 9
Figure 9
The front and back ends of a cloud computing system are two separate components. Over an intranet or via the internet, both sides of the connection can communicate with one another [26].
Figure 10
Figure 10
Venn diagrams show the key similarities and technical differences between the two computing paradigms.
Figure 11
Figure 11
Machine learning is the process of using carefully crafted code to build systems that learn and evolve on their own [30].
Figure 12
Figure 12
In terms of various requirements, different healthcare sectors demand QoS guarantees [34].
Figure 13
Figure 13
Analysis of prior studies on smart healthcare to identify recent review study methodologies and their emphasis on quality of service.
Figure 14
Figure 14
Article distribution according to year of publication.
Figure 15
Figure 15
Distribution of papers according to research methods.
Figure 16
Figure 16
This pattern shows that QoS issues in smart healthcare are receiving increased attention from the research community [46,47,48,49,50,51,52,53,54,55,56].
Figure 17
Figure 17
The QoS parameters related to SHAs that have received the most research include delay, latency, and energy consumption.
See this image and copyright information in PMC

References

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