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Review Article
Smart Home System: A Comprehensive Review
Arindom Chakraborty,
1
Monirul Islam,
1
Fahim Shahriyar,
1
Sharnali Islam,
2
Hasan U. Zaman,
3
and Mehedi Hasan
3
1
Department of Electrical and Electronic Engineering, University of Science and Technology Chittagong,
Chattogram 4202, Bangladesh
2
Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh
3
Department of Electrical and Computer Engineering, North South University, Dhaka 1229, Bangladesh
Correspondence should be addressed to Mehedi Hasan; mehedi.hasan01@northsouth.edu
Received 6 August 2022; Revised 9 November 2022; Accepted 15 March 2023; Published 21 March 2023
Academic Editor: Iouliia Skliarova
Copyright ©2023 Arindom Chakraborty et al. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
Smart home is a habitation that has been outtted with technological solutions that are intended to provide people with services
that are suited to their needs. e purpose of this article is to perform a systematic assessment of the latest smart home literature
and to conduct a survey of research and development conducted in this eld. In addition to presenting a complete picture of the
current smart home system’s (SHS) development and characteristics, this paper provides a deep insight into latest hardware and
trends. e research then moves on to a detailed discussion of some of the important services provided by the SHS and its
advantages. e paper also statistically discusses the current and future research trends in the SHS, followed by a detailed portrayal
of the diculties and roadblocks in implementing them. e comprehensive overview of the SHS presented in this paper will help
designers, researchers, funding agencies, and policymakers have a bird’s-eye view of the overall concept, attributes, technological
aspects, and features of modern SHSs.
1. Introduction
In recent years, the term smart has become synonymous
with any technology that boasts some level of articial in-
telligence. e ability to gather information from its sur-
roundings and react accordingly is the essential
characteristic of smart technology [1]. With the main ob-
jective set as to the welfare of humanity, smart technology
has become the main driving force for pioneering ideas such
as the smart home system (SHS). Due to the development of
smart products and services, the world has witnessed the rise
of device interconnectivity and information sharing, which
has inuenced the rapid development of smart home
technology globally [2]. Fueled by the advantages provided
by smart technology and a possible large global market,
interest in smart home technology has skyrocketed among
researchers.
In the eld of home automation and management, the
smart home has become a very promising sector. e term
“smart home” is not strictly limited to human abodes. It
rather has a wider range of technological implications which
include smart or intelligent habitation/living [3]. e in-
clusion of computer-regulated technology such as smart
cities, smart manufacturing, and smart societies broadens
the notion of the smart home beyond the human residence.
Smart home systems (SHSs) comprise a division of
ecumenical computing that encompasses integrating smart
technology into homes to achieve comfort, safety, security,
healthcare, convenience, and energy conservation [4–6]. By
oering automated and remote home appliance control and
services, smart homes provide a higher quality of life. One of
the main services provided by SHSs is a remote monitoring
system that uses telecommunication and the Internet to oer
remote home control and elderly care. An SHS user can
control home appliances remotely from anywhere and can
perform tasks before arriving home. Smart sensors can
monitor home temperature and humidity and maintain an
optimal atmosphere as per the user’s preference. With the
Hindawi
Journal of Electrical and Computer Engineering
Volume 2023, Article ID 7616683, 30 pages
https://doi.org/10.1155/2023/7616683
help of a smart object detection system, enhanced security
systems for smart homes can provide better safety.
e rapid growth of automation technology has led it to
domestic service, which introduced the term “smart home.”
Due to the high demand and success in the market, constant
upgrading in this sector is noticeable. To get a better un-
derstanding of the ongoing demand as well as the future of
this industry, predictions and analyses carried out by the
World Economic Forum (WEF) suggest that the value of this
industry will likely hit 13 trillion USD within 2030 [7].
According to Statista, for the year of 2021, the smart home
market was likely to hit 99.41 billion USD globally [8]. All the
predictions and studies target the greater growth of smart
home technology. As it grows, expectations are likely to be
increased as well. e smart home was considered a con-
venience product at the preliminary stage, but as the
technology grew, it turned out to be a solution of eciency,
preference, and security as well. Studies have shown that
smart homes can reduce the total electricity cost, which can
greatly aect eciency [9, 10]. Even though a single unit of
a smart home saves very little using this technology, col-
lectively, the impact is greater than what it was before. Smart
homes can also play a great role in the security system of the
house, which is another eld with great potential. A security
system can utilize various sensors within the smart home to
ensure a safer environment [11, 12]. e smart home can
also play a great role in the ambient luxury of the home,
which is its biggest market. However, if implemented cor-
rectly, smart home technology can be properly utilized for
the handicapped, elders, and patients as well [13].
Application-specic systems such as motion and image
recognition systems can provide an assistive technology that
can be utilized by patients with limitations due to age or
certain conditions [14]. Virtual reality systems are also
coming into play in similar cases as well [15].
A revolution in technological development caused
a mass advancement in the Internet, information, and
communication technology, which led to the develop-
ment of better quality SHSs at a relatively lower cost.
Interest in SHS research is at its all-time highest. Re-
search interest in SHSs has been high throughout the last
decade. However, there is a lack of collective information
assortment and demonstration of the previous works
related to this eld for future research reference. Several
recent review papers [16–18] provided short descriptions
of the recent advancements in the eld of SHSs and
presented the advantages and disadvantages of the so-
lutions discussed by researchers. However, these works
failed to provide a categorical analysis of the dierent
approaches followed by researchers or microprocessors
and sensors used in the development of recent SHSs or
services provided by SHSs. For these reasons, it has
become necessary to review SHSs based on technological
approaches, microprocessors, sensors, networking
methods, computational techniques, and services. In this
review, an overview of the current works on techno-
logical development in smart homes, based on the
aforementioned subjects, is presented. is paper also
analyses the data obtained from the works of several
researchers to provide an accurate description of the
specic areas and methods followed by scholars in SHSs.
is work lls up the gap left by the previous reviews as
mentioned previously by illustrating as follows:
(i) Various technological approaches used by dierent
SHSs and their suitability
(ii) Microprocessors and microcontrollers used in the
development of SHSs
(iii) e various types of sensors used in the develop-
ment of SHSs
(iv) A comprehensive review and classication of SHSs
based on numerous characteristics such as net-
working technologies, computational approaches,
user interfaces, and services provided
(v) A thorough analysis of the data collected from the
literature
e main aim of this review is to provide a collection of
the most recent research advancements made in the eld of
SHSs. is extensive review will help researchers, engineers,
designers, and other people involved in the development of
SHSs oering a systematic and comprehensive evaluation of
SHSs, along with a general idea of recent trends.
Figure 1 presents the overall structure of this paper. An
introduction and motivation for this paper are presented in
Section 1. e process through which the research materials
are selected is described in Section 2. is process is divided
into 3 segments: planning, review, and result. Section 3
presents a literature review of a few selected research articles.
Sections 4–6 describe the technological approaches,
microcontrollers, and sensors used in the development of
SHSs by various researchers. Later sections analyze the
networking technologies, user interfaces, computational
methods, and services provided in SHSs. A comprehensive
analysis based on the reviewed factors in the previous
sections is provided in the discussion section. Finally,
concluding observations have been provided in the con-
clusion section.
2. Methodology
e process of assessing and elucidating all available re-
search pertinent to a certain topic, question, subject matter,
or occurrence in a particularly faultless way is termed
“systematic literature review” (SLR). e objective of such
a review is to present an impartial evaluation of a research
topic with the help of a reliable, rigorous, and inspectable
methodology [19]. e SLR provides future researchers with
a short and informative guide of the previous works carried
out by other researchers on a particular eld or topic.
Fundamentally, there are two common reasons for per-
forming an SLR:
(i) Summarization and evaluation of the most recent
developments available for a specic technology or
research
(ii) Creating a path for potential future research on
a topic by identifying gaps in the present literature
2Journal of Electrical and Computer Engineering
(i) Introduces the topic
(ii) Provides motivation, scope and main goals of the work
1. Introduction
(i) Detailed explanation of the research methodology
(ii) Provides search strategy and selection process of
relevant articles
2. Methodology
(i) Provides and overall overview of SHS
(ii) Overall basic architecture of SHS
3. SHS Overview
(i) Major technological and approaches in SHS
(ii) Classication and quantitative analysis of SHS
4. Technical
Approaches in SHS
(i) Provides a detailed study on the dierent processors/
controllers used in SHS
(ii) Provide information on the processors/ controllers
5. Central Processors/
Controllers used in STS
(i) Provides a list sensor topologies used in SHS
(ii) Basic information of the sensor topologies
6. Sensors Utilized
(i) Basic networking technologies in SHS
(ii) Information on the Networking technologies
7. Networking
Technologies
(i) Provides information on the User Interfaces in SHS
(ii) Discussion on the User Interfaces
8. User Interfaces
(i) Overall computational approaches in SHS
(ii) Details on the Computational Approaches
9. Computational
Approach
(i) Identify and classify various services oered by SHS
(ii) Information on the oered services
(i) Overall discussion based on the information provided
in the previous sections
11. Discussion
(i) Overall concluding remarks based on the study
conducted
(ii) Future research insights
12. Conclusion
10. Services
Figure 1: Structure of this paper.
Journal of Electrical and Computer Engineering 3
is segment describes the methodologies implemented
in this research work to review the currently available works
and build up a panoramic analysis of the SHS concept. is
review evaluated and synthesized the existing works on SHSs
based on various aspects such as communication mediums,
energy management, sensors, and comfort. To procure in-
formation from current publications on SHSs, reputed
publishers such as IEEE Xplore, SpringerLink, ACM Digital
Library, ScienceDirect, and MDPI were utilized. e re-
search procedure portrayed in [20] was adopted for this
work, which categorized the reviewing procedure of this
paper into three major stages. e stages are termed the
planning stage, the review stage, and the result stage.
A guideline to search for dierent reviews of the liter-
ature and materials is dened in the planning stage. e
review stage concentrates on strict instructions for de-
veloping keywords and search strings to nd precise data to
review from various sources. Collection of preliminary re-
sults, extraction of relevant research materials, and cate-
gorization of the candidate papers are also carried out in this
stage. Finally, a comprehensive evaluation of the chosen
materials is conducted in the result stage.
e overall process of the search and selection of the
research paper are illustrated in Figure 2.
2.1. Planning Stage. Identication of the objectives of the
study and development of the review protocols comprised
the planning stage of the review. After a preliminary search
of the available research material, the necessity for further
study of the smart home system was identied. Following the
establishment of the eld of study, specic protocols were
developed for the study, which included the search criteria,
database selection, inclusion and exclusion criteria, and the
process of searching.
2.1.1. Formulation of the Research Questions. At an early
point of the planning stage, a list of questions that specify the
key objectives of the research is created. e Goal-Question-
Metrics approach proposed by Van Solingen [21] was used
to construct the most relevant research questions for this
study. Following this approach, four main coordinates were
obtained as follows:
(i) Purpose: investigate, evaluate, and assess
(ii) Issue: complete analysis of the smart home system
(iii) Object: smart home system
(iv) Viewpoint: a researcher’s point of view
Based on these points, four major research questions
were formulated as follows:
(i) What are the tactics or methods for constructing
a smart home system?
(ii) What types of sensors were utilized in the devel-
opment of the smart home system?
(iii) What kinds of communication protocols and net-
working tools were implemented?
(iv) What types of security measures were considered
for smart home systems?
e main objective of this research work is to review the
existing research work on smart home systems and discover
the approaches adapted, the hardware and networking
technologies utilized, and the security systems considered.
e four previously mentioned questions are linked to these
objectives. Apart from that, the formulated research ques-
tions were also used to detect keywords such as “smart,”
“home,” “system,” “solution,” “sensors,” “communication,”
“networking,” and “security.” Based on these keywords,
initial search strings were established to detect the
literary works.
2.2. Search and Review Stage. e second stage consists of
a systematic search in preselected databases, which are
shown in Table 1, based on search terms found in the
planning stage and ltering through the search results for the
most relevant research papers.
e most important term here is “smart home,” and all
the keywords are selected based on it. As the goal is to cover
the entire zone of the smart home system, the technologies
utilized inside the house and beyond, and the overall features
and comfort provided by the system, the application of the
term “smart home” is validated.
e keywords formulated in the planning stage were
used as the primary search string, and several types of lit-
erature reviews were obtained from the search. To keep the
review most up-to-date, only the papers published in the last
ten years were analyzed, where priority was given to the
papers from the last six years.
Following the primary search, papers were inspected and
sorted manually by reviewing the title, abstract, and con-
clusion. In this manual sorting, if a paper is found to include
keywords and could provide necessary details to satisfy the
inclusion criteria for this review, it was selected. For this
purpose, a set of inclusion and exclusion criteria were
created. e inclusion criteria for review materials are
provided as follows:
(i) e article’s keywords must match at least some of
the search terms dened.
(ii) Simple and understandable English is used to write
the paper.
(iii) Articles/papers that concentrated on the activity
recognition of inhabitants, monitoring, and gath-
ering information about the user experience and
comfort.
(iv) e article must be published within the last
six years.
e exclusion criteria were as follows:
(i) Potentially duplicate reports on the same research
(ii) Papers that are not published in the English
language
4Journal of Electrical and Computer Engineering
(iii) e research focused on smart grid-connected
smart homes, smart cities, and outdoor in-
telligence services
(iv) Specically, tailored studies on certain smart home
appliances such as smart refrigerators, smart mir-
rors, and robots
(v) Not related to the research question
After ltering the papers through these criteria, for more
accurate and denite results, the primary search string was
modied with additional keywords such as “smart sensors,”
“risk,” “threat,” “elderly,” “AI,” “IoT,” and “wireless.” Using
this modied search string, the electronic databases were
searched again, and the resultant papers were again man-
ually inspected. Finally, all the selected research materials
were meticulously revised for the result stage. Figure 3
represents the article selection process from dierent da-
tabases through multiple searches.
2.3. Result Stage. In the last phase of the review process, an
analytical report is presented, which includes the year of
studies, the research process, the region of technology
considered, and publishing organizations. e ndings were
utilized to establish the fundamental segments of this review
and reveal the technological developments in the smart
home system throughout the years.
A total of 111 research papers were selected (excluding
review papers) after the methodological process of sorting
them. Based on the selected articles and the information
provided, ve important graphical portrayals can be
achieved. Figure 4 depicts the year-wise amount of the
literature published from 2012–2021, based on the selected
papers. is representation shows that the highest number of
papers was published during the 2018–2020 period.
Figure 5 depicts the publisher-wise number of the lit-
erature collected. Five dierent electronic databases have
been used for collecting papers. e highest number of
papers was collected from IEEE Xplore totaling 47 papers, 9
from ScienceDirect, 19 from MDPI, 16 from SpringerLink,
10 from ACM Digital Library, and 10 from Hindawi.
Figure 6 represents the types of research material
published by the authors. It can be seen that the majority of
the authors (66) tended to create experimental design-based
articles. e other types include 45 theoretical or conceptual
works, 16 reviews, and 3 case studies. It should be noted that
the review articles and case studies were not used as review
materials for this article; instead, they were used for aca-
demic references.
Planning Stage
Set Objective
Question
Related
Keyword
selection
Select
Scientific
Database
Primary
Search string
Formulation
Search & Review Stage
Content
Revision
Scientific
Database
Queries
Primary
Search
Result
Time Based
Sorting
Manual
Sorting
Result of
first sorting
process
Second
sorted result
Manual
Sorting
Scientific
Database
queries
Fine-tuned
search
string
Result Stage
Classification of Core Parts
Figure 2: Research method in detail.
Table 1: List of electronic databases used for searching articles.
No. Electronic databases
1 IEEE Xplore
2 SpringerLink
3 ScienceDirect
4 ACM Digital Library
5 MDPI
6 Hindawi
Journal of Electrical and Computer Engineering 5
IEEE Xplore (124)
SpringerLink (89)
ScienceDirect (57)
ACM (95)
MDPI (87)
Hindawi (23)
Modified Search
String (475)
Final Selection
(111)
Figure 3: Paper selection process from dierent databases.
1111
11 11
20 20
23 23
0
5
10
15
20
25
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Number of Papers
Year
Figure 4: Year-wise frequency of literature publication.
42%
8%
15%
9%
17%
9%
IEEE Xplore
ScienceDirect
SpringerLink
ACM digital Librar
y
MDPI
Hindawi
Figure 5: Publisher-wise percentage of the papers reviewed in this work.
6Journal of Electrical and Computer Engineering
3. Smart Home System
In the last decade, the SHS has achieved unprecedented
success, and researchers are continuously working to im-
prove on its past works. With the help of IoT, it is now easier
than ever to establish communication between home ap-
pliances and users. An IoT-based SHS has become the most
popular choice in recent years. By connecting all of the
devices through the Internet, it is now possible to maintain
all of the home equipment simultaneously. Users can now
monitor and control several aspects of their house from
anywhere in the world with the help of IoT-enabled devices.
rough machine learning and articial intelligence, smart
homes can now recognize shapes, sounds, and gestures, thus
making the smart home experience much more comfortable.
e availability of powerful processors facilitates the
implementation of much more complex and processor-
hungry smart home systems. To provide such services, all
smart home systems are built following a basic structure as
provided in Figure 7. It involves three phases as follows:
(i) Collection of information through sensors, cameras,
microphones, and other home appliances
(ii) Storing and processing the collected information
with the help of the main processing unit
(iii) Generating results and delivering services
depending on the processed information
In the rst step, the SHS uses sensors such as motion,
temperature, humidity, ame, gas, and LDR for collecting
atmospheric data alongside other devices such as a camera
and microphone for recording video and audio of home
occupants. Aside from these, the system can also use home
devices connected through IoT to collect information about
their status.
After collecting the information, it is sent to the main
processing unit either wired or wirelessly. e processor
stores and analyzes the data and determines the next action
based on this information. For example, home temperature
and humidity are compared against a predetermined value,
and if the current value exceeds or vice versa, then a noti-
cation is sent to the owner for further action. Similarly, any
intrusion detected on the camera is immediately reported.
In the nal step, the information collected and processed
is used to provide various services such as home comfort,
intrusion alert, elderly care, and appliance control. Users can
control room temperature remotely and have the home
heated or cooled down before arriving. Similarly, by using
ame and gas sensors, any re breakout or gas leakage can
instantly be found, and necessary steps can be taken. With
the help of machine learning, voice commands can be
carried out and gestures can be used to control appliances,
and with the help of articial intelligence, camera feeds can
be used to dierentiate between an intruder and home
occupants.
4. Technological Approaches to SHS
After thoroughly reading and analyzing the research articles
selected for this review, a variety of technological approaches
to SHSs have been detected. A thorough analysis and
evaluation of these technological approaches utilized by the
SHS have been presented in this section.
4.1. Wireless Sensor Network (WSN)-Based SHS. Wireless
sensor network (WSN) can be dened as a network of
spatially scattered sensors that are wirelessly connected and
dedicated to observing various environmental characteris-
tics such as temperature, sound, humidity, force, and
pressure. Apart from environmental aspects, other sensors
detect movement, smoke, gas, ame, and various other
things. All these sensors are part of a WSN that collects the
data from all these sensors wirelessly and then sends the data
to processing. In [22], a WSN of smoke, gas, and temper-
ature sensors is proposed to detect and alarm early re
detection in a smart home. A WSN system for elderly
people’s care is proposed in [23]. e sink node is connected
to the sensors in a WSN system via a wireless connection
[24–26]. A wireless sensor network has been used in [27]
along with Raspberry Pi to design and implement a smart
home environment monitoring structure.
Due to its exibility, low cost, and scalable structure, the
WSN has gained exceptional popularity among SHS de-
velopers. Many of the research articles reviewed in this paper
utilize the WSN system.
4.2. Multiagent System-Based SHS. A multiagent system
(MAS) is a problem-solving approach based on self-
organizing computing that utilizes multiple intelligent
methods to solve problems that are otherwise dicult to
solve for a single system [28, 29]. Due to the eectiveness of
the system, researchers have applied MAS in the
66
45
16
3
0 10203040506070
EXPERIMENTAL DESIGN
THEORETICAL/CONCEPTUAL
REVIEW
CASE STUDY
Number of Papers
Paper Type
Figure 6: Types of research covered by the literature in the reviewed articles.
Journal of Electrical and Computer Engineering 7
development of SHSs. An MAS reduces the total compu-
tational and data transmission time of the total system which
results in reduced energy consumption.
4.3. Image Processing (IP)-Based SHS. Image processing in
SHSs deals with the analysis of data collected from single or
multiple cameras to obtain various services such as gesture
Heterogenous Data
Gas,
Smoke
and Flame
Sensor Water
and
Humidity
Sensor
Pressure
Sensor
Light
Detector
IR Sensor
Camera
Data Storage
Data
Analysis
Data
Processing
Outdoor Alarm
Smart Switch
Light
Sensor
Humidity
Sensor
Vibration Sensor
Water
Detector
Smoke
Detector
Magnetic Sensor
PIR Sensor
Temperature Sensor
Central Controller
Camera
IR Sensor
Gas
Sensor
Home Security
Energy
Management
Activity and
Gesture
Recognition
SHS for Elderly
and Physically
Challenged
Appliance
Control
Remote
Monitoring and
Control
Environment
Monitoring and
Control
Data CollectionData Storage and ProcessingService Delivery
Figure 7: Smart home system.
8Journal of Electrical and Computer Engineering
control of smart homes [30], smart home security systems
based on object detection [31], gesture controlling features
for elderly people [32], and smart home antitheft systems
[29]. Since these image processing systems are dependent on
real-time videos and images from the smart home, image
processing-based SHSs generally have a high rate of data
transmission between the camera and the data processing
section. Image processing has been utilized by several au-
thors because of its wide range of functions, from antitheft
systems to elderly support. However, since cameras are
aected by shadows, distortions, insucient light, and other
factors, the image processing system sometimes fails to
deliver the expected result.
4.4. Internet of ings (IoT)-Based SHS. e Internet of
things (IoT) is an arrangement of interconnected computing
devices, mechanical and digital machines, sensors, micro-
controllers, and other electronic devices that are uniquely
identiable, and these unique devices can communicate
through the Internet with one another without requiring
human or computer interaction. IoT is a relatively new
technology and has gained excessive popularity among re-
searchers [16, 33]. IoT has been used to design and im-
plement smart homes in [34]. In [28, 29], the authors utilized
IoT to design a security system for SHSs. An energy man-
agement system based on IoT is proposed in [35]. An IoT-
based smart home energy management system has been
explored in [36] alongside the usage of sensors to monitor
the occupants of the smart home. IoT is used in [37]to detect
the early stages of dementia with the help of machine
learning. IoT has become one of the key technologies in the
development of SHSs. In an IoT-based SHS, the main
controller, sensors, and computational devices are all in-
ternally connected through the Internet. All these devices
can receive and transfer data automatically without any
human intervention. In an IoT system, the connection be-
tween nodes (sensor node, computational node, etc.) can be
established wired or wirelessly.
4.5. Articial Intelligence (AI)-Based SHS. Articial in-
telligence (AI) is dened as a computing system that is
capable of performing tasks that generally demand human
intelligence, such as visual object detection, speech recog-
nition, and decision-making [38]. AI has been used by re-
searchers frequently due to its wide range of functions. In
[39], the authors developed a smart home system for
emotion detection based on AI. An AI-based image pro-
cessing system has been developed for an antitheft system in
[29]. An IoT elderly care system based on AI is proposed in
[40]. Articial Intelligence is paving the way for more in-
telligent SHSs. Furthermore, developments in this eld can
result in more resilient, interactive, and comfortable smart
homes [41].
4.6. Machine Learning (ML)-Based SHS. Machine learning is
a subsection of articial intelligence that deals with learning
patterns from given data by a machine to make sense of
previously unfamiliar data [42]. It also deals with the pro-
cessing of a large amount of data to recognize images,
patterns, and speech. In a machine learning-based SHS, the
data collected from the smart home are analyzed and used to
predict the status and control of the home equipment [43].
Machine learning is used in [37] to detect the early signs of
dementia among elderly people. Machine learning-based
systems can also monitor the security systems of smart
homes [44].
4.7. Deep Learning (DL)-Based SHS. Deep learning is
a segment of a wider group of machine learning techniques
that are based on articial intelligence. It imitates the human
brain with data processing and feature extraction and
decision-making [45, 46]. As proposed in [28], deep learning
can help with smart home automation and energy reduction.
An object detection system for SHSs with the help of image
processing was developed in [47]. Deep learning has also
been used for healthcare purposes along with IoT [48].
4.8. Neural Network (NN)-Based SHS. A neural network tries
to copy human brain function through a set of algorithms
and extracts traits and fundamental relationships from the
collection of data [49]. A neural network is used in SHSs for
smart decision-making such as information extraction,
image and speech recognition, and text detection [25].
4.9. Fuzzy Logic-Based SHS. Fuzzy logic is a multivalued
logic-based reasoning method that computes data based on
“degrees of truth,” which is quite dierent from Boolean
logic (0 or 1) used by modern computers. Fuzzy logic does
not have any absolute truth or absolute false value [50]. A
smart home for dementia care based on fuzzy logic is
proposed in [51].
4.10. Global System for Mobile (GSM)-Based SHS. GSM is
a standard for second-generation (2 G) digital mobile net-
works. e short message service (SMS) is a very popular
feature of the GSM mobile network. GSM-based SHSs use
this SMS feature to send a warning message to the user’s
phone about various dangers, including smoke, re, and
theft [22, 52]. Also, smart home appliances could be
monitored and controlled by sending an SMS from the user’s
phone as described in [53].
4.11. Bluetooth-Based SHS. Bluetooth is a short-range,
wireless communication method that allows data transfer
between electronic devices such as mobile phones, com-
puters, and peripherals over a short distance. A smart home-
controlled system based on Bluetooth can control home
appliances through an app installed on a user’s phone
[54–56]. A smart home system for blind people based on IoT
and Bluetooth communication is proposed in [57].
4.12. Classication of Reviewed SHSs According to Techno-
logical Approaches. A summary of the dierent approaches
Journal of Electrical and Computer Engineering 9
taken by the authors in their research to develop SHSs is
provided in Table 2.
5. Central Processors/Controllers Used in SHS
A processor or controller can be described as a miniature
computer on a single metal oxide semiconductor-integrated
circuit chip that is designed to carry out a specic operation.
A microcontroller usually consists of one or more central
processing units (CPUs), storage or memory units, and
input/output peripherals. In SHS development, micro-
controllers are used for a wide range of operations, such as
controlling sensors, computing data, executing commands,
and storing information. All the microcontrollers used in the
development of SHSs are evaluated, and an overall summary
is presented in this section.
5.1. Arduino. Arduino refers to a family of open-source
microcontrollers that are famous for their low price, exi-
bility, and easy-to-use interface. Basically, two Arduino
boards are used in SHS-Arduino Uno, which is based on an
ATmega328P microchip, and Arduino Mega, which uses an
ATmega2560 microchip. Arduino microcontrollers are well
equipped to control multiple sensors and devices as dem-
onstrated in [52, 65]. Due to their low power consumption,
Arduino boards are very popular among SHS developers
[24]. Arduino Mega has more input ports than Arduino Uno
and is capable of handling more input data because of its
better microchip [84]. Energy management systems for SHSs
can also be designed and implanted based on Arduino
microcontrollers [35]. All in all, Arduino boards have be-
come a cornerstone in the development of SHSs.
5.2. Raspberry Pi. In simple terms, Raspberry Pi is a small
computer that can receive, store, and compute data and can
control and monitor electronic components such as sensors
and cameras. It is famous for its low cost, modularity, and
open design. Raspberry Pi can be used for face detection and
image processing [66, 67]. It is fully capable of accessing the
Internet and controlling home equipment through IoT
[69, 85] and functions as a virtual assistant [62]. An AI-based
voice recognition system for remote home appliance control
for elderly people has been implanted in [79], which uses
Raspberry Pi as its computing device. Raspberry Pi can also
receive commands from the user via GSM or the Internet
and use them to control home appliances [53]. A low-
costvoice-activated SHS, which can be incorporated with
many essential subsystems and can be personalized to in-
dividual needs, is designed in [86]. e system uses a dual-
mode of interaction where the user has the option to control
the appliance from a graphical user interface (GUI)-based
app or a chat system, where text or audio commands are
used to control the system. Due to its small form factor and
high computational power, Raspberry Pi has gained a lot of
popularity in the eld of SHSs.
5.3. ESP32/ESP8266. ESP32 and ESP8266 are both members
of the ESP family, which is a series of cheap and energy-
ecient microcontrollers with integrated Wi-Fi and Blue-
tooth. Cost-eective, convenient, and comfortable smart
home automation system development is facilitated with the
help of ESP microcontrollers [88]. ESP32 is latest in the
series and is more powerful than the previous version of
ESP8266. ESP microcontrollers have gained popularity
mainly because of the integrated Wi-Fi feature, which helps
make an IoT system [89]. Despite being an old version,
ESP8266 is still used due to its high eciency and multiple
electronic component handling capabilities [26, 136]. A
cost-eective smart home automation system (SHAS) with
ESP8266 in [90] showed that it is easier for the user to
connect a new device to the system without worrying about
conguration. In [91], the ESP8266 board is used alongside
an ATmega16 microcontroller to design and develop an IoT-
based SHAS where the system can wirelessly control mul-
tiple loads and monitor vital environmental data such as
temperature and humidity. An ESP32-based smart home
monitoring and controlling system that utilizes external
LoRa connectivity is proposed in [70], which demonstrates
the versatility of the microcontroller.
5.4. FPGA. Field-programmable gate array (FPGA) is a type
of integrated circuit (IC) board that can be programmed by
the user after being manufactured and hence the name ‘eld
programmable.’ FPGAs are built around a matrix of con-
gurable logic blocks that are connected through pro-
grammable interconnects. Smart home systems based on
FPGA boards have been gaining ground among the research
community due to their exibility and the ability of logic
level programming which gives FPGA boards faster pro-
cessing speed [77]. Due to their easily changeable func-
tionality, FPGA boards could be programmed to perform
various tasks such as controlling sensors and security
monitoring [92].
5.5. PIC Microcontroller. Programmable peripheral in-
terfaces (PICs) are a series of programmable micro-
controllers that can be used to perform a wide range of
operations. PICs are very energy-ecient, cheap, and fast. In
smart home systems, PICs are used for a variety of tasks
including controlling appliances [80], setting up a smart
elderly care system [23], and monitoring home security [81].
PIC microcontrollers are used more frequently in SHSs
because they are very reliable and less prone to be faulty.
5.6. LPC. LPC is a series of ARM core-based32-bit micro-
controllers. ese microcontrollers are superfast, very reli-
able, and cheap. Smart homes based on LPC
microcontrollers are as capable as any other micro-
controllers. ese microcontrollers are suitable for multiple
purposes including home automation [68], appliance con-
trol, security [59], and IoT.
10 Journal of Electrical and Computer Engineering
Table 2: Classication of SHSs according to technological approaches.
Reference Technological approaches
WSN Multiagent Image processing IoT AI Machine learning Deep learning Neural network Fuzzy logic GSM Bluetooth
[22] + + +
[23] + +
[24] + +
[25] + + +
[26] + +
[27] +
[28] + +
[29] + + + + +
[30] + +
[31] + +
[32] + +
[34] +
[58] +
[59] +
[35] +
[37] + +
[39] + +
[40] + + +
[43] + +
[44] + + +
[47] + + +
[48] + + +
[51] +
[52] + +
[53] + + +
[54] +
[55] +
[56] +
[57] + + +
[60] + + +
[61] +
[62] +
[63] +
[64] +
[65] +
[66] + +
[67] + +
[68] + + +
[69] +
[70] + +
[71] +
[72] +
Journal of Electrical and Computer Engineering 11
Table 2: Continued.
Reference Technological approaches
WSN Multiagent Image processing IoT AI Machine learning Deep learning Neural network Fuzzy logic GSM Bluetooth
[73] +
[74] + +
[75] + + + +
[76] + +
[77] + + +
[78] + + +
[79] + + +
[80] +
[81] + +
[82] + +
[83] + +
[84] +
[85] +
[86] +
[87] + + + +
[88] +
[89] + +
[90] +
[91] +
[92] +
[93] + + +
[94] + +
[95] +
[53] + +
[96] +
[97] + + +
[98] +
[99] +
[100] + +
[101] +
[102] +
[103] +
[104] +
[105] + +
[106] + +
[13] +
[107] +
[108] + + +
[109] +
[110] +
[111] + +
[112] +
12 Journal of Electrical and Computer Engineering
Table 2: Continued.
Reference Technological approaches
WSN Multiagent Image processing IoT AI Machine learning Deep learning Neural network Fuzzy logic GSM Bluetooth
[113] +
[114] +
[115] +
[116] + +
[117] +
[118] + +
[119] + +
[120] + + +
[121] + + +
[122] +
[123] +
[124] +
[125] +
[126] + +
[127] + +
[128] + + +
[129] +
[130] +
[131] +
[132] + +
[133] +
[134] +
[135] +
[136] + +
[137] + +
[138] + + +
[139] +
Total 19 2 24 78 8 18 8 6 1 17 14
Journal of Electrical and Computer Engineering 13
5.7. Other Microcontrollers. Apart from the popular
microcontrollers mentioned so far, there are a few more that
a handful of researchers have used in their work. S5PV210 is
a 16/32-bit, programmable, and high-performance mini-
computer that has been used to design an IoT-based SHS
[93]. It uses a Samsung S5PV210 application processor that
gives it unparalleled performance but makes it too expen-
sive. AT89C2051 is an 8-bit, high-performance, energy-
ecient, and programmable microcomputer that can pro-
cess a wide variety of operations. It can control multiple
electronic appliances and send data with the help of addi-
tional networking devices [54]. With the help of its powerful
32-bit ARM processor, LM3S8962 is fully capable of SHS
automation and control as demonstrated in [94]. In [82], the
Xiaomi Mi smart home device is used to control sensors and
appliances. Finally, STM32F103C8T6 is another micro-
controller that has been used in the development of
SHSs [95].
5.8. Classication of Reviewed SHSs According to Micro-
controllers and Microprocessors. A summary of the various
types of microcontrollers used by the authors for developing
SHSs is provided in Table 3.
6. Sensors Utilized
Sensors play a signicant role in the development of SHSs.
e application of multiple types of sensors was identied in
the literature review stage conducted previously. e fol-
lowing section presents the details of the sensors used to
design, develop, and implement SHSs.
6.1. Infrared (IR) Sensor. eoretically, all the objects and life
forms that have a minimum amount of temperature emit IR
radiation. An IR sensor is an electronic device that is capable
of detecting and measuring the IR radiation in its sur-
rounding environment. e main reason for using IR
sensors is motion detection and temperature measurement.
ere are two types of IR sensors available: active infrared
sensors and passive infrared (PIR) sensors. Among these
two, PIR is the most frequently used IR sensor.
A PIR sensor detects the change in electromagnetic
radiation levels in its surrounding environment. It does not
actively emit IR radiation like an active IR sensor. e main
application of PIR is motion detection. When an object
comes in the range of a PIR sensor, it measures the dierence
in IR levels and detects the object. In SHSs, PIR is used for
intruder alert [31, 78, 92], detecting the occupant activity in
an SHS [24, 35, 103], and creating an elderly healthcare
system [51, 79]. However, PIR sensors are usually used inside
of a house because they are aected by environmental
changes such as snow and rain.
6.2. Temperature Sensor. A temperature sensor by sensing
the temperature of its surrounding environment can ensure
comfortable living. Multiple types of temperature sensors
have been used in the development of SHSs, and the most
popular is LM35 [77]. Temperature sensors are used for
measuring room temperatures based on which other ap-
pliances are controlled such as fans, air conditioners, and
heaters [25, 70].
6.3. Humidity Sensor. Humidity sensors detect and measure
the change in the amount of water vapor or moisture in the
air surrounding them. e most popular humidity sensor
used by SHS researchers is DHT11, which can measure
temperature as well. In SHSs, a humidity sensor is generally
used for monitoring room moisture levels [26, 78], home
automation [75], and early warning systems [52].
6.4. Gas Sensor. Gas sensors detect the presence of certain
gases in the air in their range. ey are used for detecting
harmful or dangerous gases such as LPG, propane, methane,
carbon monoxide, I-butane, and alcohol and are particularly
important in the development of SHSs because they give
a warning about harmful gases in the house [24, 34, 57, 93].
6.5. Smoke Sensor. A smoke sensor is an electronic device
that detects smoke in its vicinity and triggers an alarm.
Smoke sensors are used as a precautionary measure of an
early re warning system. In SHSs, a smoke sensor is
generally programmed to trigger an alarm or send a warning
signal to the home occupants via a GSM message or app
[22, 95, 104, 105].
6.6. Ultrasonic Sensor. An ultrasonic sensor emits and re-
ceives ultrasonic sound waves and measures the distance of
a certain object by emitting sound waves and receiving the
reected sound wave from that object which the sensor
transforms into an electronic signal. In SHSs, an ultrasonic
sensor is used for implementing an automatic door system
[78], water level monitoring [104], and basic smart home
automation [24].
6.7. Flame Sensor. A ame sensor is an electronic device
designed to detect the presence of re in its operating range
and respond by triggering an alarm or other warning
mechanism. Any security-centric smart home system usually
employs ame sensors for early detection and warning of
ame in the house [65, 70, 97].
6.8. Light Detection Sensor. A light detection sensor is used
to measure the intensity of light in an area. It is a photo-
electric device that is capable of converting light energy
surrounding it into electrical energy. ere are a few types of
light detection sensors in use, such as photoresistors, pho-
todiodes, and phototransistors. In SHSs, these sensors are
used for automating the lighting system of the house [34, 81].
In one particular system, a light detection sensor is used to
inform the blind occupant of the house whether it is day or
night [57].
14 Journal of Electrical and Computer Engineering
Table 3: Classication of SHSs according to microcontroller and microprocessor usage.
Reference Microcontrollers and microprocessors
Arduino Raspberry Pi ESP32/8266 FPGA PIC LPC Others
[22] +
[23] +
[24] + +
[25] +
[26] +
[27] +
[28] + +
[29] +
[30] +
[31] + +
[32] + +
[34] + +
[58] + + +
[59] +
[35] + +
[37] +
[39] +
[40] +
[43] + +
[44] +
[47] + + +
[48] + +
[51] +
[52] +
[53] + +
[54] +
[55] +
[56] +
[57] +
[60] +
[61] +
[62] +
[63] +
[64] +
[65] +
[66] +
[67] +
[68] +
[69] +
[70] +
[71] +
[72] + +
[73] +
[74] +
[75] +
[76] +
[77] +
[78] +
[79] + +
[80] +
[81] +
[82] +
[83] +
[84] + +
[85] +
[86] +
[87] +
[88] +
[89] + +
Journal of Electrical and Computer Engineering 15
6.9. Pressure Sensor. A pressure sensor is an electronic device
that can measure the pressure of liquid or gas through
a pressure-sensitive element. In an SHS, it could be used for
multiple purposes such as room atmosphere monitoring [26],
measuring the blood pressure of home occupants for health
monitoring [23], or monitoring home activities [51, 82].
Table 3: Continued.
Reference Microcontrollers and microprocessors
Arduino Raspberry Pi ESP32/8266 FPGA PIC LPC Others
[90] +
[91] + +
[92] + +
[93] +
[94] +
[95] +
[53] + +
[96] + +
[97] + + +
[98] + +
[99] + +
[100] + +
[101] + +
[102] +
[103] +
[104] +
[105] +
[106] +
[13] +
[107] +
[108] +
[109] +
[110] +
[111] + +
[112] +
[113] +
[114] +
[115] +
[116] + +
[117] +
[118] +
[119] +
[120] + +
[121] +
[122] +
[123] +
[124] +
[125] +
[126] +
[127] +
[128] +
[129] +
[130] +
[131] +
[132] +
[133] +
[134] +
[135] +
[136] + +
[137] + +
[138] +
[139] +
Total 35 36 25 2 5 2 38
16 Journal of Electrical and Computer Engineering
6.10. Accelerometer. An accelerometer is an electronic tool
that measures acceleration forces accurately. In SHSs, ac-
celerometers are used to detect the acceleration of a person
who is static or dynamic [23], identify intrusion [68], and
detect the mobility and condition of a patient in a smart
home [13, 106].
6.11. Door Sensor. A door sensor is a device that detects
a door opening or closing and noties the user. A pair of
electrical connectors detects the opening or closing by
making or breaking an electrical circuit in the sensor. ese
sensors are being used increasingly in SHSs for detecting
door opening and closing status in elderly care systems
[23, 102] and overall home occupant activity monitoring.
6.12. Gyroscope. A gyroscope or angular velocity sensor is
a device that can measure and maintain the orientation and
angular velocity of an object. In SHSs, it is used for detecting
the orientation of a person [23], along with an accelerom-
eter, and in gesture control systems, it is used for detecting
gestures [107, 108].
6.13. Pulse Sensor. A pulse sensor is a device that is capable
of detecting and monitoring human heart rate continuously.
In smart homes, it is mainly used for elderly health care
[23, 78].
6.14. Fluid/Water Detection Sensor. A uid detection sensor,
sometimes known as a raindrop sensor, is an electronic
device that can detect water via an extended pad that can
sense water on its surface. It is usually used to detect water
leakage and water level monitoring [65, 95, 100].
6.15. Camera. e application of a camera or a network of
cameras is becoming more and more popular in SHS de-
signs. SHS researchers have used the camera with various
computational methods such as image processing, machine
learning, gesture recognition, and articial intelligence for
enhancing smart home security and monitoring
[29, 52, 109], object detection [47], gesture detection [60],
and live feed monitoring [72]. Cameras provide a reliable
method of setting up security for smart home users. e only
drawback to using a camera is its cost of deployment and
maintenance.
6.16.Force-Sensing Resistor. A force-sensing resistor (FSR) is
an electronic device that is capable of measuring the amount
of force, pressure, or mechanical stress applied to it. FSRs use
a type of material that changes its resistance when force is
applied to it. ese are special types of sensors used in SHSs
to detect the home occupant’s activity [103] and set up
special systems for elderly people’s care [110].
6.17. Flex Sensor. A ex sensor is used to measure the
amount of deection or bending of certain objects. e
sensor works by changing the resistance when bent. e ex
sensor is usually used in gesture-controlled systems to detect
gestures [32, 111].
6.18. Other Types of Sensors. Aside from the types of sensors
mentioned previously, a few other types of sensors com-
bining several other sensors, such as bed sensors [71], chair
sensors, and posture sensors, have been demonstrated. ese
sensors cannot be categorized as a single type of sensor as
they often have multiple other sensors such as motion,
accelerometer, and gyroscope sensors inside them.
6.19. Classication of Reviewed SHSs According to Sensors.
A summary of the various types of sensors used by the
authors for developing SHSs is provided in Table 4.
7. Networking Technologies Used in SHS
It is necessary to have a networking setup between the
sensor, processing unit, and user-end device, to process the
data generated by sensors and other devices or to send the
analyzed result of the data to end users. e data generated
from sensors are useless if these data cannot reach the
processing unit, and the processing unit cannot function
properly without the necessary data. e result will be the
user not getting the necessary warning or update about the
SHS [140]. A network system creates a continuous channel
between sensors, processors, and users. Generally, the
network system can be divided into two separate networks:
sensor-processor networks and processor-user networks. In
this section, the networking systems are described.
7.1. Sensor-Processor Network. A network must be estab-
lished between sensors and processors that are capable of
continuous data transmission. is network could be set up
wired or wirelessly, though most of the time a wired con-
nection is preferred. In the case of wireless communication,
there are a few networking standards that SHS systems can
use, such as Wi-Fi, ZigBee [115], LoRa [70], and RF
communication [34].
7.2. Processor-User Network. e data processed by the
processing unit cannot be used by the user if there is no
communication link or network between the processor and
the user. e network between the processor and end-user
can be used for house monitoring, appliance control,
weather control, etc. is network is mostly created wire-
lessly and uses networking technologies such as Wi-Fi, LoRa,
GSM [30], Bluetooth [54], LAN [89], and cellular networks.
8. User Interfaces Used in SHS
In SHSs, a user-end communication interface is set up for
the user to receive important messages from the system and
send commands. is interface could be a smartphone
application or a website. is section presents a description
of the interfaces used in SHSs.
Journal of Electrical and Computer Engineering 17
Table 4: Classication of SHSs according to the sensors used.
Reference
Sensors used in SHSs
PIR Temperature Humidity Gas Smoke Ultrasonic Flame LDR Pressure Accelerometer Door Gyroscope Pulse Fluid
detection Camera FSR Flex Others
[22] + + +
[23] + + + + +
[24] + + + + +
[25] + + +
[26] + + + + +
[27] + + +
[28] + + + +
[29] +
[30] +
[31] + +
[32] +
[34] + + + +
[58] + + + + + +
[59] + +
[35] + +
[37] + + +
[39] + + +
[40] + +
[43] + +
[44] +
[47] +
[48] + +
[51] + + +
[52] + + + +
[53] + + + + + +
[54] + +
[55] + +
[56] + +
[57] + + + +
[60] + +
[61] + +
[62] +
[63] + + +
[64] + + +
[65] + + + + +
[66] + +
[67] + +
[68] + + +
[69] + + +
[70] + + + +
[71] + +
18 Journal of Electrical and Computer Engineering
Table 4: Continued.
Reference
Sensors used in SHSs
PIR Temperature Humidity Gas Smoke Ultrasonic Flame LDR Pressure Accelerometer Door Gyroscope Pulse Fluid
detection Camera FSR Flex Others
[72] + +
[73] +
[74] +
[75] + + +
[76] + + +
[77] + + +
[78] + + + + +
[79] + +
[81] +
[82] + + + + +
[83] + + +
[84] + + +
[85] +
[86] +
[87] +
[88] + +
[89] + +
[90] +
[91] + +
[92] + +
[93] + + + + +
[94] + +
[95] + + + + + + +
[96] + + + +
[97] + + + + +
[98] +
[99] + +
[100] + + + + + +
[101] + +
[102] + +
[103] + + + + +
[104] + + + +
[105] + + +
[106] +
[13] + + +
[107] +
[108] + +
[109] +
[110] +
[111] + + +
[112] +
Journal of Electrical and Computer Engineering 19
Table 4: Continued.
Reference
Sensors used in SHSs
PIR Temperature Humidity Gas Smoke Ultrasonic Flame LDR Pressure Accelerometer Door Gyroscope Pulse Fluid
detection Camera FSR Flex Others
[113] +
[114] + +
[115] +
[116] + +
[117] +
[118] + + + + +
[119] +
[120] + +
[121] + +
[122] + + + + + +
[123] +
[124] + + + +
[125] + +
[126] +
[127] +
[128] + + +
[129] + + +
[130] + + +
[131] +
[132]
[133] +
[134] +
[135] + + + +
[136] + +
[137] + + + + +
[138] + +
[139] + + + + +
Total 52 55 32 16 11 3 7 25 4 5 13 2 2 4 26 4 4 19
20 Journal of Electrical and Computer Engineering
8.1. Web Application-Based SHS. A web application-based
interface provides a graphical user interface (GUI) for
monitoring and controlling SHSs. ese web applications
are mainly based on the Hypertext Transfer Protocol
(HTTP) and Transmission Protocol/Internet Protocol (TCP/
IP). A user can access the sensor reading from the SHS, turn
on/o certain appliances, get a security breach alert, and
check the health condition of the elderly by using these web
applications [95, 113].
8.2. Smartphone Application-Based SHS. Since the in-
troduction of Android and IOS devices, the usage of
smartphone applications has become widespread. As a re-
sult, SHS researchers are opting more and more for
application-based interfaces [116]. Similar to web applica-
tions, smartphone applications also provide the user with
a GUI for interaction, and the user can get a real-time update
about SHSs. Smartphone applications provide all types of
data and information just like web applications [34, 112].
9. Computational Approaches in SHS
In every SHS, one of the most crucial parts is the compu-
tational unit. It can either be a physical unit placed inside an
SHS or a cloud platform employed for this purpose. e next
segment contains a summary of the computational methods
utilized by the paper reviewed in this study.
9.1. Big Data. Big data is the method of systematically an-
alyzing and extracting information from large sets of data
that are otherwise too big and complex to analyze through
traditional data processing methods. A large-scale SHS,
which employs several sensors and appliance control
mechanisms, often generates an enormous amount of data.
e big data computational method oers a system that
enables us to handle such a huge amount of data
[72, 117, 118].
9.2. Cloud Computing. In SHSs, cloud computing provides
access to computer system resources such as data storage
and processing power on demand. is way, instead of
directly purchasing extra storage and processors, the system
can use cloud resources to satisfy its needs [22, 97, 104].
9.3. Fog Computing. Fog computing or fogging is a dispersed
computing architecture that exists between cloud and data-
generating devices. It uses optimized edge devices to process,
store, and communicate with the user end through the
Internet. Fog computing reduces the energy consumption of
SHSs by decreasing the amount of data required to transmit
[72, 74, 117].
10. Services Provided by SHS
is next section mentions some of the services provided by
smart home systems. In recent times, several types of
services have been introduced for SHSs, but this section
discusses the most popular ones.
10.1. Security Systems of SHS. Security is a big concern in any
SHS, be it security from intruders or security against data
theft [141]. Smart homes are gradually employing more and
more robust security systems thanks to the ongoing research
on SHS security [142]. In this section, the security aspect of
SHSs is explored in brief.
e smart home intrusion warning is a system that
detects and alerts smart home users about possible un-
authorized entry into home. Various approaches have been
undertaken by researchers to secure smart homes from
intrusion. A real-time intrusion alert based on image pro-
cessing that can detect human faces and warn the SHS user is
proposed by the author in [29]. e authors in [81] present
a low-cost Bluetooth and smartphone-based security system
that uses voice recognition and an eye scanner to identify the
user. A cloud-based security system that enables the user to
lock all the doors and windows, alerts about intrusion via
SMS, and allows wireless home monitoring through cameras
is discussed in [114]. Alexa or the Amazon voice service and
Raspberry Pi are used to secure doors of a smart home in
[86], where a push notication is sent to the user if the door
is opened without authorization. In [79], a security system
for elderly and physically challenged people has been dis-
cussed, which uses a PIR sensor to detect an object or in-
truder and, with the help of a camera connected to Raspberry
Pi, captures a photo of the intruder. In [119], a robot is used
to monitor the smart home and detect intrusions or ab-
normal events. Equipped with a camera, this robot can
dierentiate between intrusion and private moments such as
nakedness and can avoid monitoring sensitive activities of
home occupants.
e SHS that employs Internet, IoT, remote home
monitoring, and wireless appliance controlling systems uses
wireless communication systems and thus is vulnerable to
data hacking. It is essential to set up a secure network for
a comfortable smart home experience. As the number of
devices connected to IoT-based systems is continuously
growing, a secured network is essential [143]. To terminate
the security threats of an IoT network, the authors in [61]
proposed a smart card-based security system that is based on
the secure addressing and authentication (SCSAA) scheme,
which upgrades the standard IPv6 protocol. An internal
security framework for smart devices has been proposed in
[109], which ensures devices’ security against data leakage,
modication, or false code integration into systems. A
password-protected user interface has been developed in
[59] that requires user authentication to access the moni-
toring interface. In [118], machine learning and big data
have been used to detect anomalies in the network with the
help of a hidden Markov model (HMM) that can sense the
presence of cyber anomalies in the system. An intrusion
detection and mitigation framework (IoT-IDM) structure
has been developed in [120]. is framework continuously
monitors the devices connected to the system in an IoT-
based smart home and looks out for any malicious activity or
Journal of Electrical and Computer Engineering 21
anomalies in the network. Upon detecting any unauthorized
activity, the system blocks the intruder and protects devices.
Smart devices connected to the system can authenticate one
another and create a secure data transmission network. e
system proposed in [121] is able to monitor IoT network
trac and extract information. is extracted information is
then used to detect abnormal behavior in the system. An
SHS security architecture is proposed in [122], which is
ecient, reliable, and accurate and manages the SHS net-
work safely. By using private Ethereum blockchain, the
authors in [123] proposed an SHS network security system.
10.2. Energy Management Systems in SHS. A vital part of the
SHS is energy management, and several studies have been
conducted in this eld. Smart home energy management
systems are designed based on a framework that can
satisfy energy demands and monitor available resources
without the involvement of the user [144]. Researchers
have used methods such as articial neural networks
[145], machine learning, deep learning [28], and articial
intelligence [73] to develop an energy management system
for SHSs. Smart homes that are fully capable of producing
the required amount of energy are one of the main focuses
of researchers, and several breakthroughs have been
achieved [124]. e authors in [146, 147] proposed
a nonintrusive load monitoring (NILM) system for better
energy management. Automatic scheduling of household
appliances and electric vehicles to reduce energy costs has
been proposed in [148]. To decrease energy costs, a new
energy management system with the help of photovoltaic
cells that satises consumer needs without putting too
much pressure on the national grid has been proposed in
[149]. Photovoltaic cells have been a huge part of the
energy-saving scheme in the SHS. AI-based PV pro-
duction systems have been proposed in [150, 151]. A cloud
server-based energy management system has been pro-
posed in [125]. Multiagent energy optimization systems
have been presented in [152]. PV-based smart homes now
play an active role in the national grid. Extra energy
generated through photovoltaic cells in smart homes can
now be traded through an electric grid [153, 154].
10.3. Activity and Gesture Recognition. e process of
detecting and recognizing human body movements such as
handwaves or facial expressions to control and interact with
a computer system is dened as gesture control. It is
a subdivision of image processing and computer vision. In
SHSs, mostly hand gestures are used to control certain
appliances connected to the system. SHS researchers are
trying to develop a more accurate gesture recognition system
that would make home appliance control easier. A computer
vision-based hand gesture recognition system for home
appliance control that does not require the user to wear any
extra wristband or other device is demonstrated in [126]. A
Kinect v2 sensor was used to capture body gestures in [107],
which are used to recognize a set of hand state combinations
to control home appliances. Kinect is a motion-sensing
device that is capable of gesture and voice recognition
[155]. Kinect v2 is again used in [127] for both speech and
gesture recognition systems that are specially tailored for
elderly people of age 65–80 years. In [128], the authors used
Kinect again to register body posture and control home
appliances. Another image processing-based hand gesture
recognition system is proposed by the authors of [30] that
uses MATLAB simulations. A very interesting experiment
was carried out by the author in [138], where a radar device is
used to detect objects and identify gestures. A body sensor-
based gesture recognition system has been explored in [32],
where ex sensors are used with a hand glove to register
gestures and control appliances. A secure blockchain-based
smart home health monitoring system has been proposed in
[63], which uses a sensor device for gesture recognition. A
wearable wrist-mountedmotion-sensing device is used for
gesture recognition in [108]. e device consists of an in-
ertial sensor to detect the hand motion, an Arduino
microcontroller board for processing the data, and an RF
wireless transceiver to communicate and send the data to the
main processor.
10.4. Elderly and Physically Challenged People Care. With the
advancement in smart home technology, it is becoming
more and more capable of providing health care and health
monitoring facilities for occupants, especially for the elderly
and physically challenged people [156, 157]. Researchers are
constantly working to provide a more comfortable and safer
environment for elderly people, which gives them more
safety and independence [18, 158]. e SHS could provide
great physical and health support for elderly people [159].
Several studies have been carried out to investigate the
current situation of elderly care in smart homes and the
perception of elderly people towards the smart home system
[124, 147]. A low-cost smart assistance system for elderly
people has been explored in [131], which generates re-
minders to take medicines and alerts certain people about
signicant events such as re eruptions or intrusions. Apart
from that, it also provides a wireless appliance control
option. e authors in [98] developed a voice and text-based
home appliance control system that uses voice commands or
text messages to turn on and o various appliances. is
method is very useful for elderly and physically challenged
people as it enables them to remotely control their appli-
ances. A hidden Makarov model (HMM) is used in [76] to
detect abnormal activity among smart home occupants,
especially elderly people. e study used only sensor data to
detect abnormalities in their behavior. A patient monitoring
system is discussed in [132], where audio and video data are
collected through a microphone and camera. e collected
data are then processed in the cloud, and depending on the
result, doctors can prescribe or assist the patient through
audio, video, or text messages. e researchers in [133, 139]
designed a two-way telemedicine interaction system that
allows simultaneous communication between the elderly
and a physician. An IoT-based elderly care system has been
proposed in [79] that uses AI for voice recognition and oers
wireless appliance switching as well as home monitoring and
intrusion alert.
22 Journal of Electrical and Computer Engineering
10.5. Smart Home Appliance Control. Home automation
and wireless appliance control are two of the main elds of
research in SHSs. SHSs are gradually adapting home au-
tomation and allow the user better control over their home
[17]. Several types of methods are available for appliance
switching, such as wireless control over a smartphone app
[134] or website [95], voice command [86], and text com-
mand [98]. In general, smartphone apps [70] or websites
[24] are the most popular means of appliance control in
SHSs because of their simplicity and less complicated in-
stallation process. Other methods, such as gesture recog-
nition, where the user can control devices just with some
simple gestures, have also been developed [107, 126]. Re-
cently, the SHS with dual modes of appliance control such as
speech and gesture control is becoming a more popular topic
of research [127]. IoT-based SHS systems, where the user can
monitor the home environment as well as control various
home appliances such as lights, fans, and other switches, are
already in development [137]. For better speech recognition
in appliance control, researchers are working to develop
a better speech recognition algorithm [160–163].
11. Discussion
Reviewing and analyzing all the articles based on the study,
in this section, a thorough discussion is presented on all the
topics mentioned in this study.
11.1. Discussion on SHS Approaches. Figure 8 represents
a graphical illustration of the technological approaches
shown in Table 2. Although the table does not contain the
data of all the papers reviewed in this study, it contains
a substantial amount of information from which we could
conclude the overall trend of SHSs. We can see that 31 SHSs,
or nearly 60%, used multiple approaches instead of
depending on a single method. 20 articles, or about 40%,
used only one method to develop an SHS. It can also be seen
that IoT-based SHSs are by far the most popular choice
among researchers, which is mainly due to the low cost.
Other methods, such as WSN, GSM, and Bluetooth, are also
fairly popular. Machine learning, deep learning, and neural
network-based SHSs are also gaining ground among
researchers.
11.2. Discussion on Microcontrollers Used in SHS. After
a thorough investigation, we have identied the most
popular microcontrollers used in the development of SHSs.
e data shown in Table 3 can be represented in Figure 9,
which shows the trend of microcontrollers in SHSs. Un-
doubtedly, Arduino and Raspberry PI are the most used
microcontrollers. Arduino is cheap, versatile, and reliable,
while Raspberry Pi has better computational power than
most other microcontrollers. e ESP32 and ESP8266
boards are popular due to their built-inWi-Fi modules,
which help the SHS connect to the Internet. Other micro-
controllers such as FPGA, PIC, and LPC are also used. Some
researchers have also used not-so-familiar components such
as S5PV210 and LM3S8962. Overall, 33 papers, or 61%, used
a single microcontroller method to develop SHSs, while 21
papers, or about 39%, opted for multiple microcontrollers.
11.3. Discussion on Sensors Used in SHS. e sensor usage
data shown in Table 4 are graphically represented in Fig-
ure 10. We can see that the temperature sensor is the most
used sensor in SHSs. ey are often used alongside other
sensors such as ame or gas sensors to detect any unusual
dierence in temperature, which could mean that a potential
re breaks out. e PIR sensor is the second most used
sensor due to its popular application as intrusion detection.
It is considered a cheap substitute for a camera as it can
detect human presence up to a certain level and warn about
any possible intrusion. e humidity sensor keeps an eye on
the humidity level of home and helps maintain a comfortable
humidity level. Gas, smoke, and ame sensors are very useful
and are used as a safety system to detect gas leakage, smoke,
or re breakout. e camera is mainly used for security
purposes, image processing, and gesture recognition. LDR
sensors help automate the lights in the house, which saves
energy. Other sensors, such as doors, accelerometers, and
pressure, have their own specic usage and are used
accordingly.
11.4. Discussion on Networking Technologies and User In-
terfaces of SHS. After reviewing all the studies on SHSs, we
can say that most of the development routes choose both
wired and wireless communication media to connect sensors
and other devices to the main controller, while mainly
wireless connectivity is preferred in the connection between
the smart home and the user. Even though wireless con-
nectivity between the sensor and processor is a trend, it is
comparatively more expensive than a wired connection since
another device is required for the majority of micropro-
cessors to wirelessly receive and send data.
In the case of user interfaces, both web application-based
and smartphone application-based interfaces are popular.
ough the latter might be preferred since web-based in-
terfaces need to remember the web address or user cre-
dentials to log into the system, smartphone-based
applications are not that complex. e extra steps required in
web interfaces to access the system might be considered
unnecessary annoyance by the user, while smartphone-
based interfaces are specically designed to reduce the
amount of complexity for the user.
11.5. Discussion on Services Provided by SHS. Recent de-
velopments in SHSs have introduced dierent types of
services in SHSs that make smart homes more secure,
comfortable, and user-friendly. High emphasis has been
given to networks and home security, which makes home
less prone to security threats. Systems to neutralize both
physical and network threats have been developed over the
years. Energy management has been another major sector of
development that helps smart homes be energy ecient and
environmentally friendly. With the help of image processing,
machine learning, and deep learning, gesture recognition
Journal of Electrical and Computer Engineering 23
has become more accurate and ecient, which allows users
to control appliances with mere gestures. For elderly and
physically challenged people, researchers have developed
specialized smart homes that make the lives of elderly people
easier, more comfortable, and more attentive to medical
needs. Moreover, nally, remote appliance control has
19
2
24
78
8
18
8
6
1
17
14
54
57
0 1020304050607080
WSN
MULTI-AGENT
IMAGE PROCESSING
IOT
AI
MACHINE LEARNING
DEEP LEARNING
NEURAL NETWORK
FUZZY LOGIC
GSM
BLUETOOTH
SINGLE APPROACH
MULTIPLE APPROACH
Frequency of Utilization
Technological Approaches
Figure 8: Comparison of technological approaches of SHS.
38
2
5
2
25
36
35
82
29
020406080
OTHER
LPC
PIC
FPGA
ESP32/8266
RASPBERRY PI
ARDUINO
SINGLE MICROCONTROLLER
MULTIPLE MICROCONTROLLER
Frequency of Utilization
Processor/Controller Type
Figure 9: Comparison of microcontroller usage in SHSs.
52 55
32
16 11 3 7
25
4 5 13 2 2 4
26
44
19
Frequency
Sensor Type
PIR
TEMPERATURE
HUMIDITY
GAS
SMOKE
ULTRASONIC
FLAME
LDR
PRESSURE
ACCELEROMETER
DOOR
GYROSCOPE
PULSE
FLUID DETECTION
CAMERA
FSR
FLEX
OTHER
Figure 10: Frequency of dierent sensor usage in SHS.
24 Journal of Electrical and Computer Engineering
added a new dimension to the comfort level of the smart
home user, which allows remote access and control of home
appliances.
12. Conclusion
e development process of SHSs has been going on for
decades, and breakthroughs have been achieved by re-
searchers in this eld. In recent years, due to population
blasts and rapid industrialization, the standard of living has
been decreasing rapidly. Smart homes provide a secure,
comfortable, and ecient way of living. In this study, we
have presented a thorough analysis of the recent develop-
ment of SHSs. is systematic literature review sheds light
on the various technological approaches taken by re-
searchers in the development of SHSs as well as the types of
microcontrollers and sensors used. We have identied 11
major technological approaches, 6 most popular micro-
controllers, and 17 dierent types of sensors used by re-
searchers in their work and provided an in-depth
comparison among them to ascertain the popularity and
trend in research. Aside from these, a detailed analysis and
review of the networking technologies adopted, user in-
terfaces provided, computational methods utilized, security
systems established, and several services such as energy
management, gesture recognition, elderly care systems, and
appliance control mechanisms have been presented in this
study. Moreover, a detailed comparison of the data collected
from the various articles has been provided in the discussion
part, where it can be seen that multiple approach-based SHSs
are becoming more popular due to their added functionality
from the utilization of multiple approaches. For that same
reason, multiple microcontroller-based SHSs are also
coming in trend. An IoT-based SHS is becoming a dominant
competitor. For the user interface, smartphone application-
based interfaces will play a key role in the future due to their
ease of access and better functionality. Because the network
and physical vulnerability will remain a major threat in the
SHS, more and more emphasis is being given to networks
and physical security, and this research for a better secured
SHS can be expected to continue.
Conflicts of Interest
e authors declare that they have no conicts of interest.
References
[1] M. Hasan, M. H. Anik, and S. Islam, “Microcontroller Based
Smart Home System with Enhanced Appliance Switching
Capacity,” in Proceedings of the 2018 Fifth HCT Information
Technology Trends (ITT), pp. 364–367, Dubai, UAE, No-
vember 2018.
[2] M. Hasan, P. Biswas, M. T. I. Bilash, and M. A. Z. Dipto,
“Smart Home Systems: Overview And Comparative Anal-
ysis,” in Proceedings of the 2018 Fourth International Con-
ference on Research in Computational Intelligence and
Communication Networks (ICRCICN), pp. 264–268, Kolkata,
India, November 2018.
[3] D. Debnath, A. H. Siddique, M. Hasan et al., “Smart Elec-
trication of Rural Bangladesh rough Smart Grid,” Sus-
tainable Communication Networks and Application, vol. 55,
2020.
[4] M. Hasan, M. Hossain Anik, S. Chowdhury, S. Alam
Chowdhury, T. Islam Bilash, and S. Islam, “Low-Cost Ap-
pliance Switching Circuit For Discarding Technical Issues Of
Microcontroller Controlled Smart Home,” International
Journal of Sensors and Sensor Networks, vol. 7, no. 2,
pp. 16–22, 2019.
[5] M. Hasan, M. A. Z. Dipto, M. S. Islam, A. Sorwar, and
M. S. Alam, “A Smart Semi-Automated Multifarious Sur-
veillance Bot For Outdoor Security Using ermal Image
Processing,” Advances in Networks, vol. 7, no. 2, pp. 21–28,
2019.
[6] K. Nimmy, S. Sankaran, K. Achuthan, and P. Calyam,
“Lightweight and privacy-preserving remote user authenti-
cation for smart homes,” IEEE Access, vol. 10, pp. 176–190,
2022.
[7] World Economic Forum, Digital Transformation Initiative,
World Economic Forum, Cologny, Switzerland, 2018.
[8] N. M. Allifah and I. A. Zualkernan, “Ranking security of IoT-
based smart home consumer devices,” IEEE Access, vol. 10,
pp. 18352–18369, 2022.
[9] I. Zenginis, J. Vardakas, N. E. Koltsaklis, and C. Verikoukis,
“Smart home’s energy management through a clustering-
based reinforcement learning approach,” IEEE Internet of
ings Journal, vol. 9, no. 17, pp. 16363–16371, 2022.
[10] F. Shahriyar, M. Islam, A. Chakraborty, M. Hasan,
H. U. Zaman, and A. H. Siddique, “Fault and system analysis
model of voltage source control based HVDC transmission
system,” in Proceedings of the 2021 12th International
Conference on Computing Communication and Networking
Technologies (ICCCNT), pp. 1–6, Kharagpur, India, July 2021.
[11] I. V. Paputungan, M. R. Al Fitri, and U. Y. Oktiawati,
“Motion and Movement Detection for DIY Home Security
System,” in Proceedings of the 2019 IEEE Conference on
Sustainable Utilization and Development in Engineering and
Technologies (CSUDET), pp. 122–125, Penang, Malaysia,
November 2019.
[12] M. N. Hassan, M. R. Islam, F. Faisal, F. H. Semantha,
A. H. Siddique, and M. Hasan, “An IoT based environment
monitoring system,” in Proceedings of the 2020 3rd In-
ternational Conference on Intelligent Sustainable Systems
(ICISS), pp. 1119–1124, oothukudi, India, December 2020.
[13] P. Mtshali and F. Khubisa, “A smart home appliance control
system for physically disabled people,” in Proceedings of the
2019 Conference on Information Communications Technology
and Society (ICTAS), pp. 1–5, Durban, South Africa, March
2019.
[14] T. Vaiyapuri, E. L. Lydia, M. Y. Sikkandar, V. G. D´
ıaz,
I. V. Pustokhina, and D. A. Pustokhin, “Internet of things
and deep learning enabled elderly fall detection model for
smart homecare,” IEEE Access, vol. 9, pp. 113879–113888,
2021.
[15] K. Viard, M. P. Fanti, G. Faraut, and J.-J. Lesage, “Human
activity discovery and recognition using probabilistic nite-
state automata,” IEEE Transactions on Automation Science
and Engineering, vol. 17, no. 4, pp. 2085–2096, 2020.
[16] M. Alaa, A. A. Zaidan, B. B. Zaidan, M. Talal, and
M. L. M. Kiah, “A review of smart home applications based
on Internet of ings,” Journal of Network and Computer
Applications, vol. 97, pp. 48–65, 2017.
Journal of Electrical and Computer Engineering 25
[17] D. N. Mekuria, P. Sernani, N. Falcionelli, and A. F. Dragoni,
“Smart home reasoning systems: a systematic literature re-
view,” Journal of Ambient Intelligence and Humanized
Computing, vol. 12, no. 4, pp. 4485–4502, 2021.
[18] A. H. Siddique, S. Tasnim, F. Shahriyar, M. Hasan, and
K. Rashid, “Renewable energy sector in Bangladesh: the
current scenario, challenges and the role of IoT in building
a smart distribution grid,” Energies, vol. 14, no. 16, p. 5083,
2021.
[19] P. K. Ghosh, A. Chakraborty, M. Hasan, K. Rashid, and
A. H. Siddique, “Blockchain application in healthcare sys-
tems: a review,” Systems, vol. 11, no. 1, p. 38, 2023.
[20] A. Fahim, M. A. Chowdhury, and M. Hasan, “Smart Parking
Systems: Comprehensive Review Based on Various Aspects,”
Heliyon, vol. 7, no. 5, 2021.
[21] U. B. Joy, S. Chakraborty, S. Islam, H. U. Zaman, and
M. Hasan, “Quantum-dot cellular automata-based full adder
design: comprehensive review and performance compari-
son,” Advances in Materials Science and Engineering,
vol. 2023, Article ID 6784413, 13 pages, 2023.
[22] F. Saeed, A. Paul, A. Rehman, W. H. Hong, and H. Seo, “IoT-
Based intelligent modeling of smart home environment for
re prevention and safety,” Journal of Sensor and Actuator
Networks, vol. 7, no. 1, p. 11, 2018.
[23] R. Gnanavel, P. Anjana, K. S. Nappinnai, and N. Pavithra
Sahari, “Smart home system using a Wireless Sensor Net-
work for elderly care,” in Proceedings of the 2016 2nd In-
ternational Conference on Science Technology Engineering
and Management. ICONSTEM, pp. 51–55, Navi Mumbai,
India, January 2016.
[24] S. Kalaivanan and S. Manoharan, “Monitoring and con-
trolling of smart homes using IoT and low power wireless
technology,” Indian Journal of Science and Technology, vol. 9,
no. 31, 2016.
[25] G. P R Filho, L. A Villas, H. Freitas, A. Valejo, D. L Guidoni,
and J. Ueyama, “ResiDI: towards a smarter smart home
system for decision-making using wireless sensors and ac-
tuators,” Computer Networks, vol. 135, pp. 54–69, 2018.
[26] S. Nagendram, P. Kanakaraja, M. S. R. KiranNag, and
K. Akhil, “Design and implementation of low-cost smart
home system with sensor multiplexing,” SN Computer Sci-
ence, vol. 2, no. 3, pp. 199–9, 2021.
[27] L. Zhong, T. Lv, C. Li, and Z. Wang, “Design of smart home
system based on raspberry Pi,” Advances in Intelligent Sys-
tems and Computing, vol. 1031, pp. 649–657, 2020.
[28] D. Popa, F. Pop, C. Serbanescu, and A. Castiglione, “Deep
learning model for home automation and energy reduction
in a smart home environment platform,” Neural Computing
and Applications, vol. 31, no. 5, pp. 1317–1337, 2019.
[29] S. Pandya, H. Ghayvat, K. Kotecha et al., “Smart home anti-
theft system: a novel approach for near real-time monitoring
and smart home security for wellness protocol,” Applied
System Innovation, vol. 1, no. 4, pp. 42–22, 2018.
[30] P. N. Arathi, S. Arthika, S. Ponmithra, K. Srinivasan, and
V. Rukkumani, “Gesture based home automation system,” in
Proceedings of the 2017 International Conference on Nextgen
Electronic Technologies Silicon to Software ICNETS2,
pp. 198–201, Chennai, India, March 2017.
[31] N. Surantha and W. R. Wicaksono, “Design of smart home
security system using object recognition and PIR sensor,”
Procedia Computer Science, vol. 135, pp. 465–472, 2018.
[32] S. Kshirsagar, S. Sachdev, N. Singh, A. Tiwari, and S. Sahu,
“IoT enabled gesture-controlled home automation for dis-
abled and elderly,” in Proceedings of the 4th International
Conference on Computing Methodologies and Communica-
tion ICCMC, pp. 821–826, IEEE, Erode, India, March 2020.
[33] R. Liu and Y. Ge, “Smart home system design based on
Internet of ings,” in Proceedings of the 2017 12th In-
ternational Conference on Computer Science and Education
(ICCSE), pp. 444–448, Harbin, China, July 2017.
[34] V. Govindraj, M. Sathiyanarayanan, and B. Abubakar,
“Customary homes to smart homes using Internet of ings
(IoT) and mobile application,” in Proceedings of the 2017
International Conference on Smart Technologies for Smart
Nation (SmartTechCon), pp. 1059–1063, Bengaluru, India,
August 2017.
[35] B. Chouaib, D. Lakhdar, and Z. Lokmane, “Smart home
Energy Management System Architecture Using IoT,” in
Proceedings of the ACM International Conference Proceeding
Series, Cairo Egypt, March 2019.
[36] M. Radja and A. W. R. Emanuel, “A review: design of smart
home electrical management system based on IoT,” in
Proceedings of the 2019 International Conference on In-
formation and Communications Technology (ICOIACT),
pp. 910–915, Yogyakarta, Indonesia, July 2019.
[37] F. Ahamed, S. Shahrestani, and H. Cheung, “Internet of
things and machine learning for healthy ageing: identifying
the early signs of dementia,” Sensors, vol. 20, no. 21, p. 6031,
2020.
[38] T. Mehrabi, A. S. Fung, and K. Raahemifar, “Optimization of
home automation systems based on human motion and
behaviour,” in Proceedings of the 2014 IEEE 27th Canadian
Conference on Electrical and Computer Engineering
(CCECE), pp. 1–5, Toronto, ON, Canada, May 2014.
[39] M. Chen, J. Yang, X. Zhu, X. Wang, M. Liu, and J. Song,
“Smart home 2.0: innovative smart home system powered by
botanical IoT and emotion detection,” Mobile Networks and
Applications, vol. 22, no. 6, pp. 1159–1169, 2017.
[40] E. Borelli, G. Paolini, F. Antoniazzi et al., “HABITAT: an IoT
solution for independent elderly,” Sensors, vol. 19, no. 5,
p. 1258, 2019.
[41] S. Sepasgozar, R. Karimi, L. Farahzadi et al., “A systematic
content review of articial intelligence and the internet of
things applications in smart home,” Applied Sciences, vol. 10,
no. 9, p. 3074, 2020.
[42] M. Schuld, I. Sinayskiy, F. Petruccione, S. Africa, and
S. Africa, “An Introduction to Quantum Machine Learning,”
2014, https://arxiv.org/abs/1409.3097.
[43] L. Filipe, R. S. Peres, and R. M. Tavares, “Voice-activated
smart home controller using machine learning,” IEEE Access,
vol. 9, pp. 66852–66863, 2021.
[44] E. D. Alalade, “Intrusion Detection System in Smart Home
Network Using Articial Immune System and Extreme
Learning Machine Hybrid Approach,” in Proceedings of the
2020 IEEE 6th World Forum on Internet of ings (WF-IoT),
pp. 1-2, New Orleans, LA, USA, June 2020.
[45] M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar,
N. Seliya, R. Wald, and E. Muharemagic, “Deep learning
applications and challenges in big data analytics,” Journal of
Big Data, vol. 2, no. 1, p. 1, 2015.
[46] M. Veres and M. Moussa, “Deep learning for intelligent
transportation systems: a survey of emerging trends,” IEEE
Transactions on Intelligent Transportation Systems, vol. 21,
no. 8, pp. 3152–3168, 2020.
[47] F. Mehmood, I. Ullah, S. Ahmad, and D. H. Kim, “Object
detection mechanism based on deep learning algorithm
using embedded IoT devices for smart home appliances
26 Journal of Electrical and Computer Engineering
control in CoT,” Journal of Ambient Intelligence and Hu-
manized Computing, vol. 10, no. 1, 2019.
[48] B. Guo, Y. Ma, J. Yang, and Z. Wang, “Smart healthcare
system based on cloud-internet of things and deep learning,”
Journal of Healthcare Engineering, vol. 2021, Article ID
4109102, 10 pages, 2021.
[49] P. Kumar, A. Braeken, A. Gurtov, J. Iinatti, and P. H. Ha,
“Anonymous secure framework in connected smart home
environments,” IEEE Transactions on Information Forensics
and Security, vol. 12, no. 4, pp. 968–979, 2017.
[50] J. Han, C. Choi, W. Park, I. Lee, and S. Kim, “Smart home
energy management system including renewable energy
based on ZigBee and PLC,” IEEE Transactions on Consumer
Electronics, vol. 60, no. 2, pp. 198–202, 2014.
[51] M. Amiribesheli and H. Bouchachia, “A tailored smart home
for dementia care,” Journal of Ambient Intelligence and
Humanized Computing, vol. 9, no. 6, pp. 1755–1782, 2018.
[52] Q. I. Sarhan, “Arduino Based Smart Home Warning Sys-
tem,” in Proceedings of the 2020 IEEE 6th International
Conference on Control Science and Systems Engineering
(ICCSSE), pp. 201–206, Beijing, China, July 2020.
[53] J. Peng, H. Ye, Q. He, Y. Qin, Z. Wan, and J. Lu, “Design of
smart home service robot based on ROS,” Mobile In-
formation Systems, vol. 2021, Article ID 5511546, 14 pages,
2021.
[54] M. S. Gitakarma and T. K. Priyambodo, “A real-time smart
home system using android Bluetooth control device
module,” in Proceedings of the 2019 International Symposium
on Electronics and Smart Devices (ISESD), pp. 1–7, Badung,
Indonesia, October 2019.
[55] P. U. Okorie, A. Abdu Ibraim, and D. Auwal, “Design and
Implementation of an Arduino Based Smart Home,” in
Proceedings of the 2020 International Congress on Human-
Computer Interaction, Optimization and Robotic Applica-
tions (HORA), pp. 1–6, Ankara, Turkey, June 2020.
[56] S. Naseera, A. Sachan, and G. K. Rajini, “Design of smart
home using internet of things,” Articial Intelligence and
Evolutionary Computations in Engineering Systems, Springer
Nature Singapore, Berlin, Germany, 2018.
[57] M. W. Rahman, R. Islam, M. M. Hasan, S. Mia, and
M. M. Rahman, “IoT based smart assistant for blind person
and smart home using the Bengali language,” SN Computer
Science, vol. 1, no. 5, pp. 300–313, 2020.
[58] R. A. Sowah, D. E. Boahene, D. C. Owoh et al., “Design of
a secure wireless home automation system with an open
home automation bus (OpenHAB 2) framework,” Journal of
Sensors, vol. 2020, Article ID 8868602, 22 pages, 2020.
[59] C. Sisavath and L. Yu, “Design and implementation of se-
curity system for smart home based on IOT technology,”
Procedia Computer Science, vol. 183, pp. 4–13, 2021.
[60] M. C. Su, J. H. Chen, A. M. Arifai, S. Y. Tsai, and H. H. Wei,
“Smart living: an interactive control system for household
appliances,” IEEE Access, vol. 9, pp. 14897–14904, 2021.
[61] P. Kumar and L. Chouhan, “A secure authentication scheme
for IoT application in smart home,” Peer-to-Peer Networking
and Applications, vol. 14, no. 1, pp. 420–438, 2021.
[62] S. Khattar, A. Sachdeva, R. Kumar, and R. Gupta, “Smart
home with virtual assistant using raspberry pi,” in Pro-
ceedings of the 2019 9th International Conference on Cloud
Computing, Data Science and Engineering (Conuence),
pp. 576–579, Noida, India, January 2019.
[63] M. A. Rahman, K. Abualsaud, S. Barnes, M. Rashid, and
S. M. Abdullah, “A natural user interface and blockchain-
basedin-home smart health monitoring system,” in
Proceedings of the 2020 IEEE International Conference on
Informatics, IoT, and Enabling Technologies (ICIoT),
pp. 262–266, Doha, Qatar, February 2020.
[64] H. Albataineh, M. Nijim, and D. Bollampall, “e Design of
a Novel Smart Home Control System Using Smart Grid
Based on Edge and Cloud Computing,” in Proceedings of the
2020 IEEE 8th International Conference on Smart Energy
Grid Engineering (SEGE), pp. 88–91, Oshawa, ON, Canada,
August 2020.
[65] J. M. Ibrahim, A. Karami, and F. Jafari, “A Secure Smart
home Using Internet-Of-ings,” in Proceedings of the ACM
International Conference Proceeding Series, pp. 69–74, Bar-
celona Spain, October 2017.
[66] S. Dash and P. Choudekar, “IoT-based smart home sur-
veillance system,” Advances in Intelligent Systems and
Computing, vol. 1354, 2021.
[67] P. A. Teja, A. A. F. Joe, and V. Kalist, “Home security system
using raspberry PI with IOT,” in Proceedings of the 2021
internationa conference on advance computing and in-
novative technologies in engineering, ICACITE, pp. 450–453,
Greater Noida, India, March 2021.
[68] A. Ilayaraja and V. Venkatesh, “A multilevel remote su-
pervisory and security network system for smart home,”
Indian Journal of Science and Technology, vol. 9, no. 48, 2016.
[69] A. Biswas, D. Biswas, S. S. Chauhan, and A. Borwankar,
“Smart home equipment control system with raspberry pi
and yocto,” in Proceedings of the 2020 Fourth World Con-
ference on Smart Trends in Systems, Security and Sustain-
ability (WorldS4), pp. 553–558, London, UK, July 2020.
[70] A. Nur-A-Alam, M. Ahsan, M. A. Based, J. Haider, and
E. M. G. Rodrigues, “Smart monitoring and controlling of
appliances using lora based iot system,” Design, vol. 5, no. 1,
p. 17, 2021.
[71] M. Bassoli, V. Bianchi, and I. Munari, “A plug and play IoT
Wi-Fi smart home system for human monitoring,” Elec-
tronics, vol. 7, no. 9, p. 200, 2018.
[72] S. S. Gill, P. Garraghan, and R. Buyya, “ROUTER: fog en-
abled cloud based intelligent resource management ap-
proach for smart home IoT devices,” Journal of Systems and
Software, vol. 154, pp. 125–138, 2019.
[73] A. U. Rehman, S. R. Tito, D. Ahmed, P. Nieuwoudt, T. T. Lie,
and B. Vall`
es, “An articial intelligence-driven smart home
towards energy eciency: an overview and conceptual
model,” in Proceedings of the 2020 FORTEI-international
Conference on Electrical Engineering (FORTEI-ICEE),
pp. 47–52, Bandung, Indonesia, June 2020.
[74] Y. Y. Chen, M. H. Chen, C. M. Chang, F. S. Chang, and
Y. H. Lin, “A smart home energy management system using
two-stagenon-intrusive appliance load monitoring over fog-
cloud analytics based on tridium’s niagara framework for
residential demand-side management,” Sensors, vol. 21,
no. 8, p. 2883, 2021.
[75] J. Jaihar, N. Lingayat, P. S. Vijaybhai, G. Venkatesh, and
K. P. Upla, “Smart home automation using machine learning
algorithms,” in Proceedings of the 2020 International Con-
ference for Emerging Technology (INCET), pp. 1–4, Belgaum,
India, June 2020.
[76] D. Shin, S. Lee, and D. Shin, “Sensor-based abnormal be-
havior detection using autoencoder,” in Proceedings of the
ACM International Conference Proceeding Series, pp. 111–
117, Hanoi Ha Long Bay Viet Nam, December 2019.
[77] R. Chhabra, M. K. Khurana, and A. Prakash, “Imple-
mentation of smart home automation system on fpga board
Journal of Electrical and Computer Engineering 27
using iot,” Advamces in Intelligent Systems and Computing,
vol. 624, pp. 805–810, 2018.
[78] S. K. Nitika, V. Kumar, and R. K. Behera, “Solar Powered
Smart Home Design with IoT,” in Proceedings of the 2020
IEEE-HYDCON, pp. 1–8, Hyderabad, India, September 2020.
[79] S. K. Sooraj, E. Sundaravel, B. Shreesh, and K. Sireesha, “IoT
smart home assistant for physically challenged and elderly
people,” in Proceedings of the 2020 International Conference
on Smart Electronics and Communication (ICOSEC),
pp. 809–814, Trichy, India, September 2020.
[80] R. A. Ramlee, M. A. Othman, M. H. Leong, M. M. Ismail, and
S. S. S. Ranjit, “Smart home system using android applica-
tion,” in Proceedings of the 2013 International Conference of
Information and Communication Technology (ICoICT),
pp. 277–280, Bandung, Indonesia, March 2013.
[81] A. Singh, D. Gupta, and N. Mittal, “Enhancing home security
systems using IOT,” in Proceedings of the 2019 3rd In-
ternational Conference on Electronics, Communication and
Aerospace Technology (ICECA), pp. 133–137, Coimbatore,
India, October 2019.
[82] J. Tewell, D. O’Sullivan, N. Maiden, J. Lockerbie, and
S. Stumpf, “Monitoring meaningful activities using small
low-cost devices in a smart home,” Personal and Ubiquitous
Computing, vol. 23, no. 2, pp. 339–357, 2019.
[83] P. Franco, J. M. Mart´
ınez, Y.-C. Kim, and M. A. Ahmed, “A
framework for IoT based appliance recognition in smart
homes,” IEEE Access, vol. 9, pp. 133940–133960, 2021.
[84] W. A. Jabbar, M. H. Alsibai, N. S. S. Amran, and
S. K. Mahayadin, “Design and implementation of IoT-based
automation system for smart home,” in Proceedings of the
2018 International Symposium on Networks, Computers and
Communications (ISNCC), pp. 1–6, Rome, Italy, June 2018.
[85] T. Jenkins, “Cohousing iot: technology design for life in
community,” Multimodal Technologies and Interaction,
vol. 5, no. 3, p. 14, 2021.
[86] C. Z. Yue and S. Ping, “Voice activated smart home design
and implementation,” in Proceedings of the 2017 2nd In-
ternational Conference on Frontiers of Sensors Technologies
(ICFST), pp. 489–492, Shenzhen, China, April 2017.
[87] P. N. Huu, H. N. i u, and Q. T. Minh, “Proposing
a recognition system of gestures using MobilenetV2 com-
bining single shot detector network for smart-home appli-
cations,” Journal of Electrical and Computer Engineering,
vol. 2021, Article ID 6610461, 18 pages, 2021.
[88] S. M. Shaheed, M. S. B. Ilyas, J. A. Sheikh, and J. Ahamed,
“Eective smart home system based on exible cost in
Pakistan,” in Proceedings of the 2017 Fourth HCT In-
formation Technology Trends (ITT), pp. 35–38, Al Ain,
United Arab Emirates, November 2017.
[89] C. Stolojescu-Crisan, C. Crisan, and B. P. Butunoi, “An iot-
based smart home automation system,” Sensors, vol. 21,
no. 11, pp. 3784–3823, 2021.
[90] D. Peng and C. Peng, “A design and implement for simple
smart home system for consumers,” in Proceedings of the
2016 Chinese Control and Decision Conference (CCDC),
pp. 4690–4694, Yinchuan, China, May 2016.
[91] K. K. Rout, S. Mallick, and S. Mishra, “Design and imple-
mentation of an internet of things based prototype for smart
home automation system,” in Proceedings of the 2018 In-
ternational Conference on Recent Innovations in Electrical,
Electronics and Communication Engineering (ICRIEECE),
pp. 67–72, Bhubaneswar, India, July 2018.
[92] S. B. Saleh, S. B. Mazlan, N. I. B. Hamzah et al., “Smart home
security access system using eld programmable gate arrays,”
Indonesian Journal of Electrical Engineering and Computer
Science, vol. 11, no. 1, pp. 152–160, 2018.
[93] X. Mao, K. Li, Z. Zhang, and J. Liang, “Design and imple-
mentation of a new smart home control system based on
internet of things,” in Proceedings of the 2017 International
Smart Cities Conference (ISC2), pp. 1–5, Wuxi, China,
September 2017.
[94] X. Yi, M. Zhou, and J. Liu, “Design of Smart home Control
System by Internet of ings Based on ZigBee,” in Pro-
ceedings of the 2016 IEEE 11th Conference on Industrial
Electronics and Applications (ICIEA), pp. 128–133, Hefei,
China, June 2016.
[95] Y. Ji, Y. Yang, and Z. Huo, “Design of Security protection
System for Smart home Based on Web,” in Proceedings of the
ACM International Conference Proceeding Series, pp. 1–6,
Chennai, India, Feburary 2018.
[96] S. Imran, S. Ahmad, and D. H. Kim, “Design and imple-
mentation of thermal comfort system based on tasks allo-
cation mechanism in smart homes,” Sustainability, vol. 11,
pp. 5849–5920, 2019.
[97] B. Ss Tejesh and S. Neeraja, “A smart home automation
system using IoT and open source hardware,” International
Journal of Engineering and Technology, vol. 7, no. 2-7,
pp. 428–432, 2018.
[98] S. Uma, R. Eswari, R. Bhuvanya, and G. S. Kumar, “IoT based
voice/text controlled home appliances,” Procedia Computer
Science, vol. 165, pp. 232–238, 2019.
[99] M. N. Kadima and F. Jafari, “A Customized Design of Smart
home Using Internet-Of-ings,” in Proceedings of the ACM
International Conference Proceeding Series, pp. 81–86, Bar-
celona Spain, October 2017.
[100] R. Sokullu, M. A. Akkas¸, and E. Demir, “IoTsupported smart
home for the elderly,” Internet of ings, vol. 11, Article ID
100239, 2020.
[101] G. M. Madhu and C. Vyjayanthi, “Implementation of cost
eective smart home controller with android application
using node MCU and internet of things (IOT),” in Pro-
ceedings of the 2018 2nd International Conference on Power,
Energy and Environment: Towards Smart Technology
(ICEPE), pp. 1–5, Shillong, India, June 2018.
[102] X. Ma, N. Goonawardene, and H. P. Tan, “Identifying Elderly
with Poor Sleep Quality Using Unobtrusive in-home Sensors
for Early Intervention,” in Proceedings of the ACM In-
ternational Conference Proceeding Series, pp. 94–99, Bologna
Italy, November 2018.
[103] G. Chimamiwa, M. Alirezaie, F. Pecora, and A. Lout,
“Multi-sensor dataset of human activities in a smart home
environment,” Data in Brief, vol. 34, Article ID 106632, 2021.
[104] N. Rajkumar, A. B. Rajendra, and V. Vinod, “H2M com-
munication for home appliances automation using android
application,” Procedia Computer Science, vol. 167, pp. 2561–
2569, 2019.
[105] R. Majeed, N. A. Abdullah, I. Ashraf, Y. Bin Zikria,
M. F. Mushtaq, and M. Umer, “An Intelligent, Secure, and
Smart Home Automation System,” Scientic Programming,
vol. 2020, Article ID 4579291, 14 pages, 2020.
[106] M. Chan, E. Campo, W. Bourennane, F. Bettahar, and
Y. Charlon, “Mobility behavior assessment using a smart-
monitoring system to care for the elderly in a hospital en-
vironment,” in Proceedings of the ACM International Con-
ference Proceeding Series, pp. 1–5, Rhodes Greece, May 2014.
[107] H. Fakhrurroja, A. Abdillah, U. Nadiya, and M. Arin,
“Hand State Combination as Gesture Recognition Using
Kinect V2 Sensor for Smart Home Control Systems,” in
28 Journal of Electrical and Computer Engineering
Proceedings of the 2019 IEEE International Conference on
Internet of ings and Intelligence System (IoTaIS), pp. 74–78,
Bali, Indonesia, November 2019.
[108] Y. L. Hsu, P. H. Chou, H. C. Chang et al., “Design and
implementation of a smart home system using multisensor
data fusion technology,” Sensors, vol. 17, no. 7, p. 1631, 2017.
[109] W. M. Kang, S. Y. Moon, and J. H. Park, “An enhanced
security framework for home appliances in smart home,”
Human-centric Computing and Information Sciences, vol. 7,
no. 1, p. 6, 2017.
[110] T. Nilpanapan and T. Kerdcharoen, “Social Data Shoes for
Gait Monitoring of Elderly People in Smart home,” in
Proceedings of the 2016 9th Biomedical Engineering In-
ternational Conference (BMEiCON), pp. 1–5, Laung Prabang,
Laos, December 2016.
[111] A. Moschetti, L. Fiorini, D. Esposito, P. Dario, and F. Cavallo,
“Toward an unsupervised approach for daily gesture rec-
ognition in assisted living applications,” IEEE Sensors
Journal, vol. 17, no. 24, pp. 8395–8403, 2017.
[112] J. Kulsiriruangyos, V. Rattanawutikul, P. Sangsartra, and
D. Wongsawang, “Home security system for alone elderly
people,” in Proceedings of the 2016 Fifth ICT International
Student Project Conference (ICT-ISPC), pp. 65–68,
Nakhonpathom, ailand, May 2016.
[113] A. Khan, A. Al-Zahrani, S. Al-Harbi, S. Al-Nashri, and
I. A. Khan, “Design of an IoT smart home system,” in
Proceedings of the 2018 15th Learning and Technology
Conference (L&T), pp. 1–5, Jeddah, Saudi Arabia, Feburary
2018.
[114] K. Sehgal and R. Singh, “Iot based smart wireless home
security systems,” in Proceedings of the 2019 3rd In-
ternational Conference on Electronics, Communication and
Aerospace Technology (ICECA), pp. 323–326, Coimbatore,
India, June 2019.
[115] S. Zhihua, “Design of smart home system based on ZigBee,”
in Proceedings of the 2016 International Conference on Robots
and Intelligent System (ICRIS), pp. 167–170, ZhangJiaJie,
China, August 2016.
[116] F. A. R. M. Wildan, E. A. Z. Hamidi, and T. Juhana, “e
design of application for smart home base on LoRa,” in
Proceedings of the 2020 6th International Conference on
Wireless and Telematics (ICWT), pp. 1–6, Yogyakarta,
Indonesia, September 2020.
[117] A. Yassine, S. Singh, M. S. Hossain, and G. Muhammad, “IoT
big data analytics for smart homes with fog and cloud
computing,” Future Generation Computer Systems, vol. 91,
pp. 563–573, 2019.
[118] S. Ramapatruni, S. N. Narayanan, S. Mittal, A. Joshi, and
K. Joshi, “Anomaly detection models for smart home se-
curity,” in Proceedings of the 2019 IEEE 5th Intl Conference on
Big Data Security on Cloud (BigDataSecurity), IEEE Intl
Conference on High Performance and Smart Computing,
(HPSC) and IEEE Intl Conference on Intelligent Data and
Security (IDS), pp. 19–24, Washington, DC, USA, May 2019.
[119] F. E. Fernandes, G. Yang, H. M. Do, and W. Sheng, “De-
tection of Privacy-Sensitive Situations for Social Robots in
Smart Homes,” in Proceedings of the 2016 IEEE International
Conference on Automation Science and Engineering (CASE),
pp. 727–732, Fort Worth, TX, USA, August 2016.
[120] M. Nobakht, V. Sivaraman, and R. Boreli, “A host-based
intrusion detection and mitigation framework for smart
home IoT using OpenFlow,” in Proceedings of the 2016 11th
International Conference on Availability, Reliability and
Security (ARES), pp. 147–156, Salzburg, Austria, August
2016.
[121] M. Anagnostopoulos, G. Spathoulas, B. Viaño, and
J. Augusto-Gonzalez, “Tracing your smart-home devices
conversations: a real world iot trac data-set,” Sensors,
vol. 20, no. 22, pp. 6600–6628, 2020.
[122] P. K. Sharma, J. H. Park, Y. S. Jeong, and J. H. Park, “SHSec:
SDN based secure smart home network architecture for
internet of things,” Mobile Networks and Applications,
vol. 24, no. 3, pp. 913–924, 2019.
[123] Y. N. Aung and T. Tantidham, “Review of Ethereum: Smart
home Case Study,” in Proceedings of the 2017 2nd In-
ternational Conference on Information Technology (INCIT),
pp. 1–4, Nakhonpathom, ailand, June 2017.
[124] F. Martins, M. F. Almeida, R. Calili, and A. Oliveira, “Design
thinking applied to smart home projects: a user-centric and
sustainable perspective,” Sustainability, vol. 12, no. 23,
pp. 10031–10127, 2020.
[125] J. Lalithavani, R. Senthil Ganesh, S. A. Sivakumar, and
B. Maruthi Shankar, “WITHDRAWN: cloud server based
smart home automation and power management,” Materials
Today Proceedings, vol. 12, 2020.
[126] R. Senanayake and S. Kumarawadu, “A robust vision-based
hand gesture recognition system for appliance control in
smart homes,” in Proceedings of the 2012 IEEE International
Conference on Signal Processing, Communication and
Computing ICSPCC, pp. 760–763, Hong Kong, China, Au-
gust 2012.
[127] K. Anbarasan and J. S. A. Lee, “Speech and Gestures for
Smart-home Control and Interaction for Older Adults,” in
Proceedings of the 2018 - 3rd International Workshop Mul-
timedia for Personal Health and Health Care, pp. 49–57,
Seoul Republic of Korea, October 2018.
[128] X. Tang, J. Zhao, W. Li, and B. Feng, “Design and imple-
mentation of smart home cloud system based on kinect,”
IFIP Advances in Information and Communication Tech-
nology, vol. 538, 2018.
[129] L. D. Tsuchiya, L. F. Braga, O. de Faria Oliveira,
R. W. de Bettio, J. G. Greghi, and A. P. Freire, “Design and
evaluation of a mobile smart home interactive system with
elderly users in Brazil,” Personal and Ubiquitous Computing,
vol. 25, no. 2, pp. 281–295, 2021.
[130] F. Portet, M. Vacher, C. Golanski, C. Roux, and B. Meillon,
“Design and evaluation of a smart home voice interface for
the elderly: acceptability and objection aspects,” Personal and
Ubiquitous Computing, vol. 17, no. 1, pp. 127–144, 2013.
[131] M. Galliakis, C. Skourlas, E. Galiotou, and I. Voyiatzis, “A
Low-Cost Smart home for the Assistance of Elderly Persons
and Patients,” in Proceedings of the ACM Internaltional
Conference Proceeding Series, pp. 93–98, Athens Greece,
December 2018.
[132] J. N. Chanda, I. A. Chowdhury, M. Peyaru, S. Barua,
M. Islam, and M. Hasan, “Healthcare Monitoring System for
Dedicated COVID-19 Hospitals or Isolation Centers,” in
Proceedings of the 2021 IEEE Mysore Sub Section In-
ternational Conference (MysuruCon), pp. 405–410, Hassan,
India, October 2021.
[133] F. Li, “Design of an interactive two-way telemedicine service
system for smart home care for the elderly,” Journal of
Healthcare Engineering, vol. 2021, Article ID 6632865,
11 pages, 2021.
[134] S. Sawidin, D. S. Pongoh, and A. A. S. Ramschie, “Design of
smart home control system based on android,” in Pro-
ceedings of the 2018 International Conference on Applied
Journal of Electrical and Computer Engineering 29
Science and Technology (iCAST), pp. 165–170, Manado,
Indonesia, October 2018.
[135] A. Mihoub, “A Deep Learning-Based Framework for Human
Activity Recognition in Smart Homes,” Mobile Information
Systems, vol. 2021, Article ID 6961343, 7 pages, 2021.
[136] P. Macheso, T. D. Manda, S. Chisale, N. Dzupire, J. Mlatho, and
D. Mukanyiligira, “Design of ESP8266 smart home using
MQTTand node-RED,” in Proceedings of the 2021 International
Conference on Articial Intelligence and Smart Systems (ICAIS),
pp. 502–505, Coimbatore, India, March 2021.
[137] O. Taiwo, “Internet of ings-Based Intelligent Smart Home
Control System,” Security and Communication Networks,
vol. 2021, Article ID 9928254, 17 pages, 2021.
[138] P. Nallabolu, L. Zhang, H. Hong, and C. Li, “Human
presence sensing and gesture recognition for smart home
applications with moving and stationary clutter suppression
using a 60-GHz digital beamforming FMCW radar,” IEEE
Access, vol. 9, pp. 72857–72866, 2021.
[139] C. Yi and X. Feng, “Home interactive elderly care two-way
video healthcare system design,” Journal of Healthcare En-
gineering, vol. 2021, Article ID 6693617, 11 pages, 2021.
[140] A. A. Zaidan, B. B. Zaidan, M. Y. Qahtan et al., “A survey on
communication components for IoT-based technologies in
smart homes,” Telecommunication Systems, vol. 69, no. 1,
pp. 1–25, 2018.
[141] R. Iten, J. Wagner, and A. Zeier R¨oschmann, “On the identi-
cation, evaluation and treatment of risks in smart homes:
a systematic literature review,” Risks, vol. 9, no. 6, p. 113, 2021.
[142] Z. A. Almusaylim and N. Zaman, “A review on smart home
present state and challenges: linked to context-awareness
internet of things (IoT),” Wireless Networks, vol. 25, no. 6,
pp. 3193–3204, 2019.
[143] M. Hasan, M. J. Hossein, M. Hossain, H. U. Zaman, and
S. Islam, “Design of a scalable low-power1-bit hybrid full adder
for fast computation,” IEEE Transactions on Circuits and Sys-
tems II: Express Briefs, vol. 67, no. 8, pp. 1464–1468, 2020.
[144] A. Q. H. Badar and A. Anvari-Moghaddam, “Smart home
energy management system–a review,” Advances in Building
Energy Research, vol. 16, pp. 118–143, 2020.
[145] A. S. M. Irfan, N. H. Bhuiyan, M. Hasan, and M. M. Khan,
“Performance analysis of machine learning techniques for
wind speed prediction,” in Proceedings of the 2021 12th
International Conference on Computing Communication and
Networking Technologies (ICCCNT), pp. 1–6, Kharagpur,
India, July 2021.
[146] J. Sultana, B. Saha, S. Khan, T. M. Sanjida, M. Hasan, and
M. M. Khan, “Identication and classication of melanoma
using deep learning algorithm,” in Proceedings of the 2022
IEEE International Conference on Distributed Computing
and Electrical Circuits and Electronics (ICDCECE), pp. 1–6,
Ballari, India, April 2022.
[147] A. F. Faisal, A. Rahman, M. T. M. Habib, A. H. Siddique,
M. Hasan, and M. M. Khan, “Neural Networks based
multivariate time series forecasting of solar radiation using
meteorological data of dierent cities of Bangladesh,” Results
in Engineering, vol. 13, Article ID 100365, 2022.
[148] D. J. Olsen, M. R. Sarker, and M. A. Ortega-Vazquez, “Optimal
penetration of home energy management systems in distri-
bution networks considering transformer aging,” IEEE Trans-
actions on Smart Grid, vol. 9, no. 4, pp. 3330–3340, 2018.
[149] F. Shahriyar, M. Islam, A. Chakraborty, M. M. Uddin,
A. H. Siddique, and M. Hasan, “Feasibility and cost analysis
of grid connected hybrid solar home system: a case study of
chattogram district in Bangladesh,” in Proceedings of the
2022 IEEE International Conference on Distributed Com-
puting and Electrical Circuits and Electronics (ICDCECE),
pp. 1–6, Ballari, India, April 2022.
[150] S. Chowdhury and M. Hasan, “Design of an automatic gain
control loop for high speed communication,” International
Journal of Circuit eory and Applications, vol. 51, no. 1,
pp. 47–66, 2022.
[151] W. Iqbal, H. Abbas, B. Rauf, Y. A. Bangash, M. F. Amjad, and
A. Hemani, “PCSS: privacy preserving communication
scheme for SDN enabled smart homes,” IEEE Sensors
Journal, vol. 22, no. 18, pp. 17677–17690, 2022.
[152] A. Tittaferrante and A. Yassine, “Multiadvisor reinforcement
learning for multiagent multiobjective smart home energy
control,” IEEE Transactions on Articial Intelligence, vol. 3,
no. 4, pp. 581–594, Aug. 2022.
[153] Y. Cho, J. Oh, D. Kwon, S. Son, J. Lee, and Y. Park, “A secure
and anonymous user authentication scheme for IoT-enabled
smart home environments using PUF,” IEEE Access, vol. 10,
pp. 101330–101346, 2022.
[154] A. H. Siddique, M. Hasan, S. Islam, and K. Rashid, “Prospective
smart distribution substation in Bangladesh: modeling and
analysis,” Sustainability, vol. 13, no. 19, p. 10904, 2021.
[155] A. Fahim, M. A. Chowdhury, M. F. Alam, F. Elahi,
E. C. Shourov, and M. Hasan, “Smart transformer theft
protection and maintenance monitoring system,” in Pro-
ceedings of the 2021 2nd International Conference on Ro-
botics, Electrical and Signal Processing Techniques (ICREST),
pp. 607–612, DHAKA, Bangladesh, January 2021.
[156] M. Y. Chen, “Establishing a cybersecurity home monitoring
system for the elderly,” IEEE Transactions on Industrial
Informatics, vol. 18, no. 7, pp. 4838–4845, 2022.
[157] Q. Wu, X. Chen, Z. Zhou, and J. Zhang, “FedHome: cloud-
edge based personalized federated learning for in-home
health monitoring,” IEEE Transactions on Mobile Comput-
ing, vol. 21, no. 8, pp. 2818–2832, 2022.
[158] S. Suman, A. Etemad, and F. Rivest, “Potential impacts of
smart homes on human behavior: a reinforcement learning
approach,” IEEE Transactions on Articial Intelligence, vol. 3,
no. 4, pp. 567–580, 2022.
[159] S. Zou, Q. Cao, C. Wang, Z. Huang, and G. Xu, “A robust
two-factor user authentication scheme-based ECC for smart
home in IoT,” IEEE Systems Journal, vol. 16, no. 3,
pp. 4938–4949, 2022.
[160] M. Hasan, M. S. Hussain, M. Hossain, M. Hasan,
H. U. Zaman, and S. Islam, “A high-speed and scalable
XOR-XNOR based hybrid full adder design,” Computers and
Electrical Engineering, vol. 92, 2021.
[161] M. Almutairi, L. A. Gabralla, S. Abubakar, and H. Chiroma,
“Detecting Elderly Behaviors Based On Deep Learning For
Healthcare: Recent Advances, Methods, Real-World Ap-
plications And Challenges,” IEEE Access, vol. 10,
pp. 69802–69821, 2022.
[162] M. Mohamed, A. El-Kilany, and N. El-Tazi, “Future Ac-
tivities Prediction Framework In Smart Homes Environ-
ment,” IEEE Access, vol. 10, pp. 85154–85169, 2022.
[163] M. J. Islam, A. Rahman, S. Kabir et al., “Blockchain-Sdn-
BasedEnergy-Aware And Distributed Secure Architecture
For Iot In Smart Cities,” IEEE Internet of ings Journal,
vol. 9, no. 5, pp. 3850–3864, 2022.
30 Journal of Electrical and Computer Engineering
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