IoT-enabled smart cities towards green energy systems: a review

International Journal of Reconfigurable and Embedded Systems (IJRES)
Vol. 13, No. 3, November 2024, pp. 708~723
ISSN: 2089-4864, DOI: 10.11591/ijres.v13.i3.pp708-723  708
Journal homepage: http://ijres.iaescore.com
IoT-enabled smart cities towards green energy systems: a
review
Husnul Ajra1,2
, Mazlina Abdul Majid1,3
, Md. Shohidul Islam1
1
Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, Kuantan, Malaysia
2
Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University,
Gopalganj, Bangladesh
3
Centre for Artificial Intelligence and Data Science, Universiti Malaysia Pahang Al-Sultan Abdullah, Kuantan, Malaysia
Article Info ABSTRACT
Article history:
Received May 23, 2023
Revised Apr 19, 2024
Accepted May 17, 2024
Integration of internet of things (IoT) in smart city management to improve
various functions and living standards due to increasing population growth
has dramatically evolved ubiquitous and essential services at various stages
of urbanization. Hence, smart cities need to be eco-friendly by improving
various sectors like education, health, and transport to provide an urban and
sustainable quality of life through solving complicated green energy
networks, controlling toxic pollution risks, and public safety. Linking
optimized green energy systems with the production and automation of
advanced applications is crucial to compose implementation strategies for
smart city services. This paper aims to conduct a review on eco-friendly
plans and infrastructure of IoT-enabled smart cities by exploiting green
energy approaches. This study performs critical observations, ideas, and
analyses of recent research in the context of our mentioned research theme.
This paper points out the technical and functional challenges of an optimal
performance-based green IoT-enabled smart city infrastructure. In this sense,
this study organizes observations of relevant initiatives, technologies, and
experiences in IoT-enabled smart cities, as well as how to embed it with
green energy. Moreover, it can provide significant directions to intellectuals
and authorities to develop IoT-enabled smart city applications for
prospective research.
Keywords:
Green energy
Internet of things
Renewable
Smart city
Sustainability
This is an open access article under the CC BY-SA license.
Corresponding Author:
Mazlina Abdul Majid
Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah
Kuantan, 26600 Pekan, Pahang, Malaysia
Email: mazlina@umpsa.gmail.com
1. INTRODUCTION
The notion of the smart city is of great significance in the present daytime, where the progressive
urbanization of the world population is particularly related to the ability to assure the citizens' quality of life
with all kinds of technological services and digital communication. The impact of urbanization on the rural
environment is evident due to the transfer of all services from urban areas to citizens due to rapid population
growth [1]. The interoperability of internet of thing (IoT) technology signifies the potential for a better life
for the ubiquitous citizen to emerge as smart cities in the urban context in recent years. IoT technology is
becoming more prosperous daily due to the tremendous development of new types of accessing devices and
technologies, and its inclusion in smart city applications is happening. IoT-empowered smart city framework
helps citizens create any kind of convenience and secure lifestyle using any medium at any time.

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IoT-enabled smart cities towards green energy systems: a review (Husnul Ajra)
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Operational processes used in city sub-systems and services require large amounts of energy [2]. To
build a smart city paradigm on a big data scale towards green energy systems, it needs to integrate and
manage all activities of academia, health, industry, land surveying, government, logistics, institutions, and
citizens within an IoT-based smart city system. Integration of various renewable energy schemes into smart
green [3] city systems is essential to avoid climate change and environmental instability. Cities in many
developed and developing countries in the world are facing various problems, such as extensive
infrastructural expansion, environmental degradation and pollution, transport congestion, increasing energy
demand, unequal distribution of facilities, unequal distribution of pure water and foodstuffs, and lack of basic
sanitation. Moreover, the continuous development of IoT technology in urban systems requires
environmental monitoring and optimal planning accordingly in e-healthcare, industrial digitalization,
transportation, automation, smart lighting, traffic monitoring, energy, electronics, and others. The prominent
delineation of a smart city is to provide green and pollution-free services to citizens by harnessing renewable
and clean energy to get a natural lifestyle [4].
In this case, smart cities require renewable electricity generated from various natural energy sources,
for example, light, heat, wind, and water, to be used for operational aspects of power management of various
infrastructures. The optimal electricity use can be controlled using IoT infrastructure for renewable power
management and distribution in smart city environments [5]. In IoT-enabled smart city applications using
renewable energy, numerous information on various projects related to urbanization can be shared, decisions
taken, and tasks executed optimally. Smart meters can be automated by interfacing sensors with utilities used
by urban citizens to meet the goal of reducing renewable energy consumption when only needed or
demanded. Developing greening smart city applications requires monitoring, testing, pattern analysis, and
control of overall software and devices.
However, this study offers an outstretched review of IoT-enabled smart cities towards green energy
systems with full emphasis on making them more reliable, secure, and sustainable [6]. The general paradigm
of an IoT-enabled smart city powered by renewable energy is depicted in Figure 1. Thus, the researchers
need to also ensure connectivity in social, economic, and cultural aspects to improve and ease the quality of
life of present and future generations to create green smart city frameworks considering the changing context
of the global environment. The major challenge for researchers in this work is the integration of IoT
technology [7] and renewable energy requirements into smart city applications where security, scalability,
legitimate access, authorized resource storage, and other operational information or raw material will be
protected. Furthermore, the critical analysis of this work may be favorable to intellectuals and authorities for
higher-level knowledge acquisition and potential research directions.
Figure 1. IoT-enabled smart city powered by green energy
The leading contributions of this study are highlighted as follows: i) this work introduces the
fundamentals of IoT-enabled smart cities towards green energy and provides an updated and extensive
overview of smart city domains and indicators, as well as presents the greening approach of smart cities; ii) it
represents the research methodology, including research questions (RQs) and related work selection strategy
in recent studies; iii) this study exposes the critical observation and appraisals in the context of our mentioned
research theme by exploring existing recent methods; and iv) this work epitomizes a wide range of technical
and functional challenges with directives to be addressed in the execution of IoT-based smart cities so that
researchers can get a broad scope to learn how to enhance energy efficiency and gain green sustainability by
using renewable sources.
The residue of this article is arranged as follows. Section 2 affords the background knowledge for
IoT-enabled green smart cities, which contains the fundamental design of IoT, various smart city domains
and indicators, and the greening strategy for a smart city. Section 3 explores the research methodology,

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formalizes research questions, and mentions a search strategy for related work on recent developments.
Section 4 illustrates critical observations on some recent trends and targets of IoT-powered smart green cities.
Section 5 assesses the technical and functional challenges involved in the future directions of IoT-enabled
smart cities. Finally, section 6 concludes the systematic review.
2. BACKGROUD KNOWLEDGE
2.1. IoT terminology and architecture
In the epoch of industry 4.0, the world is moving towards developing and advancing “IoT”
technology everywhere, in everything, and at any time. The world is becoming more innovative and
intelligent, with everything using unique and meaningful addresses, critical communication systems, and
various intelligent networks. IoT connects millions of devices. IoT deals with large amounts of data,
processes massive data, and performs feasible actions to make human lives smarter, safer, more convenient,
and more accessible [8]. IoT applications are in various sectors, such as smart homes, healthcare, smart cities,
transportation, industries, and the energy sector. IoT architectures accomplish the steps of data sensing,
actuating, processing, storing, analysis, and final exploitation through fog, edge, and cloud computation using
various amenities and applications for transmitting and receiving messages among appliances. Many other
technologies and approaches are engaged in the improvement of IoT frameworks. Researchers use the IoT
framework with different approaches, such as three, four, and five-layer, based on the open systems
interconnect (OSI) model to achieve their work objectives [9]. Consequently, the elementary IoT functional
architectures have been shown in Figure 2, and it comprises five layers, including the sensing or perception
layer, processing layer, network layer, business layer, and application layer.
Figure 2. Architecture of IoT
The sensing or perception layer is the foundation of IoT architecture related to the physical level. It
provides the interface with the tangible world by transferring and obtaining data utilizing wireless networks.
Therefore, the perception layer has vital functions such as data collection, information perception, physical
parameters sensed, or other intelligent objects identified, assisting in completing the control objects of
perception using various sensors, actuators, and physical objects. It includes radio frequency identification
(RFID) tags global positioning system (GPS), camera, sensor network, terminals, code reader-writer, 2D
code labels, and all varieties of sensors. Furthermore, the network layer is the brain of IoT architecture that

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processes and conveys sensor-responding information from the perception layer. It operates with network
devices, intelligent appliances, and servers using an internet network, convergence network, network
management center, resourceful processing center, information center, and base station node.
The access strategy for this layer is Ad hoc, ZIGBEE, Mesh, 5G, WIFI, LoRaWAN, industrial bus,
and Sigfox, which concede to accumulate the information by different cognitive tools or preparatory
approaches and network permits. The processing layer is a significant layer of IoT structure that
accomplishes many functionalities, such as storing, processing, and analyzing large amounts of transportation
data and huge pieces of information on objects obtained from the network layer. Also, it can handle and
process various lower layers of services and utilities. It employs a large number of primary technologies,
such as big data processing, databases, innovative processing, ubiquitous computing, and cloud computing
modules. Therefore, this layer contains several data storage modalities and possesses various functionalities.
The function of the application layer is based on the data processed in the process layer. This layer
serves smart buildings, homes, transportation, economy, and health applications in IoT-enabled smart cities.
It also customizes and delivers the output formats with specific services and applications demanded by users.
It plays a vigorous role in pushing the IoT to a large-scale development for promoting and developing the
diverse applications of the IoT. The fifth layer is the business layer which acts as the manager of the IoT
architecture [10]. This layer is employed to classify various functions and front-end instruments that ingest
vast amounts of information from the application layer for conducting developed visualization services and
big data analytics, constructing business models, supportive decision-making procedures, conducting
simulations, and maintaining user privacy.
2.2. Smart city domains and indicator
A smart city refers to an intelligent metropolitan area that operates in an automated manner based on
IoT sensors through various electronic devices and applications. Its main objective is to analyze the collected
data through data science using smart information and communication technology (ICT) to optimize various
functions related to human welfare by increasing the operational efficiency of the city and driving economic
growth to improve the quality of citizens' lives. It uses IoT [11] sensors and other electronic devices from
various aspects of urban infrastructure, including automated large buildings, vehicles, properties, highways,
trains, bridges, communications, electricity, water, and more, to collect essential data that is used later. Data
is also collected, analyzed, and processed for transportation and traffic systems, power supply, power plants,
waste management, and water systems. Residents are involved in data usage in smart cities because residents
can genuinely participate in making smart cities more sustainable through awareness, education, and
openness to data collection in case there is a problem with data use [12].
Smart cities further automate manual processes in corruption detection, information approaches,
hospitals, libraries, and schools that help to improve the work environment by providing employees with
more resources to accomplish their entire prospect and offer more convenient services. Furthermore, the
usefulness of smart technology contributes to reducing work stress, reducing the time required for daily tasks,
and making work comfortable by automating the daily tasks of city residents, allowing employees to focus on
more strategic initiatives. Additionally, increasingly metropolitan environmental resources and energy
efficiency are evolving to achieve an efficient, intelligent smart city and enhance its performance. Therefore,
sustaining energy supply and promoting energy sustainability can be used to execute sustainable [13] policy
plans to achieve significant changes in intelligent cities based on increasing metropolitan environmental
resources and energy efficiency. Another critical factor in this change is identifying, committing, and
integrating essential societal factors, such as governments, public-private organizations, civil society, and
various private companies. According to this concept, we found different domains in the smart city
framework context.
We emphasize the following domains such as smart governance, mobility and transportation, living
and infrastructure, energy, economy, industry and manufacturing, healthcare, and environment, as shown in
Figure 3, which are commonly used to represent smart city domains and indicators. Smart governance uses
ICT technology to facilitate city governance's administrative and bureaucratic actions to enhance decision-
making processes by nourishing smart collaboration, reliable channels, and network integration between
various stakeholders and social actors [14]. In this regard, nationals can contribute to decision-making
processes by participating in the data acquisition process using ICT-based tools and sociable media through
their sensor-based smartphones and mobile devices. Decision-making processes based on participating actors
are transformed into smart governance aids on a government-to-government, government-to-citizen (G2C),
and government-to-business (G2B) basis. Government to government (G2G) aims to enhance communication
between various public administration groups and entities through the interaction of actors using IoT
technology, thereby keeping all processes of smart cities dynamic. IoT technologies are extensively adopted
in G2B activities, which concentrate on interactions between public authorities and business companies to

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facilitate and enhance relationships between local governments and organizations providing services such as
water management, waste, and energy.
Figure 3. Smart city domains and indicators
Also, G2C uses smart mobile applications, public authority web portals, and social media to
enhance interaction and communication between citizens and public administration. Moreover, RFID, smart
ID cards, IoT technologies, biometric sensors for authentication and signature, and identity recognition
according to various government standards are widely and increasingly adopted in smart mobile devices.
These features are needed to access the services supplied and for the personal consultation of citizens. The
concept of smart mobility and transport [15] refers to the transition from conventional transport procedures to
mobility-as-a-service, where an intelligent IoT scheme supports distinct actors (citizens, public
administration, private enterprises) and entities (sensors, individual devices, vehicles, and actuators).
Various sensors, actuators, RFID tags, mobile-to-mobile communication, and GPS services based
on location are the basis of intelligent IoT-based transportation infrastructure. Even intelligent IoT-based
transportation infrastructure allows the provision for managing various transportation services, for instance,
public and private traffic flow, smart parking, dynamic traffic routing, sustainable mobility, and traffic
solutions against traffic violations. Smart buildings and smart homes are key domains of smart living and
infrastructure that use various IoT technologies for rapidly growing implementations depending on the
specific use or scenario. All elements of the smart living and infrastructure domain relate to the expansion of
smart cities and the advancement and management of public services, such as education, healthcare, tourism,
and cultural activities. These are engaged in boosting the quality of citizen's life. IoT assimilation with
construction modeling mechanisms for smart buildings supplies a high-fidelity manifestation of buildings. It
has spatial features, rainwater drainage, air conditioning management, video surveillance, human activity
monitoring, and security systems to manage authenticated access to buildings. It provides tools to monitor
structural integrity and maintain warns for events such as gas leaks and fires.
In addition, smart homes consist of various sensors, individual devices, and actuators linked via
wireless networks and are usually driven by AI-based human-machine interfaces. These afford intelligent and
automatic services, such as power consumption, handling home resources and appliances, lighting
management, and surveillance. In conclusion, in the smart living domain, IoT devices have extensive
applications in various places and activities that improve the available living standards of competent citizens
in smart cities. In addition to conventional existing energy, many types of energy, such as smart, green, clean,
renewable, and sustainable, have been widely promoted and discussed for some years. Smart energy is
converting conventional existing energy into new energy based on scientific and IoT technologies so that
industrial structures can optimize with strong security measures, alternative energy collection, and energy
conservation and contribute to the reduction of harmful gas emissions [16].
Consequently, a smart energy system consists of decentralized sustainable energy sources that can
automatically integrate efficient energy distribution, energy consumption data collection, and energy
consumption optimization through IoT sensors. Moreover, the eco-friendly system energy optimization

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platform manages home appliances for human longevity. Also, smart energy schemes are associated with
various essential algorithms and historical data so that the user can become proficient and interested in
optimizing, coordinating, and controlling the distribution of various energy vectors with their help. Users
take a holistic view of how embedded algorithms can be widely operated at different energy levels. As a
result, the development of the smart city fully reflects technological advances and energy production and
consumption methods that play an indispensable role in forecasting future power demand and specifying the
pricing and availability of electricity.
The notion of a smart economy facilitates the innovative interconnection of global and local trading
through IoT technology. This domain corresponds to economic development, investment, business
environment, and entrepreneurship. It maximizes the widespread use of human capital, including knowledge,
talents, skills, and creativity. Consequently, advancements in IoT technology facilitate the rapid development
and realization of many benefits and services which drives the economy smarter and improves people's
quality of life [17]. Furthermore, the idea of sharing economy is also comprised in this sector, where private
companies or individuals offer their services and exploit their resources through peer-to-peer marketplaces.
There is also peer-to-peer labor service wherever residents and citizens offer their jobs and experiences for
precise work. Machine learning and AI procedures have been performed to build predictive models and
improve authorization systems for retail marketing and e-commerce. However, the smart economy domain
deals with essential indicators related to the economy's strength, efficiency, and progress. It exposes a
significant role in the advancement of smart cities and acts as a major driver of related dimensions.
The smart industry and manufacturing domain for industry 4.0 transforms a conventional industry
and manufacturing into a less human-dependent and innovative AI-based IoT technology, various
communication approaches, cloud-based manufacturing, and productive environments [18]. This domain can
automate smart cities using sensor technology in areas ranging from manufacturing processes, shipment
tracking, real-time data collection, and utilization of production and manufacturing. IoT technology and
ubiquitous cloud computing are broadly used in smart healthcare. These are helpful for distant health
monitoring, telenursing and telemedicine services, community health care, and gaining knowledge about
detailed drug reactions. This is more relevant to the recent global COVID-19 pandemic. For instance, the
body sensor network with wireless sensor network technology plays a role in incorporating disparate data
sources using AI-based IoT healthcare applications in distant patient monitoring. Using smart healthcare [19]
based on IoT technology, medical attendants in hospitals can provide various services by collecting biometric
and physiological data of patients with the help of intelligent medical appliances.
Smart cities use smart environment schemes to contain environmental information to develop an
IoT-based environment-friendly system conception, layout, and protocol. The applications, usefulness, and
services of the smart environment usually depend on various sensors used to estimate environmental
conditions and parameters, such as pressure, humidity, temperature, and different types of pollutants [20].
Moreover, intelligent sensing and visualization methods (LiDAR, satellites) are employed for land usage and
greenhouse gas (GHG), where geographic information systems (GIS) data and location-based services are
also utilized. The scheme is also linked to the control of pollution, freshwater reserves, water quality, and
meteorological and climatic conditions. In this consideration, air level monitoring is an essential factor for air
pollutants tracking grades, which convey a severe issue to people's health generated by the vehicle, waste,
industrial emissions, and heating. Even smart waste management is incorporated in this domain as it has
many environmental implications. Control approaches for waste generation are managed with smart waste
bins, which are settled with sensor devices and are able for real-time exploration of directly available
capabilities.
2.3. Greening approach in smart city
IoT-based smart cities connect digital devices through networks, offering several new services in
various domains. A smart city uses ICT technology and sensor-based IoT devices to green the entire city
environment through a pervasive and fundamental transformation. Also, progressive technological solutions
that have evolved from conventional to modern will help a city become smarter, greener, and more
sustainable [21]. Smart cities have the challenges of saving energy, lessening carbon dioxide (CO2)
emissions, and decreasing water and pesticide use. Even increased need for preliminary energy and water
scarcity are still severe impediments to the sustainability of the existing manufacturing system.
Consequently, renewable energy repositories, the solar, wind, and allocated resources can skillfully integrate
into a smart city.
Greening the smart city can expand greening quantities, diminish the urban warmness impact,
enhance the grade of indoor and outdoor air, lower indoor temperature, beautify urban geographies, raise
energy efficiency, defend building establishments, and lessen noise. Again, a greenhouse system addresses
various facets of the difficulties of traditional greening. It minimizes water use, and energy could develop by
increasingly multidisciplinary and sophisticated strategies to reach an optimum balance between proficient

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environmental governance and manufacturer use of functional resources. The Greening approach impresses
to be much more intellectually justifiable, adjustable, and creative and assembles it much more effortless to
be sustainable and green in a smart city.
3. RESEARCH METHOD
In this study, the purpose of a systematic literature review on IoT-enabled smart cities toward green
energy systems is to conduct a comprehensive exploration by logically defining, analyzing, and classifying
relevant areas of recent research. Moreover, this paper seeks to reveal the future areas of smart city
architecture by evaluating and investigating the status of existing works, clearly guiding the search and
selection criteria. Findings and evaluated findings of related developments and applications from existing
papers can be used by researchers and scholars more confidently in their own research. Through these
reviews, one can comprehend the challenges and issues of all kinds of data management methods for smart
cities and present very innovative ideas.
The methodology of the literature review process of this paper is depicted in Figure 4, which
involves several distinctive operations. This research considers issues such as review planning, review
process management, analysis, and reporting of findings to facilitate the literature review process. In general,
research questions and answers can be derived by collecting recent research papers using search engines for
systematic and systematic reviews and, at the same time, selecting appropriate and reasonable documents
based on their inclusion/exclusion criteria. For the review planning, subsections 3.1 and 3.2 discuss how this
study has shaped the research questions, search procedures, and paper selection. These crucial elements have
been carefully considered to ensure a comprehensive and effective review process. For accomplishing the
review process, it provides the details of the records screened, inclusion and exclusion criteria, and critical
observations in subsections 3.2 and 3.3. The details of the quality and evaluation process with statistical
analysis in section 4 and challenges with directions in section 5 are presented for developing IoT-enabled
smart cities towards green energy systems.
Figure 4. The methodology of literature review process
3.1. Formalizing research question criteria
For the specific purpose of reviewing recently published research on smart city-based green
structures and understanding their core content, some specific and functional research questions can be
considered in this article. This type of research question helps in synthesizing the intelligibility, need, and
clarity of the elements involved in building a smart city. This paper records the selected questions on the
smart city paradigm in Table 1, following the relevant methodology, sustainability, and benefits to sustain the
proposed study objectives.
Table 1. Research questions criteria to extract the recent work
RQ. N. Research question approach
RQ-01 What are the main obstacles in handling related services in smart city applications and how are those services
implemented in different sectors?
RQ-02 What methods or techniques should be used to apparently appraise different types of data in IoT-enabled smart city
structures?
RQ-03 How will renewable energy sources be incorporated into this framework to ensure a better environment for present and
future generations in cities?
RQ-04 How will smart city applications be integrated with new technologies to enrich people's quality of life?
RQ-05 What kind of relationship or synergy should smart city applications have with city administrative governance?
RQ-06 How to specify the concepts, open issues, and future directions of iot-enabled smart cities and environmental
sustainability in green urban development?

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3.2. Related work selection procedure on recent developments
In this subsection, this study clarifies related work selection procedures in the recent developments
of smart cities and IoT technology for our review process. The existing papers selection creates a scope to
determine and gather acquaintance of this domain. We have considered three (3) cases for recent paper
selection.
In the first case, the paper selection process is performed by applying the search engine Google
Scholar using a search strategy. We select some famous academic publishers such as Springer, IEEE,
ScienceDirect, Wiley, and Taylor and Francis to collect the current publishing paper from their digital
databases. For collecting the research paper, this study has conducted search keywords such as smart city,
IoT, IoT-enabled smart city, green energy system, smart city towards green energy, and renewable energy in
IoT. Additionally, relevant new technologies and algorithms are taken into account for searching published
research papers. This study manages search results based on adding the whole or a part of a related word by
mentioning keywords.
In the second case, search queries are assembled based on recent articles published, and we have
tried to find these papers from 2017 to 2022. Related and relevant keywords of our study are conducted on
journal citation reports (JCR), Science Direct, Scopus, and ACM digital library databases. Search criteria for
research papers are based on focused on the nature of recent available and crucial papers. Due to complexity
and ambiguity, non-English language-based articles, review papers, conference papers, book chapters, and
review papers were excluded from our study. Besides, to provide more focus on relevant issues, articles that
did not fully provide text on IoT-enabled smart city architecture toward green energy systems were not
accepted in our review process.
We have successfully collected a total of 217 articles from reputed databases on applications
supported by smart city infrastructure from the main objective of the systematic review. The inclusion and
exclusion criteria of the collected existing studies are presented in Table 2. We did not consider non-
published articles in English, non-clear, and non-specific goals in this study of IoT-based green smart city
infrastructure papers. Articles related to innovative city applications were filtered by removing duplicate,
irrelevant, and reported articles. A final 65 papers were selected by eliminating the inconsistent papers with
inappropriate criteria based on full literature type and text.
Table 2. Inclusion and exclusion criteria for filtering research paper
Inclusion criteria Exclusion criteria
1. Include recent publications from 2017 to 2022 1.Exclude publications prior to 2017
1. Include searching keywords based on our paper title 2.Exclude non-clear keywords and non-specific goals of
full-text publications
2. Include relevant new technologies and algorithms based
on IoT-enabled green smart cities
3.Exclude duplicate, irrelevant, and reported articles
3. Include high-level published literature and highly
reputable journals
4.Exclude non-English writing and non-related academic
journals
3.3. Critical observation of recent methods and targets
Various researchers have reviewed various strategies in the field of IoT-empowered smart cities
toward green energy systems. Recently, multiple processes or appliances have been tried to resolve,
including usability evaluation methods in intelligent city applications. In this part, we have attempted to
observe recent approaches and goals critically. An organized review of various fields of selected articles can
provide cognitive ideas for future researchers to further improve smart city applications.
Table 3 exposes the effective reflections on the smart city, IoT, and green energy keywords in
existing studies. This table uses 21 research studies to perform a thorough review of existing work and the
process of deriving evaluation criteria. The existing publications were thoroughly read to assess the
effectiveness of the research work, and the relevant strategies of those publications were decided upon.
In this case, we have used shortened forms such as IoT, smart city (SC), green energy (GE),
preeminent targets, architecture-based mode (ArBM), algorithm-based mode (AlBM), [2] real skill test
(RST), simulation (Sim), and observation to describe the validity of the evaluated terms used in most studies
to facilitate tabulation. The main motive of this paper is to include the measurement of operational strategies
in research that will have a more decisive role in building smart city infrastructure. In this portion, we have
attempted to mark recent suggested approaches and directions. The approaches and directions presented in
the selected articles will recreate an essential role in conceptualizing the entire phase of future smart city
infrastructure.

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Table 3. Effective reflections on the smart city, iot, and green energy keywords in existing studies
Ref. SC IoT GE Preeminent targets ArBM AlBM RST Sim Observation
[22] ✓ ✓ × Big data applicability in smart cities ✓ ✓ ✓ ✓ Using public tram service data and
collecting building-used energy and
environmental data
[23] ✓ ✓ × Privacy-preserving data in smart cities × ✓ × ✓ Achieved privacy-preserving views of
IoT appliances in smart cities
[24] ✓ ✓ × Real-time crowd management in smart
cities
✓ × × ✓ Crowd behaviour assessed in AI-aided
IoT based crowd control systems using
Wi-Fi frames analysis
[25] ✓ ✓ × Design full lifecycle management of
dumb devices for smart cities
✓ × ✓ ✓ Automatically and periodically context
awareness data processing on narrow-
band IoT
[26] ✓ ✓ × Boost urban tasks by deriving
intelligence and optimal policy
× ✓ × ✓ Significant time reduction using learning
methods in sensible data to get the best
urban services
[27] ✓ ✓ × Allow automated translation between
source and target IoT networks for
global smart cities.
✓ × × ✓ Using semantic interworking in smart
cities provides the ﬂexibility and dynamic
adaptation required by the environment.
[28] × ✓ ✓ Improve energy skills in managed
resources by connecting channels and
devices in IoT deployment.
× ✓ × ✓ Allow using optimal renewable energy at
LoRa wireless grid using reinforcement
learning.
[29] ✓ × ✓ To make reliable the smart city load
demand using renewable energy.
✓ × × ✓ Microgrid system with smart battery
storage control to ensure maximum
renewable energy.
[30] ✓ × × To improve data rates and reduce power
costs in all-over network services for
smart cities.
× ✓ × ✓ The iterative power control process was
conducted to improve energy
consumption for device-to-device
communications.
[31] ✓ × ✓ Reducing workload and application-
level time energy costs in the energy
internet for green cities.
× ✓ × ✓ Evaluate both energy and work
optimization objectives used in smart
cities using multiaction deep
reinforcement learning methods.
[32] ✓ × ✓ To transfer cooperative energy and data
in wireless green sensor networks of
smart cities.
✓ ✓ × ✓ Controlling collaborative energy and data
transfer protocols by joint optimization
based on subcarrier pairing, grouping,
and power allocation.
[33] ✓ × ✓ Connected and automated vehicles in
smart cities collect data from sensors
and make automated decisions.
✓ × × ✓ Demonstration of evaluation to efficiently
operationalize smart city metadata and
reduce energy consumption for them.
[34] ✓ ✓ ✓ Making smart cities green and
sustainable by applying security, cyber
defence, forensics and cyber threats
intelligence in IoT devices.
✓ ✓ × ✓ Powerful computing and storage
capabilities enable IoT devices to realize
low latency and efficient detection.
[35] ✓ ✓ ✓ Making smart cities green and
sustainable by applying security, cyber
defence, forensics, and intelligence in
IoT devices.
✓ × × ✓ Notably the system of managing and
evaluating the utilities of smart city
through the integration of energy sources.
[36] ✓ ✓ ✓ Transferring large amounts of data in a
sustainable and green manner by
interconnecting smart city digital
devices.
✓ × × ✓ Estimating battery lifetime, delay, power
drain, and packet loss ratio using HABPA
and DSA algorithms in optimal resource
management in smart city systems.
[37] ✓ ✓ ✓ Providing a promising platform for
parking space sharing to adjust
emissions and energy consumption in
smart cities.
× ✓ × ✓ Intelligent and affordable car parking
sharing system for driver using game
theory reservation model.
[38] ✓ × ✓ Conduct green energy consumption
strategy for smart cities.
✓ × ✓ ✓ Green energy consumption policy verifies
data size, response time, and packet time
by Bluetooth data transmission.
[39] ✓ ✓ × Apply immune authentication technique
in various IoT devices for smart city.
× ✓ × ✓ Performing security authentication
policy, analyze best computational cost,
and energy consumption.
[40] ✓ ✓ × Optimize energy consumption to
operate smart city’s interconnected
devices.
× ✓ × × Evaluating mathematical model and
calculating entropy to reduce energy
waste.
[41] ✓ × ✓ Optimize energy consumption to make
smart city applications accessible via
IoT to reduce environmental impacts.
× ✓ × ✓ Energy used in smart cities is based on
renewable energy systems and energy
storage systems using optimization
methods.
[42] ✓ ✓ × Developing traffic modelling concepts
and its patterns for IoT applications in
fifth generation technologies.
✓ × × ✓ To maintain various traffic profiles in IoT
using target tracking, telemetry, video
espial, or other urgent operations in city.

717
Table 4 reveals the latest research for controlling IoT-enabled services in the smart city. This table
uses 18 recently published research studies to guide the process of developing an intelligent city services
regulation and assessment criteria of existing work. We have tried to specify the potential criteria for building
smart city service infrastructure from existing publications. In this regard, we have used concise forms such
as propound platform, optimization (Opt), time cost (TC), security (Secu), heterogeneous nature (HN),
reliability (Rel), intelligent control (IC), and future direction to categorize the explored parameter used in
most studies. These explored parameters will support the researchers in testing the accuracy, efficiency, and
correctness of building real smart city infrastructure.
Table 4. Recent research into the ability to control smart city services
Ref. Propound platform Opt TC Secu HN Rel IC Future direction
[43] Smart environment platform × × ✓ ✓ × ✓ Feasibility study of automatic ontology alignment
in smart cities
[44] Mobile crowdsourcing scheme with
intrusion identification
✓ ✓ ✓ × × ✓ Measures the formulated security of futuristic IoT
based intelligent city applications
[45] Intelligent learning domain architecture
for smart city
✓ ✓ ✓ ✓ × ✓ Enhance the learning domain with behaviour
detection by deploying skilled embedded sensors
and tools
[46] IoT sensor-based facts monitoring and
forecasting model of urban economic
control
✓ ✓ × ✓ × ✓ Improve the structure of information availability,
impact checks in many areas for the stabilized
economy
[47] An integrated framework of IoT based
electronic cane for urban visually
impaired.
× ✓ ✓ ✓ ✓ ✓ Increased sensor nodes, and actual node location
identification, enhance the quality of speech
messages and beeps for a large-scale domain
[48] Optimized greenhouse climate control
design in IoT
✓ ✓ × × × ✓ Extend the scheme in a real environment with
essential greenhouse parameters
[49] Software-defined–IoT network design
in smart city
✓ × × ✓ ✓ ✓ Solve the load-balancing problem for a distinct
topology on large smart city networks using the
clustering approach
[50] Smart small cell infrastructure for IoT-
enabled smart city
✓ ✓ × × × × Simplify the approach for smart cities in real-
time networking services and communications
[51] Green Wi-Fi management model for
smart cities at the access point
✓ ✓ × × × × Not mentioned
[52] Food supply chain control in a
sustainable smart city network
✓ ✓ × × × ✓ Conducting cloud computation jobs, best power
consumption, and delay optimization
[53] Deep learning-based cyberattack
detection framework in smart city
× ✓ ✓ × ✓ ✓ Explore engineered features of adversarial attacks
and improve performance
[54] Ontology-based IoT scheme with
privacy-preserving in smart city
× ✓ ✓ ✓ × ✓ Evaluate the scheme on different operating
systems and release useless data from the IoT
devices
[55] Deep learning based IoT oriented
structure with software defined
networking (SDN) and blockchain for
smart city
× ✓ ✓ × ✓ ✓ Enhance automatic confirmation of transactions
and direct participation in the transaction
[56] Energy-efficient and IoT-enabled smart
street lighting controlling scheme
✓ ✓ × × × ✓ Ensure security and reliability performance of
roles of the critical street lighting scenario
[57] LilaSCI scheme on Living Lab Platform
for the smart city
✓ ✓ ✓ ✓ ✓ ✓ Not mentioned
[58] Multimodal large-scale service support
scheme in IoT-based smart city
× ✓ × ✓ × ✓ Design exploitation and availability of large-scale
services in governance, industries, small and
medium enterprises (SMEs), and academia
[59] Distributed secure network and energy-
aware model in smart city
✓ ✓ ✓ × ✓ × Manage advanced security, use other machine
learning and artificial intelligence (AI) approach,
and feasibility test of the platform
[60] Privacy-preserving and energy
conserving policy in mobile edge
computing-based smart city
✓ ✓ ✓ ✓ × ✓ Consider and enrich realistic social impact
modelling for service providers and remote cloud
collaboration using deep reinforcement learning
4. DISCUSSION AND ANALYSIS OF EXISTING STUDIES
This section organizes the recent paper publications with the year and extracts the foremost thought
and appraisement of the IoT-enabled green smart city from the information in Tables 3 and 4. It aims to
examine the status of reflection-based research on complementary services in the infrastructure of IoT-
enabled smart cities implementing renewable green energy. Figure 5 manifests the ordination of publications
according to year based on the concept of smart city. The graph evidently demonstrates that there is a
considerable number of published research papers from 2017 to 2022. However, fewer works in this sector
were revealed in the earlier years, or 2017 and 2018. Since then, the number of publications of this work has

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significantly increased for the improvements of related smart cities. The year 2022 witnessed the highest
number of articles published so far, which indicates the interest and focus of researchers on the quality and
evaluation of smart city projects. It is expected that more research articles will be published in the coming
years as the concept of smart cities is gaining more attention and becoming increasingly popular among
researchers and professionals.
Figure 6 exposes the foremost thought of the IoT-enabled green smart city in the prior literature. As
stated in Table 3, the assessment considerations of the foremost thought for the IoT-enabled green smart city
are presented in the figure through statistical work determination. The statistic percentage based on recent
works in mentioning table exhibits handling green energy at 15%, connection setting of IoT at 19%,
architecture-based plan at 17%, algorithm-based plan at 17%, real skill test at 4%, and simulation at 28%.
Figure 7 visualizes the recent study's appraisal of the intelligent green city with IoT. As mentioned
in Table 4, the appraisements of recent opinions in the work of renewable IoT-enabled smart cities are
introduced in the appearance through statistical work anticipations. The statistic percentage based on current
studies in the citing table shows optimization at 18%, intelligent control at 22%, heterogeneous nature at
13%, security at 15%, time cost at 23%, and reliability at 9%.
Figure 5. Ordination of publications according to year
Figure 6. Foremost thought of the IoT-enabled green smart city in the existing literature
Figure 7. Recent studies appraisement in the smart green city with IoT

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5. TECHNICAL AND FUNCTIONAL CHALLENGES AND DIRECTION OF IOT-ENABLED
GREENING SMART CITY
A smart city application uses rich digital technology as its primary ingredient. A smart city contains
a giant data management strategy that is constituted by various types of sub-information methods. It is
equipped with sensors and is digitally linked. The service providers can use the urban data collected to use
limited resources better to promote sustainability. However, IoT is one of the leading technologies to enable
innovative city development. Technical knowledge of IoT-based smart cities and their applications is rapidly
extending to real-world scenarios. Some of the significant technical, functional, and nontechnical challenges
for trendy IoT-enabled smart greening cities can be used to define the requirements of different data
providers. In this case, it works with various data formats and protocols, ensures scalability and
interoperability, and supports component sharing. It is important to sidestep redundant data acquisition,
repository, and investigation, thereby diminishing functional costs and increasing the sustainability of the
city. Additionally, in a smart city, the most often occurring challenges concern privacy, interoperability, and
security.
For instance, there are heterogeneous environments and interoperability issues in a smart city due to
many different IoT protocols, formats, and frameworks. Resolving these two problems could bring financial
blessings toward economic sustainability. In fact, reaching the highest grade of interoperability [61] and
generating a heterogeneous environment among appliances, services, and applications implicates reducing
costs for constructing wholly new and distinct deployments of decisions. It allows the exploitation of existing
approaches and backward compatibility through integration and incremental deployment. In addition, from a
technology perspective, the evolution of ICT infrastructure, from transmission media to various actuators and
sensors [62] in tangible space, is a significant challenge in considering an IoT-based intelligent city
enterprise. A high-speed, scalable, and trustworthy network connectivity-based structure is a crucial basis for
developing a smart city. Such a structure must be in place before smart city amenities are accessible to
stakeholders. Thus, acceptable, satisfactory, reliable infrastructures pave the way to perceive the city and
operate through sensors and actuators.
In IoT-based smart cities, acquiring the highest scalability expresses the prospect of efficiently
gathering and processing growingly significant volumes of data, which usually conducts a more increased
exactitude in facts evaluation and promotes real-time processing. An effectual big data managing scheme is
needed to operate different data types at different speeds. This system must be scalable, trustworthy, and
consistent without downtimes. The successive collection, accumulation, generation, and processing of vastly
miscellaneous data from various smart sensors have inherent challenges. However, big data aggregated
across cities is beneficial and critical to attaining intelligent urban objectives. For instance, GPS sensors
connected to automobiles can deliver useful data about traffic flow but produce enormous amounts of highly-
rated information. The technical and operational challenges and directions for an IoT-connected
infrastructure green smart city framework are demonstrated in Figure 8.
Figure 8. Technical and functional challenges and direction for smart green city throughout IoT

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Again, the task of fulfilling the availability is straightly proportional to the system's complexity and
size with the urban technological intelligent infrastructure. A vast amount of data engendered across the
metropolis makes scalability, efficiency, and availability a vital challenge. Maintaining the efficiency of such
a large approach should be mandatory. Proficient planning, execution optimization, efficient utilization of
resources, and instantaneous reaction to inquiries are a few critical assistances of the smart city. As the trend
moves towards IoT-based data collection for smart cities, confidentiality and security [63] matters are
increasing day by day. Because an IoT-based smart city incorporates various devices and applications for our
everyday lives, for instance, a smart traffic control application, which operates on user information about
traffic jamming, will involve that area of the operator assembled. Meeting the safety and secrecy provisions
is a significant challenge for IoT-enabled smart cities that implicate processing a large volume of sensitive
data. Threats from intruders, hackers, viruses, Trojans, and worms have the massive prospect of disrupting
the services and bringing down the entire procedure resulting in tremendous damages. Protecting sensitive
records at the IoT architecture level requires comprehensive security mechanisms for collection, processing,
storage, and dissemination. Privacy and security are essential in producing data and facilities available and
making citizens' trust and confidence in utilizing these procedures.
Since IoT-enabled smart cities require essential technical infrastructure, a substantial financial
investment must be needed to put the system in place. This investment is not confined to a period only; the
functioning and maintenance expenses [64] of such a sizeable real-time design are high. Thousands of
actuators, sensors, computing equipment, and networking appliances need for end-to-end connectivity.
Likewise, the necessity of information technology (IT) professionals, consultancies, and skilled specialists
will compromise a considerable cost. In order to meet the demanding efficiency and trustworthiness
requirements, the need for additional resources leads to high overhead. Moreover, an IoT-based smart city is
a superior solution to overcome the existing problems in the city with the rapid improvement of new,
dynamic, and advanced applications and social adaption. Slow application development is a great problem in
city management. For instance, one of the significant causalities behind the achievement and widespread
adoption of Android is its Play Store, where numerous applications are uploaded daily. However, researchers
have also specified challenges to social adaptability regarding inequality, digital sharing, and modifying
cultural practices. Community adaptation of such systems involves changes in the social practices of citizens
in general and those related to managing cities in particular.
Finally, the environmental and energy factors of IoT-enabled smart cities show a crucial role in the
evolution toward sustainable urban life. Tremendous technological development in the IoT-based smart city
has changed how we work and live, but this consumes energy and embraces toxic pollution and E-waste. In
order to expand the benefits and lessen the harm of IoT, the environmental and energy issues are considered
to move toward green IoT that is considered environmentally friendly. So, it is necessary to put many
measures to diminish carbon footprint, reduce energy consumption, increase energy conservation, mitigate
climate change, conserve fewer resources, and facilitate efficient techniques for energy usage should focus on
energy efficiency [65]. Furthermore, using renewable green sources for the energy systems of smart cities
depicts noteworthy assistance in decreasing energy consumption, reducing contaminant emissions, and
improving the quality of living circumstances based on technical, environmental, and economic criteria.
6. CONCLUSION
This paper introduces an exhaustive review of methodologies in serviceability and quality
assessment of IoT-enabled smart city systems toward green energy strategies. This review investigates
various operational dimensions of smart cities from existing papers that have the potential to offer sustainable
insights for future research. Prospective qualitative articles were selected from recent studies for the purpose
of devising IoT-based green smart cities considering inclusion and exclusion criteria. The most important
criteria and information are recorded by analyzing the solutions proposed by the researchers. The evaluation
dimensions of the published articles are categorized in the table to support the overall research findings. In
this context, checkmarks (✓) were used for published articles that supported the evaluation parameters or
dimensions; otherwise, cross marks (×) were treated for unsupported articles. It delivers a holistic idea of
IoT-enabled smart cities toward green energy operation. Recent paper publications have been plotted as
years, and researchers' thoughts and evaluations of those publications have been demonstrated as
percentages. Parameters or dimensions that have been worked more in recent times from the displayed
evaluation are presented as percentages. This research paper highlights the technological and functional
challenges and directions for IoT-enabled greening smart cities. Furthermore, this study can help researchers
to plan sustainable and innovative strategic frameworks for IoT-enabled smart cities through green energy
systems.

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ACKNOWLEDGEMENTS
This research is funded by the Malaysia National Research Grant (FRGS/1/2023/ICT03/UMP/02/2)
and Universiti Malaysia Pahang Al-Sultan Abdullah (RDU230131). This study is part of the research
conducted by University Postgraduate Research Grant (PGRS220338) This study is supported by
Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Bangladesh.
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BIOGRAPHIES OF AUTHORS
Husnul Ajra is currently a Ph.D. student at the Faculty of Computing, in
Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Malaysia. Also, she is an assistant
professor in the Department of Computer Science and Engineering at Bangabandhu Sheikh
Mujibur Rahman Science and Technology University, a public university in Gopalganj,
Bangladesh. Now she is on study leave from her working university in Bangladesh for her
Ph.D. She has eight years of experience as an academician before enrolling for her Ph.D. She
received her B.Sc. and M.Sc. degrees from the University of Rajshahi, Bangladesh, in the
Department of Information and Communication Engineering. Her research interests include
green technology, IoT, AI, and computer networks. She can be contacted at email:
husnul5606ice@gmail.com.
Dr. Mazlina Abdul Majid is a professor at Universiti Malaysia Pahang Al-Sultan
Abdullah (UMPSA), Malaysia, with 20 years of experience as an academician at the Faculty of
Computing, UMP. She received her Ph.D. in computer science from the University of
Nottingham, UK. She holds various responsibilities in administrative works, including as a
deputy dean of research and graduate studies and editor in chief for the International Journal of
Computer Systems and Software Engineering. Currently, she is the head of the Green
Technology Research Lab and head of Data Science and Analytics at UMP. She is one of the
academic program committees at UMP and other universities due to her vast experience in
teaching master's and undergraduate courses. Her research work focuses on operation
management with simulation modelling, green sustainability, software usability testing, and
software agents. Please feel free to connect with her for academic and research collaborations.
She can be contacted at email: mazlina@umpsa.gmail.com and mazlina@ump.edu.my.
Md. Shohidul Islam received the B.Sc. and M.Sc. degrees from the Department
of Information and Communication Engineering, University of Rajshahi, Bangladesh, in 2009
and 2010, respectively. He is currently working toward the Ph.D. degree in system network
and security with the Faculty of Computing, University Malaysia Pahang Al-Sultan Abdullah
(UMPSA), Malaysia. He was an assistant professor in computer science and engineering with
the Central University of Science and Technology, Dhaka, Bangladesh. He is currently a
research assistant with UMPSA. His current research interests include blockchain and cyber
security, IoT, heterogeneous network, distributed network, data security, AI, and neural
network. He can be contacted at email: msi.ice.ru@gmail.com.

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