Understanding IoHT and Edge/Fog Computing Solutions for Smart In-

Home Remote Healthcare

KUO-MING CHU

Department of Business Management

Cheng Shiu University

TAIWAN

Abstract: Healthcare information systems have been dominated by cloud technology and the Internet of Things

(IoT) for decades today. In some urgent scenarios, we reveal a prevailing architectural framework that is based

on fog/edge optimal computing approaches smart in-home remote healthcare solutions and architectures and

recognize the challenges and requirements of IoHT devices for diverse utilization instances. Even with these

upsides, conventional centralized access constraint confronts privacy problems and patient health data security.

This study likewise constructs a “blockchain-enabled edge that computes” mechanism, through which smart

contracts with the consensus protocol produced by Edge Intelligent Server are deployed to secure privacy

topics and balance scalability in trustless surroundings. We expect this paper to be a significant guideline for

the subsequent elaboration of fog/edge-based systems that compute solutions for smart in-home remote

healthcare IoT applications. There will be a change of paradigm from “hospital-based” to “distributed patient

in-home healthcare”.

Key-Words: Fog/Edge Computing; Internet of Health Things (IoHT); Smart In-Home Remote Healthcare;

Blockchain-Enabled Edge Computing; Security and Privacy

Received: August 12, 2021. Revised: April 7, 2022. Accepted: May 4, 2022. Published: June 3, 2022.

1 Introduction

The Bureau of the Census pointed out that the

ageing of the global population continues to

accelerate, and it is calculated that it will reach 1.6

billion in 2050, which is equivalent to 1 in 5 people

being a “silver tsunami”. Taiwan’s population is

ageing at the fastest rate in the world. In 2016, the

elderly population exceeded the younger population.

It is expected that the population over 65 years old

will confess over 20% of the total population in

2026, becoming a superaged society. These

phenomena show that Taiwanese society is facing

the hidden worries of “a sudden increase in the

demand for care and a shortage of manpower for

care”. The study also pointed out that there will be

approximately 720,000 people in urgent need of

long-term care in the next 10 years. Fueled by the

aging trend and the wave of declining birth rates, the

market for care needs has increased dramatically. As

the concept of care quality changes, increasing

attention will be paid to meticulous humanization

and the local aging care model. How to use the

application of intelligent technology to make up for

the shortage of manpower and fill the huge and

increasingly tight care manpower demand.

Therefore, the governments of all countries are

considering how to solve the problem of elderly

care.

In recent times, healthcare has been leveraging

the advancement of information technology and 5G

mobile networks for distributing intelligent systems

that are focused on speeding treatment and health

diagnostics up. A couple of years ago, while the IoT

was brought in with explicit architecture and mobile

phones entered humans' daily routine, the brilliant

increment is seen in IoT technology. Long-term care

is one field that has already been contacted through

the digital healthcare revolution, and we look

forward to seeing further advancements have been

issued as one of the most popular fields of IoT

technology in the coming years. In this situation, the

way healthcare is seen worldwide is drastically

changed by forceful implanted hardware's broad

pervasion together with intelligent medical sensors'

evolution and apparatuses for widespread healthcare

has made for smart IoT, so it is expected that the

number of healthcare instruments that use wearable

technologies and IoT will achieve 162 million by

the end of 2020 [1]. The Internet of Health Things

(IoHT) has been familiar with the combination of

IoT and remote healthcare surroundings, where

delicate data that are relevant to patients are

communicated from IoT tools to the server [2].

Among them, IoT devices can assist in the provision

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of discrete, more independent/safer lives and are

easy to use to seniors and people with special care

needs, further helping limit overall healthcare costs.

Aided living apparatuses are progressively

dependent on the IoT to serve respect and monitor

inhabitants. Regarding the IoT's increased adoption,

we can anticipate the following profits and shifts in

the healthcare area of elderly people: illness, injury

prevention and increased efficiency, individualized

care, and big data benefits.

Many countries around the world have become a

superaged society; therefore, for caregivers, it may

become the sandwich generation (elderly parents

above, children below) under significant family,

work, and financial stress. In-home remote

healthcare is a kind of supportive care provided at

home. In-home healthcare can reduce the time for

family members to travel to and from the hospital,

and by increasing the deployment of wireless

medical devices, hospital staff can remotely monitor

the health of patients. There is no need to be

physically present, so more patients can be cared

for. Therefore, in this article, we propose an

inspection of edge/fog and smart IoHT computing

solutions, concentrating on remote health care that

improves healthcare personnel's ability to

considerably comprehend the requirements and

patterns of the people they take care of daily,

permitting them to offer greater feedback, guidance,

and support for keeping healthy. The article's

remainder is arranged as follows. Section 2 offers

the modular framework of smart IoHT that applies

Fog/Edge computing for in-home healthcare,

including its architecture and features, which are

cautiously interpreted. Section 3 illustrates smart in-

home remote healthcare in implementing the

proposed framework. Section 4 shows the

discussion and results related to the comparison and

performance of the proposed framework. The

concluding comments are contained in the last

section and emphasize the open study trends for the

combination of IoT, fog/edge and cloud in

intelligent remote care.

2 Hospital-to-Home Model: In-home

Remote Healthcare

With age, various healthcare troubles frequently

requiring long-term and continuous medical

care can put harsh pressure on healthcare

resources and raise expenses. A scarcity of

nursing assistants, in-home health assistants and

healthcare givers, nevertheless, exceeds

worldwide, which causes care for the elderly

cost. Thus, regarding the rise in the number of

patients (such as aged citizens) living home

solely under the monitoring of their doctors, it

is required to add equipment for remote

supervision, as the patient must be diagnosed

for fast involvement [3]. There is a growing

prominence in discovering alternative

nontraditional approaches, such as in-home

healthcare, to take charge of patients to reduce

the strain on healthcare services and manipulate

expenses. Moreover, remote healthcare endures

play a crucial role in decreasing physical

contact, hospitalization, consultation time,

queuing list and whole health expenditure for a

patient when the strain, workload, and pressure

on the medical worker decrease [4]. The fast

advance of ICTs in in-home remote healthcare

services likewise makes it conceivable for

patients, particularly the elderly or disabled, to

master inevitable home solutions with comfort

from tools such as mobile phones, tablets,

laptops, and the internet. Novel healthcare

technologies' fulfilment has a function to play

in-home healthcare, stimulated into fast

utilization through the pandemic, with data

integration, artificial intelligence and remote

patient monitoring devices assisting virtual

healthcare platform efforts, including healthcare

for patients at home rather than in a hospital

environment. Not only does this broaden

healthcare from the hospital to the home, but it

also gives a sense of consolation and security to

their family and the patient that they are still

being monitored outside of a hospital condition

by a healthcare team.

The localized home-style and community-style

services allow elders to obtain the services they

need in a familiar life circle. The study likewise

reveals that many elderly people do favor in-home

healthcare with remote monitoring. For such

examples, urgent needs are represented by smart

healthcare systems. These systems adopted the

technology to supervise patients at their hospital or

homes and inform their caregivers, family persons

or doctors of their requirements and health

condition. By using IoT technology, telemedicine or

remote healthcare will become realizable. It can

help to deal with chronic diseases, yet for patients

who live in remote areas. Another cause for remote

health monitoring's growing popularity is that

wearable medical instruments and assorted

biosensors are currently readily available to

consumers from diverse sources. Cloud computing,

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Figure 1. A generic In-home Remote Healthcare Architectures

fog computing and IoT are involved in these technologies, as shown in Figure 1.

3 A General Architecture for IoHT

and Edge/Fog Computing Systems

The advent of 5G has prompted IoT

technologies to develop several more intelligent

applications that employ both network

architectures and solution platforms fields and

force IoMT applications' further development.

Among them, big healthcare data are

continually being augmented by information

generated by intelligent edge devices such as

wearable sensors. They offer information about

patient health and transmit it directly to a

network or through mobile devices to be

available whenever and wherever they are

needed. These wearable devices' greatest

advantage is that they admit disease

management and patient health monitoring in

non-clinical settings such as private homes,

nursing homes, and aided living. In recent

times, to diminish service reaction time,

enhance system quality, and enhance energy

efficiency, combining fog/edge computing into

the IoMT solutions has been suggested and

accomplished several positive outcomes. The

adoption of Fog/Edge, which computes

applications in the healthcare field, usually

copes with the intention of remote monitoring

applications leveraging wearable and domain

sensor networks for carrying out conservatory,

preventative, and responsive systems out [1]. In

this situation, most contributions involve fog

nodes playing local servers collecting and

treating health data to react to the service

prerequisites rapidly [5]. We then reveal a

prevalent architectural framework for fog/edge

computing-based in-home healthcare

applications. Both Edge and Fog Computing

leverage the proximity to the user to offer

excellent availability and diminished latency for

location-aware health services. Several

approaches that depend on hierarchical

computing strategies have been suggested to

assign and distribute the reasoning missions of

AI and machine/deep learning techniques

between the cloud, the fog and the edge layers

(or fog/edge peers), attempting to drive the

(limited) computational capabilities of edge

apparatuses to their apex [6]. We convey an

overall computing architecture of fog/edge-

based smart IoHT solutions, as shown in Figure

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In this section, the proposed framework's

principal architecture is depicted, called the smart

in-home remote healthcare framework, and smart

healthcare's general performance is improving. In

the postcloud epoch, Figure 2 reveals the proposed

framework architecture for monitoring elderly

people and remote patients, which is composed of

three principal layers: fog/edge, cloud layer and

IoHT. Meanwhile, the non-falsify-proof, cryptonym

and decentralization of blockchain technology can

offer edge computing and fog new security that

computes the surroundings. To a more beneficial

comprehension, the functions, and principal

requirements of the distinct layers in intelligent

healthcare architecture are summarized in Figure 2.

3.1. Sensor Device Layer of IoHT

Application

For a better apprehension to gather the patient and

elder people's information reflecting his activity,

several kinds of sensors and health states are

required, such as medical sensors, activity sensors,

surroundings sensors and intelligent cameras

representing the sensor network layer. The sensor's

elements detect the patient's presence, capture his

picture, and transmit it to the fog devices via

wireless and gathering readings or wired

communication protocols. Sensors ought to be in all

areas the patient can transfer to collect the

information of the patient's critical signals or

physiological signs such as temperature, heartbeat,

brain indications, blood pressure, etc. Then, the

information is sent by these sensors wirelessly to the

base stations. The influence of the IoT in healthcare

institutions is revolutionizing through providing

very high and huge areas available for an advance.

Aid to IoHT sectors such as machine learning,

artificial intelligence, wireless sensor networks and

cloud computing is provided by diverse supportive

technologies. The transmitted information is

obtained by remote healthcare centers or the cloud

of intelligent healthcare applications. Wireless body

area networks (WBANs) are the key network style

in the deployment of IoT intelligent healthcare

services exploiting technologies of ZigBee, WLAN,

and Bluetooth, which reform the eventual destiny of

medical services that are detected by offering

elderly people checking and continuous patient and

diagnosing numerous dangerous illnesses.

IoHTs services result in diverse storage of

measured and analyzed information of the health

condition of a patient occurring on cloud units to be

subsequently assessed by healthcare professionals

and doctors. Information through the cloud is treated

for a smart healthcare services framework using

artificial intelligence ideas and cognitive computing.

The employment of smart remote care architectures

with different IoT capabilities permits remote

monitoring and uninterrupted following of medical

matter of patients, a long-term inspection of

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wellbeing records of the patient, diminishing

clinical expenses and widening the innovation for

offering patient-driven care instead of medical

hospital-driven therapy.

3.2. Cloud-Based Solutions

Cloud computing will forever own a place, e.g.,

whereas many IoT apparatuses need instant

decision-making at the edge, medical institutions

may require historical analysis for model

development and process improvement. Alternative

computing assistance is offered by cloud computing

through the Internet. Compounding it with IoHT

addresses many troubles, such as information

management, data storage, communication, privacy,

and security. The healthcare industry can likewise

supply a widespread platform to obtain shared

prevailing health data and can provide “Things as a

Service” by combining the cloud concept with the

IoHT. This works best when data from multiple

edge apparatuses can be integrated centrally. It can

permit interconnected relationships, and

perceptiveness that is obtained from the historical

analysis can be driven back to the edge so that IoT-

enabled edge devices continue to evolve to create

more excellent instant decisions. The computing

architectures thus become an integration of edge and

cloud computing, where IoT devices operate in real-

time at the edge, where raw data are gathered and

processed, and metadata are shared to the cloud for

continuous process advance and comprehensive

historical analysis.

Distant healthcare is one of the cardinal services

of a digital and intelligent healthcare service that

will decrease the burden on healthcare workers and

crowded hospitals and would be more advantageous

for offering healthcare to patients who reside in very

distant regions. The cloud layer comprises servers

with exceedingly enormous storage, and long-term

treatment decisions are made by processing and

analysis capacities assisting medical workers for

patients. Cloud-based IoT programs have been

smoothly arrayed in the last decade; nevertheless,

Quy et al. [7] indicated that existing IoHT packages

that are dependent on the cloud possess high service

reaction time and restricted scalability. Cloud-only

medical systems comprise wearable devices, cloud

servers, and a network. These components may have

large distances between elements, which further

worsens high latency trouble (revealed in Figure 2).

Transmitting information to the cloud frequently for

calculation is the reason for higher power

expenditure and related costs, even more so

currently, when the amount of information that is

produced through sensors is extremely large. Cloud-

based solutions in a similar way do not offer low-

cost mobile surroundings to the user, which is

needed for numerous patient monitoring scenarios.

Recently, many medical monitoring systems have

embraced a contrast between conventional cloud

systems and distributed or fog/edge approaches.

3.3. Edge- and Fog-Based Applications

Edge computing is a distributed computing system

transferring digital data, services and programs from

the network's cardinal node to the edge nodes on the

network logic for processing. Edge computing

resolves large-scale services originally dealt with by

fundamental nodes, splits them into smaller and

easier-to-manage portions, and distributes them to

edge nodes for processing. The edge node

approximates the user terminal device, which can

accelerate the transmission and processing of data

and decrease the delay. Thus, edge and fog

computing are advantageous to serve localized

healthcare solutions since settling the IoT devices

next to the user or in the user declines the proximity

of the network latency time and reaction to very low

service. Fog that apparently computes will be one of

the most feasible services for the IoHTs solutions in

urgency save scenarios and in a number of instant

patient monitoring systems, such as patients with

records of stroke, heart disease and blood pressure.

The combination of IoT devices with the edge/fog

and cloud computing can be an assisting hand for

the healthcare industries to decrease the care

expense and network that processes intelligent IoT

devices charge the same. The Internet gateway,

local router, and fog servers are encompassed by the

fog layer. This layer conveys and transmits

information between the cloud and IoT sensor

device layers. It employs offered information from

the underlayer, federated learning, fog computing,

and deep learning to create decisions for urgent

medical scripts. Along with it, there are likewise

some other advantages of fog and edge computing,

such as scalability, usability, dependability, and

performance. Hence, the major concept of

combining IoHT with these techniques is to move

from habitual forms of caring for the patients,

inspecting the hospitals, etc. to smart in-home

healthcare styles.

In recent times, fog computing's vast potential

was presented by the boom of healthcare IoT

solutions that were dependent on fog computing.

Key traits and basic services, such as data analysis

and big data storage, are provided by the cloud

layer. Healthcare systems are one of the most

suitable paradigms of those programs that require

real-time processing of big data yield through

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medical apparatuses and sensors and with ultralow

latency. It should be mentioned that the overall

burden on the cloud system would be less in an IoT-

Fog-Cloud combined system than in a system where

there are merely IoT devices and cloud servers. This

is because some of the tasks are executed at the fog

layers, such as safety checking and data analysis. In

a fog-based IoT healthcare architecture, cloud

computing is used to assist fog services. There are a

number of studies confirming fog's effectiveness in

healthcare applications. The end user may be an

individual, a system administrator, a medical

specialist, a professional, or a patient.

To deal with the chaos in the distinction between

edge and fog computing, we begin this controversy

by depicting both edge and fog computing from

distinct viewpoints. Several scholars ascertain edge

and fog computing layers as the equal notion with

disparity merely in their names, whereas others

distinguish them as two distinct ideas [8]. Shukla et

al. [9] claim that fog computing is a foundation of

the IoT; it expands distinct services and cloud

computing to devices such as switches, routers,

multiplexers, etc. Edge computing promotes

solutions, applications and data from the kernel to

the network edge, depending on the core-edge

topology. Smart in-home remote healthcare aims at

permitting intelligent control of distinct intelligent

end-user devices that are linked inside homes and

non-hospital institutions. The linked smart

healthcare devices produce plenty of information for

intelligent control and decision-making in-home

distant healthcare. The processing and the analysis

of these data demand a vast volume of resources and

storage needing a scalable application to warrant

feature and progression of service without any

degradation. Edge computing provides an extremely

distributed application for developing intelligent in-

home remote healthcare systems in an efficient

approach by treating the data at the network edge

that provides low latency, time-saving data

processing and less energy expense. Edge

computing-based intelligent home objectives at

improving the succeeding IoHT architecture,

particularly those requiring a short reaction time,

such as observation and smart manipulation of smart

devices inside the home.

3.4. Technology of Blockchain-Enabled Edge

Computing

Smart in-home remote healthcare applications,

however, create a significant provoke into fighting

in preserving the security and privacy of patient care

information. Because of the secret nature of location

information and health, it is significant to warrant a

high degree of safety to users [10]. Patients' health

information at the network's edge, frequently on

mobile devices, should be encrypted prior to

transferal to other nodes. Because of resource

restrictions, this should be performed in an efficient

but effective approach. Many potential computing

nodes provide novel approaches for acquiring a

patient's information but could also permit a higher

degree of privacy as a consequence of the

distribution of important information. An excellent

perspective for the integration of edge computing

and blockchain has been evidenced accurately by

the above discussion. On the one hand, it establishes

full adoption of the edge in the edge computing

cognate to the end-user to comprehend the

administered instant processing technique; on the

other hand, it uses the decentralized acceptability

attribute of blockchains to even elevate the

scalability of distributed construction. Furthermore,

it is the collaborative logical and physical node of

the edge computing blockchain network and

network.

The principal properties of a blockchain as the

underlying kernel technique have become an

emerging broad-scale network data/information-

sharing technology, and several types of edge nodes

have been created to efficiently balance complexity

in trustless surroundings. Manager nodes assisted

through an Edge Intelligent Server (EIS) have solid

computing and guaranty capacities and combine

various types of equipment to establish diverse

edge-cloud applications. It profits from EIS, where

smart contracts by the concord protocol are

allocated, which can accomplish information

management and service customization. Complex

logic is depicted by the smart contract that runs on

the blockchain as code, which turns into contract-

based automation protocol implementation. Data

services that are based on tamper-proof features and

distributed data storage can guarantee service

process records' integrity. The smart contract then

writes down the store event's execution as a block.

Among them, the EIS nodes deal with it and inspect

their data to the linkage following the agreement

mechanism by using smart contracts. Finally, a

novel block is replenished to the blockchain

pursuant to the agreement algorithm. Consequently,

blockchain-enabled edge computing comprises the

end-user sensor layer, EIS, kernel roles of

blockchain, cloud servers and their system portions,

as demonstrated in Figure 3.

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Figure 3. Diagram of blockchain-enabled edge computing

For the objective of offering a more elastic

application, a blockchain-enabled system must be

exploited for the manager to reach the resources in

IoHT sensors and edge computing. The

decentralized architecture carries several attributes:

device nodes are controllable synchronously by

multiple managers and belong to dissimilar

management fields throughout their lifetime in the

blockchain, the managers and device nodes are

interrelated by the blockchain network, and

constricted managers are able to control apparatus

nodes well with Edge Smart Server. It in total is an

intelligent strategy to allocate systems and crux

applications. The edge layer is broadened by the

edge computing layer on cloud computing.

4 Concluding Remarks and Future

Work

Under the situation that the global low birth rate

trend is difficult to reverse, it is obviously a luxury

to increase the nursing manpower to improve the

long-term care system. In the past, people used to

regard ageing as a burden. The aim of smart in-

home novel healthcare technologies is to permit

intelligent control of distinct smart end-user devices

linked inside nonhospital institutions and homes.

Not only does this broaden healthcare from the

hospital to the home, but it also gives a sense of

consolation and security to their family and the

patient that they are still being monitored outside of

a hospital condition by a healthcare team. In this

article, to decrease service reaction time and

improve system quality and energy efficiency, we

propose an overall architecture for fog/edge

computing-based smart IoHT in-home healthcare

applications. Therefore, smart in-home remote

healthcare is an excellent alternative to be utilized at

homes, aged care homes, or hospitals soon due to

increased overall system intelligence, energy

efficiency, less network usage, and quick reaction

time. There will be a change of paradigm from

“hospital-based” to “distributed patient in-home

healthcare”.

First, depending on the IoT era, smart

healthcare's notions will be a broad revolution of the

next Internet in remote healthcare industries. In this

transition, machine learning approaches and AI play

a critical mission, but their fulfillment requires

computational capability that is usually available

merely with cloud services' means. To offer

computing approaches for these services, many

computational techniques have been provided,

including cloud computing, edge computing, and

fog computing. An accustomed healthcare solution

that is based on the cloud has several limitations,

such as high computing costs and service reaction

time. To save or instant ambulance urgency and

rescue solutions, these applications need instant

service reactions. After all, completely local

management's selection is even unrealistic because

of limitations in treating storage and capacity,

particularly in the example of real-time performance

and dynamic monitoring. Therefore, fog/edge

computing was born to emerge the energies of the

cloud nearer to end-users to close in IoHT systems'

requirements. Because the distance between the

cloud database and the end-users center is curtailed,

fog computing obtains prominent benefits over

cloud computing and is particularly agreeable for

instant IoHT solutions. It is expected that this

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article's achievements will be a major guideline for

future research in the domain of the healthcare

industry and fog computing.

Furthermore, we have revealed the associated

applications, data operations, and the social capital

perspective as detailed in the surveyed research.

Edge computing and blockchain can be a complete

match because of the supplementary features they

unveil and the identified demerits that can be

mended. This survey concentrates on the privacy

facets and security of blockchain-based edges that

compute networks. We dealt with the profits and

challenges of these subareas of privacy and security.

We have likewise investigated the research from the

standpoint of blockchain-enabled edge computing,

including expense, latency, safety, position

awareness, and energy efficiency. The results

indicate that authorization considers both security

and efficiency with “blockchain-enabled edge

computing” integration. Our completion is that

blockchain-edge computing orchestration is forced

to lead to a significant revolution in remote

healthcare industries, extending the way for new

distributed architectures and novel business models.

Finally, the principal properties of a blockchain

as the underlying kernel application have become

aborning broad-scale network data-sharing

architecture, and several types of edge nodes are

created to efficiently balance complexity in trustless

surroundings. Manager nodes that are served

through an EIS have storage capacities and solid

computing and combine software and hardware to

establish diverse IoT-edge-Fog-cloud applications.

It profits from EIS, where smart contracts with the

acceptability protocol are allocated, which can

accomplish information management and service

customization. Complex logic is depicted by the

smart contract performing on the blockchain as

code, which transforms the fulfillment of the

contract-based automation protocol. Data services

that are based on tamper-proof features and

distributed data storage can guarantee service

process records' integrity. The smart contract then

records the store event's execution as a block. In this

examination, we intend an all-in-one computing

application solution. In addition, we recently

investigated IoT systems that depend on edge and

fog computing programs in remote care industries.

Depending on the overview consequents, we

resolved the challenges, next study orientations and

solution feasibility. It is expected that this study's

outcomes will be a significant direction for future

research in the domain of the remote in-home

healthcare industry and fog/edge computing.

References:

[1] Akmandor, A. O. and Jha, N. K., “Smart

health care: an edge-side computing

perspective”, IEEE Consum. Electron. Mag.

7(1), 29–37 (2017).

[2] Wang, L., Ali, Y., Nazir, S. and Niazi, M.,

“ISA Evaluation framework for Security of

Internet of Health Things system using AHP-

TOPSIS methods”, IEEE Access, 8, 152316-

152332 (2020). doi:

10.1109/ACCESS.2020.3017221.

[3] Mshali, H., Lemlouma, T., Moloney, M. and

Magoni, D., “A Survey on health monitoring

systems for health smart homes”, International

Journal of Industrial Ergonomics. (2018).

[4] Taiwo, O. and Ezugwu, A. E., “Smart

healthcare support for remote patient

monitoring during covid-19 quarantine”,

Informatics in Medicine Unlocked. 20, 100428

(2020).

https://doi.org/10.1016/j.imu.2020.100428

[5] Tang, M. S., Wu, X. R., Lee, H. W., Xia, Y.,

Deng, F. M., Moreira, A. L., Chen, L. C.,

Huang, W. C. and Lepor, H., “Electronic-

cigarette smoke induces lung adenocarcinoma

and bladder urothelial hyperplasia in mice”,

Proceedings of the National Academy of

Sciences, 116(43), (2019). doi:

10.1073/pnas.1911321116.

[6] Abdellatif, A. A., Emam, A., Chiasserini, C. F.,

Mohamed, A., Jaoua, A. and Ward, R., “Edge-

based compression and classification for smart

healthcare systems: concept, implementation,

and evaluation”, Expert Systems with

Applications, 117, pp. 1–14 (2019).

https://doi.org/10.1016/j.eswa.2018.09.019

[7] Quy, V. K., Hau, N. V., Anh, D. V. and Ngoc,

L. A., “Smart healthcare IoT applications

based on fog computing: architecture,

applications and challenges”, Complex &

Intelligent Systems. 17 (2021).

https://doi.org/10.1007/s40747-021-00582-9

[8] Laroui, M., Nour, B., Moungla, H., Cherif, M.

A., Afifi, H. and Guizani, M., “Edge and fog

computing for IoT: A survey on current

research activities & future directions”,

Computer Communications, 180, pp. 210-231

(2021). doi:10.1016/j.comcom.2021.09.003

[9] Shukla, S., Hassan, M. F., Khan, M. K., Jung,

L. T. and Awang, A., “An analytical model to

minimize the latency in healthcare internet-of-

things in fog computing environment”, PLoS

ONE. 14(11): e0224934 (2019).

https://doi.org/10.1371/journal.pone.0224934

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DOI: 10.37394/23205.2022.21.22

Kuo-Ming Chu

E-ISSN: 2224-2872

178

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[10] Hartmann, M., Hashmi, U. S. and Imran, A.,

“Edge computing in smart health care systems:

Review, challenges, and research directions”,

Trans Emerging Tel Tech., pp. 1-25 (2019).

https://doi.org/10.1002/ett.3710

Data Availability

All data, models, and code generated or used during

the study appear in the submitted article.

Conflict of Interest

The corresponding author (Kuo-Ming Chu) states

that there is no conflict of interest.

Sources of Funding for Research

Presented in a Scientific Article or

Scientific Article Itself

No funding is involved.

Creative Commons Attribution

License 4.0 (Attribution 4.0

International , CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/dee

d.en_US

WSEAS TRANSACTIONS on COMPUTERS

DOI: 10.37394/23205.2022.21.22

Kuo-Ming Chu

E-ISSN: 2224-2872

179

Volume 21, 2022