Performance Evaluation of an Internet of Healthcare Things for Medical Monitoring Using M/M/c/K Queuing Models

Abstract

Due to the non-stop and rapid spreading of virus pandemics all over the world, traditional healthcare monitoring capabilities of hospitals and/or medical centers are under a severe over-load. Modern computing infrastructures with the harmony of various layers of computing paradigms (e.g., cloud/fog/edge computing) for healthcare monitoring are apparently the essential computing backbone that help access and process instantly the medical data of every single patient at the very edge of the healthcare system to combat with global or regional virus contagion. Previous studies proposed different computing system architectures for healthcare monitoring but few works considered the evaluation of pure performance of medical data transmission in a comprehensive manner. In this paper, we proposed an M/M/c/K queuing network model for the performance evaluation of an Internet of Healthcare Things (IoHT) infrastructure in association with a three layer cloud/fog/edge computing continuum. The model considers a life cycle of medical data from body-attached IoT sensors in edge layer all the way to local clients (e.g., local medical doctors, physicians) through fog layer and to remote clients (e.g., medical professionals, patient's family members) through cloud layer. Furthermore, we also explore the impact of the alteration in system configuration and computing capability of computing layers in two scenarios on various performance metrics. Critical performance metrics related to quality of service are evaluated in a comprehensive manner, such as (i) mean response time of medical data transmission to fog (local) clients and to cloud (remote) clients, (ii) utilization of cloud/fog/edge computing layers, (iii) service throughput, (iv) number of medical messages in a period of time, and (v) drop rate. The simulation results pinpoint bottle-neck parameters and configurations of the IoHT infrastructure's system architecture in relation to the frequency of medical data collection for health check of patients. Thus, the findings of this study can help improve medical administration in hospitals and healthcare centers and help design computing infrastructures in accordance for medical monitoring in the severe circumstances of virus pandemics.

A Novel Cloud Architecture for Internet of Space Things (IoST)

“…Thus, the Internet of Things is not only concerned with devices connected all over the world but can also be specific to various fields depending on utilities. Some of the areas that have witnessed specific Internet of Things within their domain are Industrial IoT [9], Internet of Agricultural Things (IoAT) [10], Internet of Healthcare Things (IoHT) [11], [12], Internet of Medical Things (IoMT) [13]- [17], Internet of Nano Things (IoNT) [18], Internet of Robotics Things (IoRT) [19], [20], Internet of Drone Things (IoDT) [21], Internet of Underwater Things (IoUT) [22], blockchain and IoT [23], Social IoT [24], Green IoT [25]- [27], etc. Some of the recent areas that highlight the necessity of IoT are Next-Generation IoT [28], [29], Satellite IoT [30], Internet of nano things [31], Internet of underwater things [32], [33].…”

Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets

“…However, the work did not elaborate the involvement of performance evaluation for such medical infrastructures. The works [ 2 , 28 ] studied different pure performance metrics of an Internet of Health Things (IoHT) such as mean response time (MRT), resource utilization, service throughput and so forth. Nevertheless, the evaluation of pure performance metrics without an appropriate consideration of failure modes is optimistic and, in some cases, it is not sufficient, for instance in the case of massive data transactions as in MIS.…”

“…The use of such an MIS is to improve the quality and effectiveness of healthcare services within and across hospitals and medical centers. The implementation of existing MIS often relies on an underlying sophisticated networked system associated with advanced computing paradigms (e.g., cloud/fog/edge computing) via dedicated communication links to remote data centers [ 2 ].…”

“…Pereira et al [ 27 ] in their most recent work proposed comprehensive continuous time Markov chain (CTMC) models to investigate the availability of fog/edge computing nodes for drone-based facial recognition security systems. Only two recent works [ 2 , 28 ] presented comprehensive studies on the performance assessment of IoT-based healthcare systems with a consideration of the integration of cloud/fog/edge computing paradigms using M/M/c/K queuing network models for pure performance evaluation. Santos et al [ 29 ] was one of the most recent studies on a performability evaluation of an IoT-based medical system.…”

Privacy Preservation of User Identity in Contact Tracing for COVID-19-Like Pandemics Using Edge Computing

“…Furthermore, the authors have indicated a future improvement to resolve slight fluctuations that result from randomness differential evolution optimization. Silva et al [37] proposed a queuing model to evaluate the performance of health monitoring in the context of IoHT. The evaluation included IoT sensors and edge, fog, and cloud components while estimating metrics such as mean response time, the utilization level, and mes-sage drop rate.…”