Analyzing the Adoption Challenges of the Internet of Things
(IoT) and Artiﬁcial Intelligence (AI) for Smart Cities in China
Ke Wang 1,*, Yafei Zhao 2, Rajan Kumar Gangadhari 3and Zhixing Li 4
Citation: Wang, K.; Zhao, Y.;
Gangadhari, R.K.; Li, Z. Analyzing
the Adoption Challenges of the
Internet of Things (IoT) and Artiﬁcial
Intelligence (AI) for Smart Cities in
China. Sustainability 2021,13, 10983.
Academic Editor: Fadi Al-Turjman
Received: 10 August 2021
Accepted: 28 September 2021
Published: 3 October 2021
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1Department of Civil and Architectural Engineering, Qingdao University of Technology,
Shandong 273400, China
2Building Information Technology Innovation Laboratory (BITI Lab), Solearth Architecture Research Center,
Hong Kong 999077, China; firstname.lastname@example.org
3Industrial Engineering and Manufacturing Systems, National Institute of Industrial Engineering,
Mumbai 400087, India; Rajan.Gangadhari.email@example.com
4School of Design and Architecture, Zhejiang University of Technology, Zhejiang 310023, China;
Smart cities play a vital role in the growth of a nation. In recent years, several countries
have made huge investments in developing smart cities to offer sustainable living. However, there
are some challenges to overcome in smart city development, such as trafﬁc and transportation
management, energy and water distribution and management, air quality and waste management
monitoring, etc. The capabilities of the Internet of Things (IoT) and artiﬁcial intelligence (AI) can
help to achieve some goals of smart cities, and there are proven examples from some cities like
Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has
some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited.
This study aims to ﬁll this research gap by analyzing the causal relationships among the challenges
in smart city development, and contains several parts that conclude the previous scholars’ work,
as well as independent research and investigation, such as data collection and analysis based on
DEMATEL. In this paper, we have reviewed the literature to extract key challenges for the adoption
of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption
status. Therefore, using the PRISMA method, 10 challenges were identiﬁed from the literature review.
Subsequently, determination of the causal inter-relationships among the key challenges based on
expert opinions using DEMATEL is performed. This study explored the driving and dependent
power of the challenges, and causal relationships between the barriers were established. The results
of the study indicated that “lack of infrastructure (C1)”, ”insufﬁcient funds (C2)”, “cybersecurity
risks (C3)”, and “lack of trust in AI, IoT” are the causal factors that are slowing down the adoption of
AI and IoT in smart city development. The inter-relationships between the various challenges are
presented using a network relationship map, cause–effect diagram. The study’s ﬁndings can help
regulatory bodies, policymakers, and researchers to make better decisions to overcome the challenges
for developing sustainable smart cities.
smart cities; sustainability; Artiﬁcial Intelligence (AI); Internet of Things (IoT); expert
opinions; DEMATEL; emerging economy
The rapid migration of people to urban areas from rural areas is causing a burden
on big cities. The city councils and local governments are facing issues in managing the
essential needs of huge populations. On the other hand, global warming, climate change
and technology’s move towards renewable energy has pushed the revolution of smart
cities, which aim to provide all essential needs to the public without causing many negative
impacts on the environment. With the rapid development of science and technology
Sustainability 2021,13, 10983. https://doi.org/10.3390/su131910983 https://www.mdpi.com/journal/sustainability
Sustainability 2021,13, 10983 2 of 35
today, technological innovation has become a leading force in driving economic and social
development. These advanced cities must use new technologies to improve their core
systems to maximize optimization in all functions of city management by using limited
energy. As an important carrier of economic and social development, cities are also the main
gathering place of innovation factors, and the role of science and technology innovation in
urban development is becoming increasingly prominent, and is becoming the engine of
future development of cities [
]. Due to the recent industry achievements, the Internet of
Things (IoT) and artiﬁcial intelligence (AI) are current popular research topics, and they
have proven to return better results in many disciplines such as automating factories, public
surveillance, asset monitoring, waste management, weather monitoring, etc. Combining
IoT and AI is an effective way to intelligently upgrade the existing information systems.
Global economic crises can often lead to technological revolutions. For example, the
world economic crisis in 1857 led to the ﬁrst technological revolution, the world economic
crisis in 1929 led to the second technological revolution, and the economic crisis in 1987
led to the information technology revolution [
]. After the ﬁnancial crisis in 2008, smart
cities have become a global trending topic (Figure 1) with the iconic report Smart Planet
by IBM. Smart Planet advocates the full use of next-generation information technology in
all industries [
], thus giving rise to concepts such as smart cities and digital cities. In the
present day, the concept of smart cities is not clearly and statically deﬁned [
]. However, it is
clear through its interdisciplinary development that smart cities are deeply integrated with
information and communication technology (ICT) and IoT [
]. The objective of smart cities
is to enhance the efﬁciency of resource utilization, optimize city management and services,
and improve the quality of life of citizens [
]. A smart approach with the help of AI and the
IoT can be applied to smart transportation, security, energy, buildings, education, health,
and more [
]. The smart city framework contains a large number of indicators to measure
sustainability, such as social and economic sustainability, and AI and IoT technologies
also help to maintain sustainability, so Khan et al. focused on the sustainable smart city
and analyzed the challenges it faces [
]. Treude summarized the deﬁnitions proposed by
several scholars for the sustainable smart city. He argued that ﬁnding a comprehensive,
sustainability-oriented deﬁnition of a smart city is a complex challenge. Treude, therefore,
proposed a limited deﬁnition: a city is smart if it uses smart technologies to better address
21st-century challenges. If it includes sustainable development goals, then it represents
sustainable urban development .
Figure 1. Impacts on technological revolutions by global economic crises (source: IBM, 2008).
Cities continue to attract new populations, and the United Nations estimates that
by 2030, more than 60% of the global population is expected to live in large cities [
Population migration to large cities has become a common phenomenon worldwide. For
example, the current resident populations of Beijing, Shanghai, and Shenzhen are all more
than 20 million [
]. Huge population growth brings many impacts and challenges to
Sustainability 2021,13, 10983 3 of 35
urban resources and services [
]. An analysis of public reports and government documents
showed that in recent years, there has been an increasing number of smart city pilot projects
in China (Figures 2and 3). The increasing trend is indicated in Figure 2, which shows
the number of China’s smart city pilot projects, and summarizes the data from different
years and integrates them into one form. The data for different years were from several
sources, which are listed below the title of ﬁgure. They are China MOHURD, China
NDRC, and Forward the Economist. China MOHURD is the Ministry of Housing and
Urban–Rural Development of China. China NDRC is National Development and Reform
Commission. Forward the Economist is one of the popular media and academic report
analysis publishers in China. Sources provided by MOHURD and NDRC are government
documents, while sources by forwarding the Economist are public reports. Refs. [
are the lists of original data published by the cited source that are integrated into the ﬁgure.
The original data from the above sources are separate, therefore the ﬁgure summarizes
them and veriﬁes the increasing trend.
Number of smart cities with experimental policies in China (data source: China MOHURD,
2013, 2015; China NDRC, 2020; Forward the Economist, 2019, 2020, 2021).
Trends for market size and investment spending of smart cities in China (data source: CCIT,
IDC Qianzhan Industry Research Institute, 2019, 2020, 2021).
The idea of “smart” is to use information technology to drive the operation of the
city, which includes monitoring, forecasting, and real-time management. The combination
of IoT and AI can replace the traditional means of managers in the past, with IoT mainly
referring to sensors or hardware, and AI mainly referring to back-end algorithms. With
the advancement of technology and the increasing awareness of smart city residents, the
concept of smart is no longer limited to intelligence, but fully incorporates the participation
of residents or users. The mode of engagement includes not only citizen participation, but
also decision making. Decisions must be made in a fast and effective manner, often relying
on real-time feedback and behavioral changes from citizens [
]. Decisions are made
Sustainability 2021,13, 10983 4 of 35
by top-down or bottom-up approaches, are technology or community-led, and include
purposeful design or natural evolution. In well-functioning cities, citizens’ decisions are
dependent on multiple types of tools that guide them in their daily decision making [
According to IDC’s Worldwide Smart City Spending Guide released in July 2020, China’s
smart city market spending will reach USD 25.9 billion in 2020, up 12.7% year-on-year,
higher than the global average, and the second-largest spending country after the United
In recent years, due to the improvement of cloud computing technology, computers
have been able to overcome the problems that used to occur due to the lack of arithmetic
power. Artiﬁcial intelligence algorithms are being noticed by several industries due to
the development of cloud computing, including the study of smart cities. Software, ap-
plications, and plugins are usually deployed on a cloud server and operated with cloud
computing techniques. According to the investigation, for the Infrastructure as a Service
(IaaS) category, the top ﬁve companies in the Chinese public cloud computing market are
Alibaba Group (Aliyun), Tencent, China Telecom, Kingsoft, and Amazon Web Services
(AWS). However, for the Platform as a Service (PaaS) category, the top ﬁve companies in
the Chinese public cloud computing market are Alibaba Group (Aliyun), Oracle, Amazon
Web Services (AWS), Microsoft, and IBM [
]. Wu summarized that from a functional point
of view, the smart city system can be divided into a perception layer, a network layer, and
an application layer [
]. According to Cohen, there are three stages in the development of
smart cities: 1.0 Technology-Driven, 2.0 Technology-Enabled, and 3.0 Citizen Co-Creation.
Different countries and cities are currently at different stages [
], and therefore they have
different levels of adoption. The level of adoption reﬂects the degree of acceptance of
technology in an object. It is a series of driving processes, from awareness to acceptance to
use. This study aimed to examine the challenges hindering the adoption of AI and the IoT
in smart city development by evaluating the current literature using the PRISMA method
and incorporating expert opinions to select the appropriate barriers. Using the DEMATEL
model, we developed cause-and-effect relationships between key AI and IoT implemen-
tation challenges. The ﬁndings of the study will be useful to understand the cause–effect
relationships between the challenges that would help policy makers, practitioners, and
researchers to understand the effect of the challenges in building smart cities in China.
2. Development of AI and IoT for Smart Cities
2.1. The Case for Smart City Development
Singapore is considered a pioneer in the smart city movement (Qi and Shen, 2019).
Emerging technologies of IoT are used for the development of smart cities in smart ﬂeet
management, air quality monitoring, energy management, and smart agriculture needs
with the help of sensors, robots, and other cyber–physical systems. All the generated data
in this process are sent to the cloud (a central platform) for processing, and the output
generated is used to plan smart city strategies .
In the context of Industry 4.0, the combination of smart cities with IoT and AI can lead
to win–win results. Tobias’ research highlighted the importance of monitoring systems [
In New York, for example, the government has upgraded its lighting system to enable
intelligent control of dimming and turn-on times. It is also reducing lighting power by
retroﬁtting LED installations. An automated meter reading (AMR) system is used to
provide feedback on water usage, and also to monitor potential leaks in real time. Tobias
also described the representative smart waste management, a technology that is also widely
used in Singapore. As part of the smart waste management program, monitors on the bin
lids collect information about the contents and location, and transmit the information to
the waste team via a central server. The route of the waste collection team can then be
]. In Singapore, the Smart Nation vision was established in 2014 to harness
new technologies to address growing urban challenges. To date, the most developed smart
services in Singapore are the intelligent transport system (ITS), which is more than 10 years
old, and e-government, which has been developed since the early 1980s [
]. In addition,
Sustainability 2021,13, 10983 5 of 35
One Monitoring, developed by the transport department, provides services to all drivers
and vehicle owners in the city, including access to information about cabs on the road and
real-time road information, among other features .
In China, the Premier’s report “Making Science and Technology Lead China’s Sus-
tainable Development” in 2009 opened the era of smart cities. Over the past decade, the
smart city industry chain has involved interdisciplinary and multi-industries, integrating
multilevel technologies. Based on the upstream and downstream industry chains of smart
cities described by [
], this study integrated the industry ecosystems and technol-
ogy architectures involved in smart cities. Figure 4illustrates the streams (up, middle
and down) for the integrated structure of industrial chains of a smart city. The vertical
axis shows multiple layers, including upper terminal device, platform service, and the
Integrated structure of industrial chains of a smart city (data source: Qianzhan Industry
Research Institute, 2019, 2020, 2021).
As of 2019, there were more than 700 smart city pilots in China. The market size is
growing by more than 30% per year, reaching RMB 10.5 trillion in 2019. Of these, up to
94% of provincial-level cities and 71% of prefecture-level cities have launched smart city
Hangzhou proposed the City Brain system in April 2016. It is a digital interface for
citizens, including 11 systems and 48 application scenarios such as police, transportation,
cultural tourism, and health, with an average of more than 80 million data points per
]. The City Brain connects and shares data resources that were originally scattered
in various departments and isolated from each other. By establishing a high-speed urban
“CPU”, the government’s service effectiveness is continuously improved [
]. The City
Brain has taken over the signals of 1300 intersections in a 420 km
area of Hangzhou. The
functions that have been realized to facilitate people’s livelihood include convenient park-
ing, digital tourism, smart environmental protection, vegetable-planting base management,
convenient access to medical care, and many other application scenarios. Therefore, the use
of cutting-edge technologies such as big data, cloud computing, blockchain, and artiﬁcial
intelligence to promote urban management means and management modes is a way to
drive cities from digitalization to intelligence .
Shenzhen proposed the concept of “Building a Smart Shenzhen” in 2010, and is
now one of the representative smart city models in China. Shenzhen is a mega city with
more than 20 million people, so the scale of data and the way it is processed is very
important. In the dimension of a smart city, “smart” means to have data, and that the data
is connected [
]. To improve the efﬁciency of urban governance and industrial sectors, it
Sustainability 2021,13, 10983 6 of 35
allows the sharing of effective data resources across sectors, industries, and ﬁelds, so that
these integrated data and new technologies can improve capacity, output, and efﬁciency.
For example, the Construction Digital Management Center (CDMC) was developed for
metro construction sites. It enables centralized control of more than 400 construction sites
across the city and uses AI algorithms to identify safety hazards, greatly enhancing work
2.2. Features of the IoT
The Internet of Things (IoT) is a highly integrated and comprehensive use of next-
generation information technology, which is important for a new round of industrial
transformation and green, smart, and sustainable economic and social development [
New IoT applications are driving smart city initiatives around the world. The IoT is based
on the installation of sensors (RFID, IR, GPS, laser scanners, etc.) for everything and con-
necting them to the Internet for information exchange and communication through speciﬁc
protocols for intelligent identiﬁcation, location, tracking, monitoring, and management [
The IoT and IT are at the core of the hyperconnected society, which is also known as
Machine to Machine (M2M) or Internet of Everything (IoE) [
]. Theodoridis developed a
city-scale testbed for IoT and future Internet experiments [
]. Latre et al. [
the City of Things smart city testbed located in Antwerp, Belgium. The platform consists
of a multi-wireless technology network infrastructure and includes an integrated approach
that allows experimentation in three different layers: network, data, and living labs [
Sanchez deployed the Smart Santander project in the city of Santander, a typical application
of the Internet of Things in a smart city .
Big data highlight openness and connectivity [
], and the combined application with
IoT can moreover stimulate the value behind it. Chen et al. proposed a cloud platform
architecture for building energy management based on big data and distributed technol-
ogy using machine learning and environmental real-time analysis to provide technical
support for building operations [
]. Zhang et al. summarized common data mining
techniques, such as association mining, multivariable linear regression modeling, classiﬁca-
tion, clustering, and neural networks, and analyzed the data-mining techniques and the
methods and trends of applying various data-mining techniques in the ﬁeld of building
energy efﬁciency [
]. Shi et al. used the SSIS algorithm with SQL to store historical
data on energy consumption, and used Visual Studio as a development tool based on the
ASP.NET MVC framework to establish an energy consumption management platform and
energy consumption management software [
]. Yang et al. proposed a data-mining-based
method for processing energy consumption data from public buildings, including building
data sets based on historical data and classifying them according to different energy use
2.3. Implementation of AI in Smart Cities
As an important part of the smart city process, the construction industry plays a
pivotal role. Technologies based on IoT and artiﬁcial intelligence algorithms have greatly
accelerated the development of the construction industry. Artiﬁcial intelligence algorithms
are a means of bulk problem solving [
]. At the same time, intelligent energy simula-
tion tools combining parametric methods, BIM technologies, or computer programming
techniques have emerged based on the architectural design perspective .
A smart city refers to a regional scale that aggregates multiple buildings. Therefore, the
shared data of each building form the interconnection system. Building performance simu-
lation technology can be used to rationalize or reduce the energy consumption of buildings.
This method is called sensitivity analysis or parametric simulation. The combination of
optimization algorithms to analyze changes in building energy consumption by changing
several parameters simultaneously is called building performance optimization [
are two main methods used to predict energy consumption in buildings, the data-driven
approach and the physical modelling approach [
]. Due to the development of cloud
Sustainability 2021,13, 10983 7 of 35
computing and algorithms, the data-driven approach has received increasing attention
in recent years. The common machine learning algorithms are support vector machines
(SVMs), artiﬁcial neural networks (ANNs), and decision trees.
Amasyali et al. reviewed the research on data-driven building energy prediction models,
with a particular focus on reviewing the prediction scope, data attributes, and pre-processing
methods in conjunction with machine-learning algorithms. Their study proposed future re-
search directions in the field of data-driven building energy prediction. A brief overview of
existing review studies on data-driven building energy prediction was presented by considering
building type, time granularity, predicted energy consumption type, data type, element type,
and data size [
]. For the machine-learning algorithms used to train energy consumption
prediction models, the mainstream ANNs, SVMs, and DTs occupied 76% of the distribution,
while 24% of the studies used other statistical algorithms, andsome scholars also conducted
comparative studies between two or more algorithms to derive similarities and differences
in energy consumption prediction (Table 1). Nearly half (47%) of the studies focused on the
prediction of overall building energy consumption [
]. A combination of multiple algorithms
can be used to efficiently solve the problem of building energy consumption in smart cities. The
below table summarizes the literature on building performance simulation and prediction in
smart cities based on mainstream algorithms.
Summary of algorithms mentioned by previous researchers for improving building performance management in
smart city projects.
Scholars Research Topics Algorithms or Models
Sadeghi et al. Predicting residential building energy
Dong et al. Predicting residential electricity energy
Combination of ANN, SVR, LS-SVM,
Li et al. Predicting building electricity
consumption Optimized-ANN, PCA
Ahmad et al. Forecasting building electric energy
consumption ANN, SVM
Daut et al. Analysis of building electrical energy
consumption prediction Mixture of SVM and SI
Turhan et al. Comparative study of building heat load
Combination of KEP-IYTE-ESS and ANN
Qiu et al. Simulation of vacuum photovoltaic glass
and building energy consumption
Coupling ANN with light harvesting
Zhong et al. Prediction of energy consumption in
ofﬁce buildings SVM
Roldán-Blay et al. Prediction of building electric energy
consumption ANN and temperature proﬁle model
Goyal et al.  HVAC zone control for occupancy MPC
Williams et al. Predicting monthly residential energy
Massana et al. Short-term energy consumption
forecasting for nonresidential buildings MLR, MLP, SVR
Shi et al. 
Forecasting energy consumption in ofﬁce
Jovanovi´c et al. Forecasting heating energy consumption
for university campuses FFNN, RBFN, ANFIS
Bourhnane et al. Smart building energy consumption
forecasting and planning GA, ANN
Adaptive Neuro-Fuzzy Inference System (ANFIS), Artiﬁcial Neural Network (ANN), Deep Neural Network (DNN), Echo
State Network (ESN), Feed Forward Neural Network (FFNN), Multivariate Adaptive Regression Splines (MARS), Multilayer Perceptron
(MLP), Multiple Linear Regression (MLR), Model Predictive Control (MPC), Principal Components Analysis (PCA), Radial Basis Function
(RBF), Swarm Intelligence (SI), Support Vector Machine (SVM), Support Vector Regression (SVR).
Sustainability 2021,13, 10983 8 of 35
3. Literature Review and Challenges to IoT and AI Adoption in Smart Cities
The arrival of new technologies is always accompanied by opportunities and chal-
lenges. Although the concept of smart cities was only created less than 20 years ago,
the rapid iterations and upgrades have brought about many challenges as well. This
section identiﬁes and summarizes inﬂuencing factors from a systematic literature review
of published papers and studies. Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA) was applied in the review process. PRISMA is a commonly used
method that clearly presents the process and reasons of document identiﬁcation, screening,
inclusion, or exclusion, which can improve the accuracy of reviews and meta-analysis
]. The initial source data was collected from major English and Chinese academic
search engines, including Web of Science, Engineering Village, Scopus, Google Scholar,
CNKI, and Wanfang Data. By using the identiﬁed keywords “IoT/ Internet of Things”,
“AI/ Artiﬁcial Intelligence”, and “Smart City/ Smart Cities”; and “adoption”, “challenge”,
“barrier” and their relative Chinese translations with Boolean structured searching methods,
a total of 439 initial papers were collected. Then, 128 duplicated articles were removed.
After reading the title, abstract, and keywords of those collected papers and determining
whether they were aligned with the research topic, 87 of them that did not meet the criteria
were removed. Then, a second measurement was done to check if the main content of
those papers met the criteria, and whether they were articles, reviews, degree theses, or
conference proceedings. After this step, 171 articles were included for further categorizing
(see Figure 5). The ﬁnal 171 articles selected based on the above bibliographic research
were studied carefully to identify the potential challenges of AI and IoT adoption for smart
city development. After analyzing the content of these articles, 10 inﬂuencing factors
(challenges) were identiﬁed, and the supporting literature is presented in the Table 2. The
roles and interrelationships of the identiﬁed challenges were established using DEMATEL
by considering the experts’ opinions.
Literature search results using the Preferred Reporting Items for Systematic Reviews and
Meta Analyzes (PRISMA) method.
Sustainability 2021,13, 10983 9 of 35
Table 2. Ten key challenges identiﬁed in the literature, with sources.
No. Challenges (Factors) Code Reference
Lack of Infrastructure
2Insufﬁcient Funds or
Capital C2 [74–91]
Data Risks C3 [92–119]
Smart Waste and
5 Lack of Professionals C5 [141–156]
Demands C6 [157–176]
Transportation C7 [177–206]
8 Environmental Risks C8 [207–232]
Health and Education
10 Lack of Trust in AI
and IoT C10 [229,245–265]
3.1. Lack of Infrastructure
Smart cities need the latest very advanced infrastructure, and every piece of equip-
ment should be connected to the Internet to monitor it. In smart cities, connected IoT
devices collect data from the physical medium to optimize decisions to improve urban
services for citizens [
]. The growth of the population requires the need for housing,
educational institutes, hospitals, and entertainment facilities. AI and the IoT should ad-
dress the needs of infrastructure for all the people living in smart cities that will become
part of the IoT infrastructure [
]. Artiﬁcial intelligence has important implications for
achieving sustainable development, which is directly related to infrastructure development
in emerging economies [
]. In addition, AI and the IoT should take care of utilizing
and connecting the old (existing) infrastructure for multiple purposes. IoT technologies
have contributed signiﬁcantly to most of the detailed aspects of smart city technologies
and infrastructures. As the basic concepts and ideas of IoT technologies are shared with
smart city technologies and infrastructures, there are substantial business opportunities
and extensive growth potential .
3.2. Insufﬁcient Funds or Capital
A study showed that funds and budget were the main factors to begin any project [
The government or local authorities should have sufficient funds to design, develop, and
continue smart city planning. Unavailability of funds delays the implementation of projects,
which again causes an increase in project costs. Abdalla considers the lack of investment
and capital funding as the main threat to smart city strategies [
]. AI and IoT techniques
should help in prioritizing the projects based on the severity, requirements, timelines, and other
parameters, and optimally allocate the budgets using the best optimization techniques [
To effectively address this issue, the government may need to obtain additional funding from
entities in the private sector that are interested in these smart city projects .
3.3. Cybersecurity and Data Risks
Smart cities’ interconnective networks create huge amounts of data containing rich
information; bring innovation; and connect governments, industries and citizens. The data
create a foundation for operating cities that will make them more effective and sustain-
able. Sharing and storing large amounts of actionable data also raises many concerns and
challenges. This might include data related to citizens private information, government
documents, and the information of all private organizations. On the other hand, cyber
Sustainability 2021,13, 10983 10 of 35
insecurity raises concerns about data privacy and threats to smart city systems [
For example, there was a cyber attack on a top US pipeline network company, and the
company was forced to shut down its operations for few days and paid a huge amount
to the hackers to resume its operations [
]. On 20 August 2021, China passed a new
personal data privacy law that will take effect on 1 November 2021 [
]. This law requires
that separate consent be obtained from individuals for the processing of sensitive personal
information such as biometric, medical health, ﬁnancial accounts, and whereabouts. It also
prohibits the collection of sensitive information from individuals in certain special cases of
writing, such as facial information and biological information. This might create challenges
for the tech giants to handle the information without mismanagement and misuse. Due
to the increasing volume of sensors and their data, robust connectivity technology is a
requirement for success. [
] Without powerful citywide coverage, the success of such
a project is more than unlikely. Smart cities will house millions of people, and managing
the people data, analyzing it, and preventing cyber-attacks is a challenge for AI and IoT.
Therefore, an integrated approach is needed to deal with data security, user privacy, and
trust in smart cities [
]. Meanwhile, the challenge of cybersecurity and data risks and the
associated responsibilities must be shared by all parties involved in the smart city process,
including city managers, residents, and the society itself. Cybersecurity and data risks com-
prise various types, and a cyber attack is one of the aspects. Therefore, effective measures
have to be taken for smart cities to elaborate methods to detect attacks. Pasqualetti et al.
have designed a centralized and distributed attack detection and identiﬁcation monitor
by describing monitoring limitations [
]. At the same time, they proposed a framework
for cyber–physical systems, attacks, and monitoring. Carl et al. explored the capabili-
ties of DoS attacks by analyzing denial-of-service attack detection techniques, including
activity-based analysis, change-point detection, and wavelet-based signal analysis [
They argued that the detectors used for testing do not solve the problem independently,
and that a combination of methods could produce better results in the future. Salem et al.
surveyed insider attack detection and solutions in the literature [
]. Their approach was
to classify insider attack detection cases. By summarizing the current common methods
and techniques for insider attack detection, future research directions were proposed. Data
security is particularly important in the medical ﬁeld, and Liag et al. developed an at-
tack detection model for medical surveillance systems using Internet Protocol (IP) virtual
private networks (VPNs) over optical transport networks. They proposed a multilayer
network architecture and a lightweight implementation of the procedure that enabled
remote access to medical devices .
3.4. Smart Waste and Hygiene Management
There is a growing interest in the potential application of these technologies in manu-
facturing systems and municipal services to ensure ﬂexibility and efﬁciency [
waste still remains a major concern for many countries. Waste management from inception
to disposal is one of the key challenges faced by municipal companies worldwide [
With the current phase of living styles in which most of the food and other items are
wrapped with plastic or paper packaging, handling the waste produced by millions of
people is a huge task. Waste collection must be completed within a speciﬁed time frame, as
cities generate waste at an alarming rate and need to collect it more smartly [
waste management companies have developed AI-enabled waste segregation techniques
that automatically separate different kinds of waste (e.g., paper, plastics, glassware, metals,
etc.) automatically without human intervention. AI and the IoT should address the issues
associated with collection, transportation, treatment, recycling, and disposal in waste
management. For example, the intelligent disposal process was achieved through Ant
colony optimization (ACO) technology. The entire process can be monitored centrally, thus
providing high-quality services to the citizens of smart cities .
Sustainability 2021,13, 10983 11 of 35
3.5. Lack of Professionals
The adoption of AI and the IoT require highly skilled professionals. Without proper
experience and knowledge, organizations always misunderstand the beneﬁts of these
technologies. One of the current challenges facing smart cities is the lack of professionals
with knowledge of computer technology and knowledge of different ﬁelds [141,142]. The
unavailability of skilled professionals lags behind the adoption of AI and the IoT in
sustainable smart city development [
]. Novák et al. provided a detailed analysis of
the lack of professionals in the Middle East, Amsterdam, and Hungary [
attraction and adoption of professionals will be particularly important in the post-pandemic
era. The epidemic has already had an economic impact on the city, and if there is a chronic
shortage, this will make it difﬁcult to achieve intelligent urban management, and will affect
the efﬁciency and quality assurance of smart city operations. For the construction of smart
cities, the knowledge of professionals helps to achieve desired goals .
3.6. Managing Energy Demands
Smart devices are integrated and converted from everyday objects with advanced
computing algorithms, and become the intelligent terminals that transfer data with a cloud
server or other devices [
]. Thus, it requires a huge amount of energy to maintain the
smart city [
]. Providing energy for a city’s needs is a challenging task, and with increas-
ing attention shifting towards renewable energy sources, several cities have struggled in
migrating to renewable energy sources, On the other hand, energy demand and costs are
increasing over time [
]. Domestic energy consumption is exponentially increasing due
to the use of modern televisions, air conditioners, washing machines, smartphones, and
computers. The technological advances and changes in consumer habits are leading to
higher energy demands [
], and now the energy producers are looking for help from
AI and the IoT to optimize the distribution of energy demands through methods such as
automating streetlights, increasing unit electricity prices during peak times, and updating
old equipment with modern equipment. AI implies new rules for organizing activities.
There is a need to improve the design, deployment, and production of energy infrastructure
to meet multiple challenges [
]. One solution to this challenge is to install solar power
generation facilities for homes to help alleviate the energy consumption problem. Modern
homes can also help boost the value of ﬁxed assets by installing auxiliary solar power
systems. Since solar panels do not generate electricity at night, homes will begin to draw
power from the main grid as usual after the sun goes down. The Mississippi Power Com-
pany has announced a partnership with Tesla to create the world’s ﬁrst smart community
with a Tesla residential solar+storage system [
]. Anbari argues that the combined use of
solar and wind resources in a smart city environment can help energy companies to be able
to meet the energy needs of cities [
]. Northﬁeld argues that there is a need to maximize
the use of the regional grids powered by solar, wind, or hydro energy so that the growing
energy demands can be met locally instead of relying on a central supply line .
3.7. Managing Transportation
As a noteworthy part of today’s economy, transportation accounts for 6–12% of a
country’s GDP that can be created. Although transportation has greatly improved our
lives, many excessive problems remain unresolved [
]. Transportation has become the
second-largest carbon-emitting sector due to inefﬁciencies. Current transportation methods
are heavily dependent on crude oil products such as petroleum, diesel, etc. Electric cars are
a good alternative to combat the emissions-related problems. Electric vehicles generate
the necessary power for driving using a battery pack and electric motors. The charging
requirements of electric vehicles can be met if existing fuel stations are upgraded to hybrid
modes that provide petroleum products as well as e-charging stations. To avoid trafﬁc
congestion, it is important to design a city to minimize the public daily transportation
requirement that can bridge the gap to reduce residents’ travel times. One of the main
branches of smart cities is smart transportation [
]. There is no smart city without a
Sustainability 2021,13, 10983 12 of 35
reliable and efﬁcient transportation system. This necessity makes intelligent transportation
systems (ITSs) a key component of any smart city concept [
]. This affects not only
smart transportation, but also the environment [
]. Saroj et al. developed a real-time,
data-driven smart transportation simulation for smart cities. This simulation model was
used to evaluate and visualize the feasibility of network performance metrics to provide
dynamic operational feedback in a real-world, big data environment [
]. The machine-
learning techniques that are an integral part of AI should be capable enough to analyze
the past data from public and private transportation activities to analyze the root where
frequent congestion occurs or where most accidents occur, the reasons for the accidents,
and preventive measures to address these issues. In China, besides the smart taxi dispatch
system for automobiles, there are also smart systems for bicycles, which is known as the
shared bicycle. To improve the efﬁciency of shared bicycle use, the algorithm will propose
a reasonable scheduling plan based on real-time road conditions, after which it will be
carried out manually. Bicycle sharing is a solution to the “last mile” problem for residents,
as many users choose to ride bicycles as an alternative to walking short distances.
3.8. Environmental Risks
Cities are becoming increasingly vulnerable to environmental risks and climate
]. In July 2021, several European countries and China experienced severe ﬂoods
that caused millions of dollars of property damage and loss of people’s lives [
Smart cities should have highly responsive and agile disaster management systems such as
weather monitoring, and alerting people regarding preventive measures to reduce pollution
levels. Due to the increase in the number of inhabitants, there is a constant need to support
economic growth to the point of creating environmental risks [
]. Large population
concentrations also result in an increase in pollution from transportation, single-use plas-
tics, and other types of waste, which largely contributes to environmental pollution [
Large-scale housing needs also pose several threats to the environment [
]. AI and
IoT can be used in the design phase to minimize environmental risks. Automated drones
have been used in various ﬁelds such as environmental risk detection and capture, traf-
ﬁc regulation, and air pollution monitoring. Air quality sensors on publicly accessible
online platforms can support the measurement of environmental risks [
environments improve competitiveness and respond to environmental risks to make cities
3.9. Managing Public Health and Education
By 2020, 86% of health-related companies will spend an average of USD 54 million
on artiﬁcial intelligence and healthcare [
]. In China especially, which is home to the
world’s largest population, providing healthcare facilities to all people is a challenge. It is
difﬁcult to provide the necessary medical requirements to all age groups without assessing
their past health conditions. AI should learn to assess the health condition of a patient
using their past medical records and present physical situation. The digitalization of
medical records on a central server will help governments to assess the data anytime and
anywhere in the country. This would help medical staff to investigate the patients and
treat them carefully. Besides using a central server to store the medical records, there are
also alternative methods using blockchain. Holbl et al. [
] have studied the applications
of blockchain in healthcare, and marked it as an enabler for decentralized healthcare
management. Kong proposed an AI-based model for online triage in sustainable smart city
]. IoT-enabled medical equipment such as operation theatres, diagnostic tools,
and smart equipment should assist in managing health. For example, during the COVID-
19 pandemic, countries adopted measures for effective protocols for sharing health data.
Allam argued that only by designing different smart city products to support standardized
protocols that allow seamless communication between them can better urban structural
risk management decisions be provided [
]. The ﬂip side of the sensible use of new
technology is the misuse of personal health data. Measures involving data collection and
Sustainability 2021,13, 10983 13 of 35
use all inevitably trigger data breaches or misuse. The examples include personal journeys
during the COVID-19 pandemic or records of visits for difﬁcult medical conditions, which
can compromise the rights of patients or even the general population. A new law enacted
in China in August 2021 also prevents such incidents from occurring.
Education is also a primary need for the citizens. AI should help in the design
and development of educational programs according to industrial and academic needs.
Continuous revision of syllabi, assessment of student skills, and analyzing the industrial job
requirements of developing skills in students are some examples in which AI can play a role.
In this era of data abundance and exponential growth of new knowledge development, IoT
is challenging institutions to rethink teaching and learning in the global marketplace [
The use of IoT can exchange and utilize information in very appropriate ways to facilitate
student engagement and interaction with their peers and teachers. Mahmood explored the
applicability of Raspberry Pi development boards or single-board computers in teaching
enhancement, IoT technologies, and environments, and proposed a low-cost, efﬁcient,
and ﬂexible educational platform [
]. Nuseir focused on entrepreneurial intentions
in smart cities and elucidated the relationship between entrepreneurial competencies,
entrepreneurial self-efﬁcacy, and entrepreneurial intentions .
3.10. Lack of Trust in AI and IoT
Trust is generally located at the level of interpersonal relationships. In modern life,
more particularly in smart cities, trust between people is increasingly systemic trust [
The lack of trust in AI and the IoT might slow their implementation in smart city develop-
ment. As mentioned by researchers in [
], building the trust AI models that can trans-
form social, political, and business environments and can help people in decision-making
processes will remove any negative opinions about the usage of AI and IoT technologies.
Government agencies should try to create awareness by spreading positive news about
using AI in sustainable practices, maintaining transparency, and publishing some case
studies that might improve the trust. The report “Artiﬁcial Intelligence and Life in 2030:
One Hundred Year Study on Artiﬁcial Intelligence” by Stone et.al analyzed in detail how
AI could impact a typical North American city in 2030 [
]. Each of these chapters and
predictions for the next 15 years reﬂect the different AI impacts and challenges. Examples
include the difﬁculty of creating secure and reliable hardware, the challenge of gaining
public trust, and the social and societal risks of reduced human interaction. Poola men-
tioned simple tasks that AI can perform, such as facial recognition and car driving, as well
as complex tasks such as developing a super AI that improves itself and triggers an intelli-
gence explosion. It is also possible to use advanced technologies to eradicate poverty while
also developing appropriate precautions for AI [
]. Kamble et.al provided an overview
of AI and its applications in human life [
]. For example, they explored the current use of
AI techniques in cyber intrusions to protect computers and communication networks from
intruders. In the medical ﬁeld, technologies are used to improve hospital inpatient care.
Smart city data management systems provide the collected data and generate revenue,
but the system should also maintain people’s trust while doing so [
]. Managing and
building people trust is a key challenge in sustainable smart city development. Data-
and citizen-centric smart city governance is essentially built trust [
]. However, Falco
proposed the concept of participatory AI, and he argued that engaging the public will not
only increase community trust in AI [
]. Hwang analyzed the evolution of smart cities
in Korea. He argued that many Koreans have lost trust in smart city development due to
their experiences with u-City [
]. However, the birth of new technologies can change this
situation to some extent. The contribution made by the AEC (Architecture, Engineering,
Construction) industry is indispensable to the process of building smart cities. Solearth and
Zhao proposed the discipline of AEC-IT that integrates the AEC industry with advanced
Information Technology in the era of the AI age [
]. Solearth and Zhao proposed a new
view that currently, the stage of the AEC industry or architectural design sector is in the
second half of the industry cycle, comparing with hundreds of years’ development. AI and
Sustainability 2021,13, 10983 14 of 35
IoT play important roles in the process of AEC enterprises, from the original purely design
service to DBOB mode (Design, Build, Operate, Brand) .
This section uses a systematic literature review approach to discuss the use of AI
and IoT in smart cities. A total of 10 challenges were identiﬁed in the literature using
the PRISMA method, and the number of articles published in the academic ﬁeld and the
number of smart city pilot projects were found to be increasing in the last few years. This
growth pattern also was in line with the evolution of new technologies combined with
the development and exploration of traditional industries. Moreover, the importance of
algorithms, especially artiﬁcial intelligence algorithms, in this process can be reﬂected by
combining the deﬁnition and use of smart devices. Therefore, how to develop efﬁcient and
secure algorithms is another hot research area that requires more attention. The importance
of smart devices and algorithms is reﬂected by the fact that they are relevant to each of the
inﬂuencing factors. Whether it is infrastructure, or water management, or health education,
they are all relevant. The acquired inﬂuences and challenges will be investigated and
analyzed later to determine the case of adoption.
4. Research Methodology
This section discusses the research methodology used in the study. We applied the
Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to analyze and to
identify the interrelationships between the challenges that are hindering the adoption of
AI and the IoT in sustainable smart city development. DEMATEL was ﬁrst employed in
1976, and it has managed to solve many global complex problems by considering experts’
attitudes, and has become a widespread technique in decision-making problems [
DEMATEL is widely used in analyzing the barriers, challenges, and enablers in multiple
disciplines. Recent studies have used DEMATEL for analyzing barriers in smart sustainable
], adoption of IoT [
], online food consumption [
], and supply-chain
risk management [
]. The above literature emphasized the importance of DEMATEL
in analyzing relationships between the challenges in various industries [61,328–331].
Figure 6shows the various steps of the proposed methodology. There were three
stages in the methodology. In stage 1, challenges for adoption of AI and IoT adoption in
smart city development were identiﬁed using an extensive literature review according to
the PRISMA method; in stage 2, industry experts having knowledge about the smart cities
were contacted for collecting information about the challenges; and in stage 3, the collected
data were analyzed, and the inter-relationships and ranking of the challenges are presented
using the DEMATEL technique. Table 3presents the proﬁle of industry experts contacted in
this study. A total of 10 experts from industries, government organizations, and universities
were consulted for the data collection process. The data collection sheet was prepared in
Microsoft Excel, and printed copies of the sheet were supplied to the experts. In some
cases, the data sheet was sent over email to obtain the responses. The data analysis was
done on a Windows-operated computer with an Intel i5 8th-generation processor with 8
GB of RAM and a 1 TB hard disk. The experts’ selection was done based on a convenience
sampling method. Experts with a bachelor’s degree and at least two years of experience
in domain knowledge of the environment, architecture, and IT roles in government and
private organizations were considered. Experts with research publications and research
interests in AI, smart cities, IoT technologies, and a willingness to share information were
given preference. The data was collected from July 2021–August 2021.
Sustainability 2021,13, 10983 15 of 35
Figure 6. Roadmap of the research methodology.
Table 3. The linguistics evaluation for the assessments of the responders.
No. Years of
Experience Education Level Position in Organization Type of Industry
1 6 PhD in Architecture Associate Professor University
2 4 Master of Education Senior Lecturer University
3 9 Master of Science Start-up Founder Technical Service
4 11 Bachelor of Economics Analyst Manager Business Consultancy
5 5 Bachelor of Information Systems Technical Manager IT
6 3 Master of Engineering Intern Construction
7 12 Master of Engineering Cost Supervisor Construction
8 7 Bachelor of Science in
Environmental Studies Administrator Research Institute
9 5 Bachelor of Art Senior Architect Architecture
10 2 Bachelor of Engineering Planner Government
Various Steps in DEMATEL Methodology (Stage 3).
In the present study, 10 key challenges, including “Lack of infrastructure (C1)”, “In-
sufﬁcient funds or capital (C2)”, “Cybersecurity and data risks (C3)”, “Smart waste and
hygiene management (C4)”, “Lack of professionals (C5)”, “Managing energy demands
(C6)”, “Managing transportation (C7)”, “Environmental risks (C8)”, “Managing public
health and education (C9)”, and “Lack of trust in AI and IoT (C10)”, in the hindering of
AI and IoT in the evolution of long-lasting smart cities were pointed out by reviewing
substantial literature, as depicted in Table 2. A DEMATEL method (see Figure 5) was
utilized to establish and analyze the causal linkages between the challenges.
We approached 10 industry experts (see Table 1for proﬁle details) to obtain infor-
mation regarding the challenges to AI and IoT adoption for smart city development, and
created a direct-relation matrix, M1, using the experts’ inputs and computing it according
to the formula presented in Equation (1). The causality effect of one variable (challenge)
on the other variable was collected using the matrix provided in Equation (1). For our
convenience, the words, “challenge” and “variable” were interchangeable in the study.
0a12 a13 a14 a15 . . . a1n
a21 0 a23 a24 a25 . . . a2n
a31 a32 0 a34 a35 . . . a3n
. . . . . . . . . . . . . . . . . . . . .
a(n−1)1a(n−2)2a(n−3)3 . . . . . . 0 a(n−1)n
an1an2an3 ... ... an(n−1)0
Sustainability 2021,13, 10983 16 of 35
represents the complete matrix information collected from one expert, N repre-
sents the number of challenges, and P represents the number of experts (10) contacted for
Based on the scale provided in Table 4, the experts were asked to provide their
recommendations in the given format. A ﬁve-point scale running from 0–4 was used
by earlier studies [
] for analyzing the barriers using DEMATEL. This scale ran
from 0 to 4 (0—no inﬂuence, 1—very low inﬂuence, 2—low inﬂuence, 3—high inﬂuence,
and 4—very high inﬂuence), as depicted in Table 4. We computed the pairwise effect of
each challenge with the other using this inﬂuence scale. For example, to determine the
inﬂuence between “Insufﬁcient funds or capital (C2)” on “Lack of infrastructure (C1)”
all 10 of the experts’ inputs from the data collection table were extracted, and the values
were 4, 1, 0, 4, 1, 4, 3, 4, 4, and 4, respectively. When computing an arithmetic mean of all
experts’ opinions, we get
2.9, and this value was conﬁned
to a cell (1, 2) of a direct relation matrix (DRM), which is represented in Table 5. The
value 2.9 indicated a signiﬁcant effect of lack of funds on lack of infrastructure. Similarly,
the inﬂuence between “Managing transportation (C7)” and “Insufﬁcient funds or capital
(C2)” could be calculated as
3.6, and was conﬁned to a cell
(2, 7) of the direct relationship matrix in Table 5, which also implied a signiﬁcant effect
of transportation management and the availability of funds. For another computation
that showed the inﬂuence between “Managing public health and education (C9)” and
“Insufﬁcient funds or capital (C2)”, we get
1.3, which was
speciﬁed in cell (2, 9) of DRM, and indicated a low or moderate relationship between the
variables. As the same challenge could not have an effect on its own, all diagonal elements
in the DRM were set to zero. Using the above procedure, all cells of DRM were calculated,
and the results are shown in Table 5.
Table 4. The scale used for data collection for DEMATEL.
Please Put a Suitable Value in the Matrix Based on Your Experience Score
No Inﬂuence 0
Low Inﬂuence 1
Moderate Inﬂuence 2
High Inﬂuence 3
Very High Inﬂuence 4
Step 1: Formation of a direct relationship matrix (DRM).
Table 5. The direct relationship matrix (M1).
Challenges C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
C1 0 2.6 2.3 2.6 2.5 2.7 2.6 2.8 2.7 2.8
C2 2.9 0 2 2.1 3.1 3 2.7 3.3 2.3 2.2
C3 1.6 2.3 0 2.6 2.5 2.4 1.9 1.9 2.2 2.1
C4 2.1 2.7 1.6 0 2.7 1.9 2.1 3 2.9 2
C5 2.6 1.9 2 2.8 0 3 2.9 2.6 2.8 2.2
C6 2.3 2.5 1.4 2.2 2.6 0 2.7 2.5 2.3 2
C7 2.1 3.6 2.1 2.7 2 2.9 0 2.3 2.5 1.9
C8 2.1 2.5 2.3 1.9 2.5 2.7 2.8 0 2.5 1.8
C9 2.5 1.3 1.9 2.6 2.8 2.5 2.5 2.3 0 2.8
C10 1.8 1.9 3.2 2.8 2.2 2.2 2.8 3.4 3 0
Step 2: Formation of a normalized direct relation matrix (NRM).
To prepare the data for the DEMATEL analysis, an NRM was prepared. The normal-
ized direct-relation matrix “M2” (see Table 6) was constructed using the data based on
the original direct-relation matrix. Each cell was divided by the sum of all cell values in
that corresponding row. For example, for the DRM in row 1, the summation of all values
(2.6, 2.3, 2.6, 2.5, 2.7, 2.6, 2.8, 2.7, and 2.8) equaled 23.6. Each cell in row 1 of Table 5was
Sustainability 2021,13, 10983 17 of 35
divided by this corresponding row total of 23.6. Then, the normalized values were obtained.
Similarly, for all rows of the DRM, the normalization is done to obtain the NRM, which is
shown in Table 6.
Table 6. The normalized direct-relation matrix (M2).
Challenges C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
0.1079 0.0954 0.1079 0.1037
0.0871 0.1286 0.1245
0.1369 0.0954 0.0913
0.1079 0.1037 0.0996 0.0788 0.0788 0.0913 0.0871
0.0788 0.0871 0.1245 0.1203
0.1245 0.1203 0.1079 0.1162 0.0913
0.0954 0.1037 0.0581 0.0913 0.1079
0.0871 0.1494 0.0871
0.0954 0.1037 0.0788
0.0871 0.1037 0.0954 0.0788 0.1037
0.1037 0.0539 0.0788 0.1079 0.1162 0.1037 0.1037 0.0954
0.0747 0.0788 0.1328 0.1162 0.0913 0.0913 0.1162 0.1411 0.1245
Step 3: Calculation of the total relation matrix (TRM).
Further, by taking into account the normalized direct-relation matrix (M2), a total-
relation matrix (M3) was calculated by using the following equation:
where Trepresents the notation for TRM, X denotes NRM, and I denotes the identity
matrix. Then using the procedure mentioned in [
], threshold values (
), D, and R were
calculated, where D represents the sum of the values in a row, and R represents the sum of
values in a column. Equations (3)–(5) were used to calculate the values D, R, αvalues:
D=[dii ]n×1= [
where i and j are the respective values for the rows and columns in the TRM, and n is
the number of challenges in the study. The threshold values were used to determine
the important and inconsequential challenges based on the threshold value. The
was obtained as 0.9752, and the cell values in the TRM matrix that were smaller than the
value (0.9752) were replaced with the value “zero (0)” in the
-cut TRM (see
Table 7), and were ignored for further DEMATEL processing. The
value of challenges,
which was equal to or greater than the threshold value, was updated in a new matrix called
α- cut TRM (Table 8).
Sustainability 2021,13, 10983 18 of 35
Table 7. The total direct-relation matrix (M3).
Challenges C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
1.0695 1.0936 1.1181 1.1042 1.1496 1.1117
0.9062 1.0534 1.1153
1.1099 1.1675 1.0992 0.9574
0.6939 0.9126 0.9325 0.9431 0.9169 0.9496 1.0925 0.9374
0.8815 0.9441 0.8093 0.8755 1.0023 0.9909
1.0532 1.0186 0.8634
0.9802 0.9917 1.0444 0.9663 1.0946
1.1074 1.0818 0.9276
0.8721 0.9221 0.7871 0.9419
0.8992 0.9905 1.0172 0.9797 0.8472
0.9182 1.0153 0.8595 1.0159 1.0201 1.0668 0.9485 1.0727 1.0457 0.8954
0.8814 0.9396 0.8334 0.9499 0.9954 1.0192 1.0121 0.9415 1.0047 0.8566
0.8984 0.9023 0.8251
1.0348 0.9176 0.8959
0.9921 0.9308 1.0574 1.0635 1.0805 1.0901 1.1476 1.1018 0.8549
Table 8. The α-cut total direct-relation matrix (M4).
Challenges C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
1.0695 1.0936 1.1181 1.1042 1.1496 1.1117
1.1099 1.1675 1.0992
C3 0 0 0 0 0 0 0
C4 0 0 0 0
0.9802 0.1147 1.0444
C6 0 0 0 0 0.98 0
0.9905 1.0172 0.9797
1.0159 1.0201 1.0668
C8 0 0 0 0
0.9954 1.0192 1.0121
C9 0 0 0 0.98
1.0574 1.0635 1.0805 1.0901 1.1476 1.1018
-cut total DRM was prepared by removing the matrix values less than the threshold
value (0.9752), all the values that were less than
were assigned a 0 value, and all the values
other than zero shown in Table 8were considered to build the directed graph (digraph). The
arrows in the digraph (see Figure 7) indicate the relationships between the challenges.
Figure 7. Directed graph showing the relationships between the challenges.
For example, the cybersecurity risks category is in the rightmost corner of the map,
and it has only one arrow pointing towards it, which indicates that the challenge “lack of
professionals (C5)” inﬂuenced “cybersecurity and data risks (C3)”. In addition, “cybersecu-
rity and data risk (C3)” has two outgoing arrows towards “managing public health and
education (C9)” and “lack of trust in AI and IoT (C10)”. The overall network graph analysis
concluded that the other challenges had much less inﬂuence on the cybersecurity risks. The
bidirectional arrows represent the dual relationship between the given set of challenges.
The ﬁndings of R and D corroborated the degree of relationship effect among each critical
Sustainability 2021,13, 10983 19 of 35
challenge. While D + R represents the signiﬁcance of a particular challenge, D-R shows the
net inﬂuence of the provided challenge. The D + R score is plotted on the X-axis, and the
D-R score is plotted on the Y-axis to plot the cause–effect challenges on the scatter plot. For
instance, for computations of D + R and D-R for challenge C1; the score of D was 10.462
and the score of R was 9.0393, so adding them together (D + R) equaled 19.50125, whereas
subtracting them (D-R) equaled 1.4226752. The challenges with high D-R values had a high
importance, and were named as the “cause” group or causal challenges, and the challenges
with low D-R scores were less signiﬁcant and inﬂuenced by the cause group challenges,
and were named as the “effect” group challenges. All results are presented in Table 9.
Table 9. Prominence and relation results obtained by using the DEMATEL method.
Criteria Name Code D Score R Score D + R Score D-R
Lack of infrastructure C1 10.462 9.0393 19.50125 1.4226752 Cause
Insufﬁcient funds or capital C2 10.477 9.5411 20.01761 0.9353911 Cause
Cybersecurity risks C3 8.7706 8.4383 17.20887 0.3322851 Cause
Smart Waste and hygiene management C4 9.4267 9.9004 19.32717 −0.473676 Effect
Lack of professionals C5 10.116 10.179 20.29484 −0.062452 Effect
Managing energy demands C6 9.2369 10.359 19.59631 −1.122412 Effect
Managing transportation C7 9.8581 10.248 20.1064 −0.390191 Effect
Environmental risks C8 9.434 10.641 20.07508 −1.207166 Effect
Managing public health and education C9 9.4868 10.292 19.77877 −0.805249 Effect
Lack of trust in AI, IoT C10 10.258 8.887 19.14481 1.3707933 Cause
As shown in Table 9, the barrier with a D–R value less than zero was identiﬁed as an
effective group, while a barrier with more than the D–R value fell under the cause group.
Based on the DEMATEL results as shown in Table 9and Figure 8, the causal interactions
and the degrees of inﬂuence among the AI and IoT adoption challenges in the smart city
development are explained as follows. The challenges “C1, C2, C3, C10” were the main
challenges affecting the implementation of various challenges for adoption of AI and the
IoT in sustainable smart city development. Figure 8displays the differentiation between
the cause and effect groups. The challenges above the horizontal axis fell under the “cause”
group, while the challenges below the horizontal line fell under the “effect” group.
Figure 7displays the relationship between the cause–effect challenges; whereas the
red colored solid lines show the strong relationship between the challenges, the red colored
dashed lines indicate a moderate relationship, and the black colored dotted lines show
the weak relationships. Researchers and organizations should assign prime importance
to strong relationships, as understanding the connection between the challenges will
help to overcome issues associated with the adoption of AI and the IoT in smart city
development. To understand the relationships between the various challenges, we used
NRM (see Figure 7), which displayed the weak, moderate, and strong causal linkages
between all 10 challenges. The cause–effect relationships and their importance is explained
in Section 5.
Sustainability 2021,13, 10983 20 of 35
Figure 8. The cause–effect diagram.
5. Results and Discussion
The following section summarizes the ﬁndings based on the causal diagram (see
Figures 6and 7). As shown in Table 9, the challenges with a D–R value less than zero were
identiﬁed as the “effect” group, while challenges with more than the D–R value fell under
the “cause” group. The challenges, including Lack of infrastructure (C1), Insufﬁcient funds
or capital (C2), Cybersecurity risks (C3), and Lack of trust in AI, IoT (C10) were classiﬁed
into the cause criteria group, while affecting the remaining challenges, which included
Smart Waste and hygiene management (C4), Lack of professionals (C5), Managing energy
demands (C6), Managing transportation (C7), Environmental risks (C8), and Managing
public health and education (C9). Because cause variables have an impact on the effect
group criterion, they should be given more attention while planning AI and IoT tech-
nologies in smart city development. The effect group challenges, although they had less
dominance over the other challenges, were highly inﬂuenced by the cause group challenges.
As mentioned in [
], cause group factors will have a strong relationship and high
prominence. The factors that fall under the cause group are driving factors. The factors
directly linked with cause group factors will have a high inﬂuence on these variables, and
they have a strong relationship and low prominence. The factors (barriers) that are not
connected to other factors will have no inﬂuence on or prominence in other factors.
The most important challenge in adopting AI and the IoT to achieve a large competitive
factor in a sustainable smart city was “Insufﬁcient funds or capital (C2)”, with the greatest
D-R score of 1.422, implying that (C2) should be given greater emphasis in the entire system
of IoT and AI implementation when designing an advanced city. This ﬁnding held true
because recent studies have identiﬁed the unavailability of funds or ﬁnancial resources
as a major constraint for smart city transformation projects [
]. Furthermore, Table 9
reveals that the important effect degree of (C1) was 10.462, which ranked second-highest
among all causative factors. In general, (C1) was a major element that requires further
consideration during the implementation phase, and this ﬁnding also was supported by the
]. With the second-greatest D-R value, “Lack of trust in IoT and AI (C10)” had
a substantial inﬂuence on the other challenges. Lack of trust creates a barrier for building
trust and promoting the use of AI and the IoT in smart city projects. One more important
challenge identiﬁed from the analysis was “Cybersecurity risks (C3)”, with a D-R value of
0.3352, which also held in the context of smart city development. The interconnection and
speed of the Internet and availability of vast public data makes smart cities vulnerable to
cyber security attacks. An example of such a cyber security attack in the USA [
discussed in the introduction section of this study.
If the value of D-R was negative, the perspective belonged to the impact group (effects),
and they were heavily inﬂuenced by cause group challenges. Furthermore, in terms of
Sustainability 2021,13, 10983 21 of 35
notable effect degree, (C6) and (C8) were highly inﬂuenced by the cause group challenges.
Similarly, the remaining challenges C4, C7, and C9 had low D-R values, which implied a
signiﬁcant low importance of these challenges in the smart city development in the context
of AI and IoT adoption. In this study, “Environmental risks (C8)” had an R value of 10.641,
the highest out of all challenges. Furthermore, when contrasted with other variables in
the effect group, its D-R value was quite high (
1.2071). To understanding this trade-off
clearly, a cause–effect diagram was used. In Figure 8, it can be observed that the challenge
regarding environmental risks fell on the right side of the graph, which implied that even
though the C8 challenge fell under the effect group, it had a substantial inﬂuence on the
other elements. The D + R for “Managing transportation (C7)” was the second-largest in
the entire procedure. Its D-R value was quite low, but “Managing energy demand (C6)”
and “Managing public health and education (C9)” had the second- and third-highest R
values of 10.359 and 10.292, respectively, as well as the second- and third-highest D-R
0.8052, respectively. Both also can be considered on a priority
basis. However, their D+R scores were lower than the other effect group criteria. The
other components had mediated R values, indicating a high degree of effect. “Lack of
professionals (C5)” had a negative inﬂuence on the system, since its D-R value was
Other challenges had a major inﬂuence on it. The digraph of net cause and effect is shown
in Figure 7, and it was used to demonstrate the relationship.
In addition, based on the M4 matrix results displayed in Table 6, we created a causal
inter-relationship graph of the implementation of AI and IoT challenges in the development
of the smart city and the interaction among these; key challenges are shown in Figure 7.
The arrow-headed lines indicate the causal interactions among each pair of challenges. The
red-colored labels are causal challenges, and the blue-colored labels represent the effect
group challenges. It was found that “Lack of professional (C5)” had strong interactions with
another challenge, “Managing energy demands (C6)”, while “Managing transportation
(C7)”, “Environmental risks (C8)”, and “Managing public health and education (C9)” were
further related by highly inﬂuencing, or having more interactions with, other challenges to
the implementation of the IoT and AI in the development of a smart city (see Table 9).
According to this study, “Lack of infrastructure (C1)”, “Insufﬁcient funds (C2)”,
“Cyber security risks (C3)”, “Lack of trust on AI, IoT models (C10)” were highly dominant
challenges that affected the other challenges. The organizations that are planning to
implement AI and the IoT in smart city development should focus on these challenges
ﬁrst, as overcoming these challenges will facilitate the adoption process. The ﬁndings of
the study will help to identify important challenges, and the relationship and inﬂuence
of challenges also can be extracted. As a result, researchers and policymakers researching
sustainable smart city development must address the major challenges to implementing
IoT and AI.
6. Conclusions and Future Discussion
The concept of smart cities is becoming important to providing sustainable living
conditions to people. Achieving sustainability is difﬁcult due to the involvement of multiple
factors. AI and IoT technologies can transform challenges and offer potential solutions
to the issues perceived by society and regulatory bodies. This study aimed to identify
and analyze the effect of important challenges that are hindering the adoption of AI and
the IoT in sustainable smart city development. Initially, the challenges were extracted
from an extensive literature review, then the DEMATEL method was used to analyze the
relationship of causes–effects between the identiﬁed challenges. Data were collected from
experts in diverse industries familiar with smart cities. Due to the rapid urbanization
worldwide, cities are becoming overcrowded and are facing issues such as water scarcity,
pollution, soil contamination, etc. On the other hand, unavailability of funds; global
warming; and providing food security, basic health, and education are the key issues
facing government bodies. Previous studies elaborated that AI and IoT technologies
were useful in analyzing the optimization of resources using accurate data collection
Sustainability 2021,13, 10983 22 of 35
processes. Developing plans for sustainable smart cities is a major challenge for emerging
economies. Some countries (e.g., Singapore, Denmark, etc.) have leveraged the beneﬁts
of the AI and IoT techniques to measure and track various activities such as pollution
levels, weather monitoring, energy distribution and management, trafﬁc and transport
management, water distribution and sewerage monitoring, etc. All the data collected by
various sensors, cameras, videos can be sent to a central platform to perform an analysis
to identify the issues and possible solutions using analytics. For example, the beneﬁts
of AI in trafﬁc management to improve pedestrian safety is followed in Singapore [
and Copenhagen uses an AI-enabled solutions lab to optimize energy usage and weather
]. However, the majority of the smart city development is occurring in
developed countries, whereas emerging economies such as China, India, and Brazil are
still struggling to overcome the challenges in developing smart cities. The huge population
and rapid urbanization rates are causing these emerging economies to design and develop
sustainable smart cities at the present time.
Using the data from experts’ opinions, the identiﬁed challenges were analyzed using
the DEMATEL method. The challenges were grouped into cause types and effect types
based on their importance as determined by the experts. Using the ﬁndings, we aimed to
explore the relationships between the challenges and how they inﬂuenced the adoption
of AI and the IoT in sustainable smart city development. Among all challenges, Lack of
infrastructure (C1), Insufﬁcient funds or capital (C2), Cybersecurity risks (C3), and Lack
of trust in AI, IoT (C10) were the most prominent factors inﬂuencing the adoption of
AI and the IoT in smart city development. Therefore, researchers and regulatory bodies
should give importance to these challenges while planning a smart city project. In the
causal loop diagram, it was observed that Smart waste and hygiene management (C4),
Lack of professionals (C5), Managing energy demand (C6), Managing transportation (C7),
Environmental risks (C8), and Managing public health and education (C9) were highly
affected by the other factors. The relationships inﬂuencing these challenges can help us
to understand the limitations and uncertainties associated with the other challenges. The
results of this study can help academicians, industry practitioners, and policymakers
reach an overall understanding of the inter-relationships between the challenges that are
hindering the adoption of AI and the IoT in sustainable smart city development. The data
were collected from experts from diverse industries and organizations, which made this
study unique for emerging economies. A summary of the highlights of this study follows.
We identiﬁed challenges for adopting AI and the IoT for sustainable smart city devel-
opment using a systematic literature review method (PRISMA);
Ten key challenges were identiﬁed using the PRISMA method, and a total of 171
articles were studied;
Ten experts having experience in smart city development were contacted, and their
opinions were evaluated using DEMATEL;
The study ﬁndings revealed that lack of infrastructure, lack of trust in AI and the IoT,
cyber security and data risks, and insufﬁcient funds were the major barriers that are
affecting the adoption of AI and the IoT for sustainable smart city development;
The results of this study can be used as a basis for understanding various inter-
relationships between the barriers, as the study was based on data from China and
developing countries such as India, Brazil, and Malaysia can observe the ﬁndings as
useful while adopting AI and the IoT for smart city projects.
Despite many efforts taken to carefully design the study, there were some shortcom-
ings. We did not use a Grey scale to measure the accuracy of opinions of experts about
the challenges, so in future research, Grey-DEMATEL with a greater number of challenges
can be explored to achieve good results in this domain. ANP, AHP, or ISM can be used to
present the hierarchical interrelationships more robustly. In addition, the consultation with
experts with great experience in this domain can help reach possible solutions to overcome
the challenges hindering sustainable smart city development.
Sustainability 2021,13, 10983 23 of 35
Conceptualization, K.W.; methodology, R.K.G.; software, R.K.G.; validation,
K.W., Y.Z., and R.K.G.; formal analysis, K.W.; investigation, K.W. and Z.L.; resources, K.W.; data
curation, K.W. and Z.L.; writing—original draft preparation, R.K.G. and Z.L.; writing—review and
editing, K.W. and R.K.G.; visualization and formatting, Z.L.; supervision, K.W.; project administration,
Y.Z.; funding acquisition, Y.Z. K.W., Y.Z., and Z.L. contributed equally to this work. All authors have
read and agreed to the published version of the manuscript.
This research and the APC were funded by the University-Enterprise-Partnership Program
of Solearth Architecture (grant number DOU- 315324C-KGT).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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