energies

Review
Contributions and Risks of Artificial Intelligence (AI)
in Building Smarter Cities: Insights from a Systematic
Review of the Literature
Tan Yigitcanlar 1, * , Kevin C. Desouza 2 , Luke Butler 1 and Farnoosh Roozkhosh 3
1 School of Built Environment, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000,
Australia; luke.butler@hdr.qut.edu.au
2 QUT Business School, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia;
kevin.desouza@qut.edu.au
3 School of Arts and Architecture, Guilan University, Persian Gulf Highway, Rasht 41998-43653, Guilan, Iran;
farnoosh_r@msc.guilan.ac.ir
* Correspondence: tan.yigitcanlar@qut.edu.au; Tel.: +61-7-3138-2418




Received: 24 February 2020; Accepted: 16 March 2020; Published: 20 March 2020


Abstract: Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in
the use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban
locations that are enabled by community, technology, and policy to deliver productivity, innovation,
livability, wellbeing, sustainability, accessibility, good governance, and good planning—has increased
the demand for AI-enabled innovations. There is, nevertheless, no scholarly work that provides a
comprehensive review on the topic. This paper generates insights into how AI can contribute to the
development of smarter cities. A systematic review of the literature is selected as the methodologic
approach. Results are categorized under the main smart city development dimensions, i.e., economy,
society, environment, and governance. The findings of the systematic review containing 93 articles
disclose that: (a) AI in the context of smart cities is an emerging field of research and practice. (b) The
central focus of the literature is on AI technologies, algorithms, and their current and prospective
applications. (c) AI applications in the context of smart cities mainly concentrate on business efficiency,
data analytics, education, energy, environmental sustainability, health, land use, security, transport,
and urban management areas. (d) There is limited scholarly research investigating the risks of wider
AI utilization. (e) Upcoming disruptions of AI in cities and societies have not been adequately
examined. Current and potential contributions of AI to the development of smarter cities are outlined
in this paper to inform scholars of prospective areas for further research.

Keywords: artificial intelligence (AI); AI technologies; AI algorithms; disruptive technology; smart

city; smart urban technology; urban informatics; sustainable urban development; climate change

1. Introduction
There exists a strong scientific consensus that anthropogenic climate change is the biggest crisis of
our time [1,2]. In a rapidly urbanizing world, climate change and the misuse and mismanagement of
land and resources are triggering natural disasters and increasing their intensity [3,4]. Subsequently,
cities are becoming frequently subjected to the direct or indirect impacts of natural disasters—for
example, the 2019 Amazon Rainforest fires [5] and the 2020 Australian bushfires [6]. There have been
numerous top-down (e.g., the Paris Agreement, Intergovernmental Panel on Climate Change, UN’s
Sustainable Development Goals, UN Climate Change Conferences) and bottom-up (e.g., school strikes,
extinction rebellion protests, climate emergency declarations) attempts to raise awareness and develop
policy actions to address the climate emergency [7,8].

Energies 2020, 13, 1473; doi:10.3390/en13061473 www.mdpi.com/journal/energies

Energies 2020, 13, 1473 2 of 38

These efforts provided some hope, despite the political and policy quagmires in many countries.
Nevertheless, there has been no significant climate action undertaken to address the crisis. Instead,
in recent years, with the advancement of the current digital revolution, a large portion of policymakers,
practitioners, and scholars have increased their faith in smart urban technologies to mark a major
turning point in the history of humankind [9]. This technocentric view—in solving urban and
environmental problems with the aid of technology—has increased the popularity of the ‘smart
cities’ notion [10]. These cities—also referred to as ‘geographies of disruption’ [11]—harness digital
technologies to offer new business opportunities, shape the urban fabric, improve the quality and
performance, and overcome many of the challenges confronted by urban areas [12].
The prospects of smart urban technologies range from expanding infrastructure capacity to
generating new services, from reducing emissions to engaging the public, from minimizing human
errors to improved decision-making, and from supporting sustainable development to improving
performances of commercial enterprises and cities [13,14]. The most popular technologies in the
context of smart cities include but are not limited to internet-of-things (IoT), autonomous vehicles
(AV), bigdata, 5G, robotics, blockchain, cloud computing, 3D printing, virtual reality (VR), digital
twins, and artificial intelligence (AI) [15–17]. While all these technologies are critical in transforming
our cities into smarter ones, AI combined with these technologies has significant potential to address
the urbanization challenges of our time [18]. Furthermore, AI is certainly seen as the most disruptive
technology among them [19,20].
The prospective benefits of AI for cities continue to be discussed in the literature in the context of
smart cities—that are enabled by community, technology, and policy to deliver productivity, innovation,
livability, wellbeing, sustainability, accessibility, as well as good governance and planning [15,21,22].
Despite the growing number of articles on the topic, there is no scholarly work that provides a
comprehensive review of the growing literature. This paper organizes the literature to examine how AI
can contribute to the development of smarter cities. As the methodologic approach, the study adopts a
systematic literature review on the topic of ‘AI and the smart city’.

2. Conceptual and Application Background

In broad terms, AI is defined as “machines or computers that mimic cognitive functions that
humans associate with the human mind, such as learning and problem solving” [23]. In other words,
AI, where machines mimic human cognitive functions, can make decisions, think, learn and improve
themselves. It was first introduced in 1956 at Dartmouth College, but development was slow until
recently due to immature computational technologies. In recent times, advances in hardware, software,
and networking technologies have enabled us to design, develop, and deploy AI systems at scale.
The application areas of AI ranges from banking and finance to marketing and gaming, and from
agriculture and healthcare to AVs and space exploration—and many more areas [24].
AI-driven computational techniques are diverse and range from rule-based systems to deep
learning systems. A popular AI knowledge map was created by Corea [25]. His conceptualization
brings together the AI paradigms and the AI problem domains (Figure 1). The X-axis categories
various computational paradigms and the Y-axis outlines problem domains. Various technologies are
then mapped to illustrate their potential value. For example, rule-based systems (e.g., expert systems,
robotic process automation) that are logic-driven knowledge engines, which are common in urban
systems, are often used to capture and automate structured workflows.
Energies 2020, 13, 1473 3 of 38
Energies 2020, 13, x FOR PEER REVIEW 3 of 36

Figure 1.
Figure 1. AI
AI knowledge
knowledge map,
map, derived
derived from
from[25].
[25].

Today,AI
Today, AIapplications
applications areare being
being deployed
deployed in all facets of cities [26,27].
[26,27]. WeWe can
canclassify
classifythese
these
applicationsbased
applications basedon on their
their underlying
underlying AI technologies along along with
with other
otherrelevant
relevantsmart
smarttechnologies
technologies
asshown
as shownin inFigure
Figure1.1.
AIapplications
AI applications are are used
used to
to improve
improve and innovate the the delivery
delivery of
of public
public services
services[28].
[28].Various
Various
citieshave
cities have begun
begun ‘robotic
‘robotic process
process automation’
automation’ (RPA)(RPA)
projects.projects. These are
These projects projects areonfocused
focused on
automating
automating tasks that are currently conducted by public workers that are mundane,
tasks that are currently conducted by public workers that are mundane, repetitive, and costly. Thus, repetitive, and
costly. up
freeing Thus, freeing
valuable up valuable
resources resources
to be better to be better
deployed. Cities deployed. Cities
are using RPA to are using
process RPAapplications
online to process
online applications for items such as permits. RPA follows structured
for items such as permits. RPA follows structured rules to reach an outcome [29]. rules to reach an outcome [29].
The interest in autonomous vehicles (AV) is palpable. AVs open opportunities for citiestoto
The interest in autonomous vehicles (AV) is palpable. AVs open opportunities for cities
modernizetheir
modernize theirpublic
publictransport
transport infrastructure
infrastructure [30].
[30]. Autonomous
Autonomous busesbusesareareexpected
expectedtotostart
startcarrying
carrying
fare-paying customers in Scotland by mid-2020 after successful trials across a 14-mile routeon
fare-paying customers in Scotland by mid-2020 after successful trials across a 14-mile route onthe
the
Forth Road Bridge between Fife and Edinburgh. All Nippon Airways (ANA)
Forth Road Bridge between Fife and Edinburgh. All Nippon Airways (ANA) has commenced trials has commenced trialsof
of autonomous
autonomous busesbuses with minimal
with minimal humanhuman
oversightoversight
at Haneda at International
Haneda International Airport
Airport [31]. [31].
Depending
Depending
on the success on of
thethese
success of these
trials, ANAtrials,
plansANA
do a plans do adeployment
full-scale full-scale deployment
by the end byofthe end In
2020. of recent
2020.
In recent times, we have also seen AVs complete successful tests in more
times, we have also seen AVs complete successful tests in more complex urban environments including complex urban
environments including London and Paris [32].
London and Paris [32].
The interest in autonomous systems is not limited to RPA or AVs. Smart cities are exploring how
The interest in autonomous systems is not limited to RPA or AVs. Smart cities are exploring how
to take advantage of advances in robotics [33]. The City of Houston will be beginning trails of robot
to take advantage of advances in robotics [33]. The City of Houston will be beginning trails of robot
police shortly at various transit centers to curb petty crime and free up law enforcement resources
police shortly at various transit centers to curb petty crime and free up law enforcement resources [34].
[34]. Robots are also being tested to augment law enforcement personnel and lower incidents of
Robots are also being tested to augment law enforcement personnel and lower incidents of conflict
conflict between them and the public. In the US where, in recent times, several routine traffic stops
between them and the public. In the US where, in recent times, several routine traffic stops have led to
have led to confrontations, robots are being tested in mediating encounters between police officers
confrontations, robots are being tested in mediating encounters between police officers and drivers [35].
and drivers [35].
Engagement platforms between public agencies and various stakeholders of the city are also being
Engagement platforms between public agencies and various stakeholders of the city are also
transformed through a range of AI applications. Chatbots are the most popular set of AI applications in
being transformed through a range of AI applications. Chatbots are the most popular set of AI
this regard. Rammas, a chatbot, was deployed by the Dubai Electricity and Water Authority (DEWA) in
applications in this regard. Rammas, a chatbot, was deployed by the Dubai Electricity and Water
January 2017 [36]. DEWA can respond to queries from residents in Arabic and English, and promotes
Authority (DEWA) in January 2017 [36]. DEWA can respond to queries from residents in Arabic and
greater knowledge awareness on utility matters. In its first year of operations, Rammas responded to
English, and promotes greater knowledge awareness on utility matters. In its first year of operations,
roughly
Rammas700,000
responded queries, which led
to roughly to anqueries,
700,000 80% drop in in-person
which led to an visits.
80% drop in in-person visits.
AI systems ingest vast amounts of data, apply learning algorithms, and learn patterns from the
data to enable predicting outcomes [37]. Today, cities are deploying machine learning systems to
Energies 2020, 13, 1473 4 of 38

AI systems ingest vast amounts of data, apply learning algorithms, and learn patterns from
the data to enable predicting outcomes [37]. Today, cities are deploying machine learning systems
to exploit data across their ecosystem from sensors on public infrastructure, to machine readable
cards that provide access to city services (e.g., public transport), to images and videos that capture
movements around the city, and even devices that capture auditory, olfactory, and tactile data [38,39].
Urban infrastructures, such as traffic lights, are becoming connected. Traffic lights are connected to
road sensors to reduce wait time at signals based on the traffic flow. Scotland’s Glasgow city has
installed networks of sensors that connect to streetlights and traffic lights to help monitor traffic flow
and increase connectivity, aggregately reducing travel time for drivers. Further, the traffic data also
feeds into maps in real-time to help drivers, cyclists, and pedestrians make decisions to plan their
commutes [40].
The Las Vegas Health Department, in partnership with the University of Rochester trialed the
nEmesis app which utilized machine learning to collect and examine tweets, the purpose of which
was to select restaurants which were suitable for inspection [41]. Following controlled experiments,
the nEmesis app was found to be 64% better at identifying restaurants with food safety issues than
established processes involving random inspections. It also had success at identifying restaurants
that were unlicensed and had infectious staff. Overall, the app was very effective at helping the Las
Vegas Health Department address issues with their restaurant inspection process without the need for
additional resources [41]. The harm assessment risk tool (HART) was developed by UK’s Durham
Constabulary to detect patterns of recidivism among criminals [42]. The tool was trained on crime
data from 2008–2012, including information about suspects’ gender and zip code. The tool was used
to predict the recidivism rate in 2013. It successfully predicted 98% of low-risk offenders and 88% of
high-risk offenders.
In 2017, the Seattle Police Department launched a data analytics platform to transition towards
improved oversight, data-driven decisions, and community engagement [43]. This platform helps the
department to manage, govern, and support insightful policing. The system is designed to help the
department’s leadership team to track trends related to operations. It integrates 17 internally tracked
metrics and develops visualizations for department heads. The system tracks several measures such as
use-of-force incidents, number of arrests, self-initiated trips, response to calls, number of stops, and
civilian complaints. The department can use this detailed information on each police officer to take
appropriate measures (e.g., counselling). Since 2012, the police department in San Diego has collected
over 65,000 face scans, in an attempt to match them to a directory of over 1 million images collected as
part of the San Diego County Sheriff’s Tactical Identification System (TACIDS) [44]. More recently,
London Metropolitan Police has announced plans to use facial recognition technology to aid police in
identifying suspects.
While the housing and urban development space today is data rich, much of the data is often
left unanalyzed, thereby resulting in an inability to keep policies and enforcement standards current.
Researchers from Georgia Institute of Technology, Emory University, and University of California,
Irvine collaborated with the Atlanta Fire Rescue Department (AFRD) to develop an algorithm which
was able to predict fire risk in buildings [45]. Using data from 2010–2014, the algorithm included over
50 variables—including property location, building size, structure, age, and history of fire incidents—to
predict fire risk. The algorithm classified fire risk ratings for 5000 buildings and found another 19,397
buildings requiring inspection. Furthermore, the algorithm was able to predict 73% of fire incidents
which occurred within the study area [45].
AI-enabled computational tools also help in protecting cyber-infrastructure that is the core fabric
of smart cities [46]. Four US cities, namely Pensacola, New Orleans, Galt, and St Lucie, were all
victims of different cyberattacks throughout December that rendered telephone and email systems,
law enforcement systems, waste, energy, and payment systems inoperable. Often, these attacks
demand a ransom, and councils find themselves either paying the attackers or employing external
cybersecurity and consulting firms to mitigate and repair the situation. In Lake City, Florida, council
Energies 2020, 13, 1473 5 of 38

reluctantly paid a $460,000 ‘ransom’ to attackers after all their council systems were shut down [47].
Researchers from MIT developed an AI platform called AI2 that outperforms existing systems in
predicting cyber-attacks [48]. AI2 detects 85% of cyberattacks, performing about 300% better than
previous systems. The system is also able to reduce the instance of false-positive readings to one-fifth
of previous outcomes. This high detection rate is enabled through supervised and unsupervised
machine learning.
While we have treated each AI application in isolation, it is common to have them bundled and
integrated. Researchers from Carnegie Mellon University collaborated with the City of Pittsburgh to
develop a Scalable Urban Traffic Control (SURTRAC), which was able to simultaneously monitor and
control the flow of traffic [49]. The system has been deployed in the East Liberty neighborhood since
2012 and covers nine intersections. On average, 29,940 vehicles pass through this area daily. SURTRAC
is a schedule-driven system designed to manage multiple competing traffic flows shifts. SUTRAC is a
multi-agent decentralized system, where an agent system runs each intersection. Each agent system
controls traffic signals for their intersection and monitors traffic flow by dynamically coordinating with
other agents in real-time. The deployment of SURTRAC resulted in a 34% increase in vehicle speed,
and a reduction of 25% in travel time, 40% in waiting time, 31% in traffic stops, and 21% in emissions.
While AI systems have significant potential, their deployments are never straightforward.
In Detroit, a $9 million initiative ‘Neighborhood Real-Time Intelligence Program’ implemented facial
recognition software and video surveillance cameras at 500 different Detroit intersections. This
initiative built on the previous ‘Project Green Light’ Initiative, which installed 500 cameras outside
of businesses capable of recording and reporting real time video footage to the police. The software
boasts an ability to match faces with 50 million driver’s license photographs in the Michigan police
database. Nevertheless, recent research has shown that current facial recognition software more
often misidentifies black faces than white faces [50]. Whilst intended to increase public safety, there
is widespread public criticism directed towards this technology, as residents feel their privacy is
compromised and knowledge of the racial biases continues to increase.
Fake news in its purest form refers to completely made up information, nonetheless, such
information is often hard to identify as it can resemble credible journalism and attract maximum
attention, spreading like wildfire through various social media channels [51]. A man was caught after
he carried an assault rifle and fired shots at a pizza parlor in Northwest Washington Upon arrest, the
man informed the police that he was investigating a conspiracy theory which claimed that the pizza
parlor—Comet Ping Pong—was the headquarters of a pedophilia ring [52]. This incident caused panic
among people in the neighborhood, resulting in the lockdown of several businesses.
In Arizona, the hotbed for testing of AVs by major technology providers, we have seen incidents
of residents throwing rocks at these vehicles [53]. There, AVs are seen as a threat to jobs, livelihood,
and are a source of frustration. As noted by Selby & Desouza [54], “If theory and practice advance
over the next few years without paying attention to fragility, then cities will continue to be vulnerable
to manageable threats. As the trends of urbanization continue, it is even more imperative to attend to
fractures of social compacts. Cities will continue to grow, and their complexity will only increase. This
complexity will continue to mask fragility in the city and could result in the breakdown in one of our
society’s most valuable artifacts, developed cities, representing a potential loss of life and economic
prosperity”. AI technologies, for all their good, do make cities more fragile [55] as they put pressures
on local governments to maintain existing, and strengthen, social compacts in the face of job losses,
automation, shifts in public finances, and so on.
In May 2016, a Tesla Model S car collided with a tractor-trailer in Williston, Florida. The accident
occurred on the highway when the car, on autopilot mode, collided with the truck while crossing an
uncontrolled intersection. The Tesla driver sustained fatal injuries, raising several questions about
the autopilot functionality. However, the National Highway Traffic Safety Administration’s (NHTSA)
final investigation report concluded that the accident was caused by the driver’s inattention [56]. Since
Energies 2020, 13, 1473 6 of 38

the accident, Tesla has implemented several features to keep drivers engaged while their car is in
autopilot mode.
Chrysler recalled about 1.4 million cars and trucks, because these vehicles could be hacked
remotely over the Internet [57]. These vehicles used UConnect features to connect with Sprint networks
for navigation. Hackers could access these cars’ navigation systems to control air conditioners, cut
off brakes,
Energies and shut
2020, 13, down
x FOR engines, and so on. Software installed in vehicles need to be constantly
PEER REVIEW 6 of 36
upgraded to protect against hacks and security breaches [58].
conditioners, cutlocal
Economically, off brakes, and shut down
governments engines,
may lose and sostreams
revenue on. Software installed
because in vehicles
of AVs due toneed to
a decrease
be constantly upgraded to protect against hacks and security breaches [58].
in speeding tickets, towing fees, and driving under influence charges [59]. Cities in Arizona such as
Phoenix and Economically,
Mesa collectedlocal governments may loseand
about $10.8 million revenue
$4.2 streams
million because of AVsfor
from drivers due to a decrease
traffic violationsin [60].
speeding tickets, towing fees, and driving under influence charges [59]. Cities in Arizona such as
On an average, cities in California generated $40 million in towing violations annually [15].
Phoenix and Mesa collected about $10.8 million and $4.2 million from drivers for traffic violations
The conceptual and application background of AI as presented above underlines the importance
[60]. On an average, cities in California generated $40 million in towing violations annually [15].
of furtherThe investigations
conceptual and intoapplication
how we can best integrate
background of AI asAI systemsabove
presented in addressing
underlinescritical urban issues.
the importance
Particularly
of furtherthe challenges we
investigations intoface—e.g.,
how we canclimate emergency—calls
best integrate AI systems in for smartercritical
addressing systems in place.
urban issues. This
is alsoParticularly
highly critical to increase
the challenges wethe smartness
face—e.g., of our
climate cities. A recentfor
emergency—calls study thatsystems
smarter evaluated the smartness
in place. This
levelsisofalso
180 highly
Australian local
critical to government areas argues
increase the smartness for the
of our importance
cities. A recent of better
study integration
that evaluated of theurban
smartnessincluding
technologies, levels of AI,
180 into
Australian local government
local service delivery and areas argues for
governance the importance of better
[14].
integration of urban technologies, including AI, into local service delivery and governance [14].
3. Materials and Method
3. Materials and Method
We undertook a systematic review of the literature to answer the following research question:
We undertook a systematic review of the literature to answer the following research question:
How can AI contribute to the development of smarter cities? We adopted a three-phase methodologic
How can AI contribute to the development of smarter cities? We adopted a three-phase methodologic
approach (Figure 2) following the study steps of Yigitcanlar et al. [61].
approach (Figure 2) following the study steps of Yigitcanlar et al. [61].

Figure2.2.Literature
Figure Literature selection
selectionprocedure.
procedure.

The first
The first phasephase is planning,
is planning, whichinvolves
which involves developing
developing the
theresearch aim,
research research
aim, question,
research a list a list
question,
of keywords, and criteria for the inclusion and exclusion of articles. The research aim was framed to
of keywords, and criteria for the inclusion and exclusion of articles. The research aim was framed to
generate insights into forming a greater understanding on how AI can contribute to the development
generate insights into forming a greater understanding on how AI can contribute to the development
of smarter cities. The inclusion criteria were intended to be peer-reviewed journal articles, that were
of smarter cities.
available Theininclusion
online, criteria
English, and were intended
had relevance to betopeer-reviewed
with respect the research aim.journal articles,library
A university’s that were
available online, in English, and had relevance with respect to the research aim. A university’s
search engine, which gives access to 393 different databases including: Directory of Open Access library
search engine,Science
Journals, whichDirect,
gives Scopus,
access to 393 different
TRID, databases
Web of Science, and including:
Wiley OnlineDirectory of Open
Library, was used Access
to
complete an online search. The search was carried out towards the end of December 2019 using the
query string of ((“artificial intelligence” OR “AI”) AND (“smart” OR “smarter” OR “smartness”)
AND (“city” OR “cities” OR “urban”)) to search the titles and abstracts of available articles. The
publication date was left open. From this search it was determined that the one of the earliest studies
Energies 2020, 13, 1473 7 of 38

Journals, Science Direct, Scopus, TRID, Web of Science, and Wiley Online Library, was used to complete
an online search. The search was carried out towards the end of December 2019 using the query string
of ((“artificial intelligence” OR “AI”) AND (“smart” OR “smarter” OR “smartness”) AND (“city” OR
Energies 2020, 13, x FOR PEER REVIEW 7 of 36
“cities” OR “urban”)) to search the titles and abstracts of available articles. The publication date was
left open.
on AIFromand thethiscity
search
wasitfrom
was determined
Schalkoff [23].thatThethe one of were
abstracts the earliest studies
then read, andonif AI
theand thewas
article city was
from considered
Schalkoff [23].to beThe abstracts
relevant to thewere then aim,
research read,theand if the article
full-text was considered
was reviewed to decide to be relevant
whether it wasto the
suitable
research aim,totheinclude in final
full-text wasanalysis.
reviewed to decide whether it was suitable to include in final analysis.
The second
The second phase phase involved
involved carrying
carrying out review
out the the review of relevant
of relevant articles.
articles. The The initial
initial searchsearch
resulted
resulted in a total of 799 records. These records were then screened and reduced
in a total of 799 records. These records were then screened and reduced to 245 by applying the inclusion to 245 by applying
the inclusion criteria—i.e. journal articles that were peer-reviewed, and available online. The articles
criteria—i.e. journal articles that were peer-reviewed, and available online. The articles were then
were then ‘eye-balled’ to ensure they were consistent with the keyword search, the abstracts assessed
‘eye-balled’ to ensure they were consistent with the keyword search, the abstracts assessed against the
against the research aim, and duplicates removed. The total number of articles was reduced to 208.
research
The aim, andofduplicates
full-text the selectedremoved.
articles wereTheread
totaltonumber
determine ofthe
articles was with
relevance reduced to 208.
respect to theThe
aimfull-text
of
of thethe
selected articles were read to determine the relevance with respect to
study and the results were narrowed down to 119 articles. After another round of full-text the aim of the study and
the results werethe
screening, narrowed
number down to 119was
of articles articles.
reduced After
to another round
93. Finally, of full-text
these 93 articlesscreening, the number
were reviewed,
of articles was reduced
categorized, to 93. Finally,
and analyzed. these
The criteria for93formation
articles were
of thereviewed, categorized,
themes is presented and analyzed.
in Table 1. For the The
categorization,
criteria for formation the of
main
thesmart
themes cityisdevelopment
presented in dimensions—i.e.,
Table 1. For the economy, society, environment,
categorization, the main smart
and governance—were selected. Figure 3 below presents these
city development dimensions—i.e., economy, society, environment, and governance—were dimensions in the context of smartselected.
Figurecities.
3 below presents these dimensions in the context of smart cities.
Table 1. Selection criteria for formulating categories.
Table 1. Selection criteria for formulating categories.
Selection criteria
1. Determine the literature relevant to Selection Criteria
the research aim by using the eye-balling technique;
2. Identify the suitable literature pieces focusing on AI and smart cities after reading the full-
1. Determine the literature relevant to the research aim by using the eye-balling technique;
text;
2. Identify the suitable literature pieces focusing on AI and smart cities after reading the full-text;
3. Group the identified AI technology, algorithm and application areas with similarities into
3. Group the identified AI technology, algorithm and application areas with similarities into
broadbroad categories;
categories;
4. 4.
Narrow downdown
Narrow the selected
the selected categories
categories and the
and review review the reliability
reliability of theseother
of these against against other literature;
published
5. published
Review literature;
the selected literature again and update the shortlisted categories if necessary;
6. 5. Review
Confirm the selected
and finalize literature selected
the categories again and
forupdate the shortlisted
the classification categories if necessary;
of literature;
7. 6. Confirm and finalize the categories selected for the classification
Catalog the literature selected for the review under the selected categories. of literature;
7. Catalog the literature selected for the review under the selected categories.

Figure
Figure 3. Smart
3. Smart citycity conceptualframework
conceptual framework highlighting
highlightingkey
keydomains,
domains,derived fromfrom
derived [62]. [62].
Energies 2020, 13, 1473 8 of 38

The third and final phase is reporting and dissemination. This phase involved critically
documenting and presenting the results from the 93 articles analyzed. A discussion of the perceived
benefits and concerns associated with AI implementation were outlined.

4. Results

4.1. General Observations

The first step in the analysis of the selected 93 articles was to classify them by date of publication.
This process revealed interest in AI technology had grown exponentially in recent times. Almost half
of the reviewed articles were published in 2019 (n = 46; 49%), one-fifth in 2018 (n = 18; 20%), and nearly
one-tenth in 2017 (n = 8; 9%). Slightly less than a quarter of all articles were published before 2016
(n = 21; 23%). The earliest article included in the literature review was published in 1999. Despite the
fact that articles on AI and the city dated back to 1990, the earliest one that met our selection criteria
was [63]. These figures reflect the results of other review studies and are representative of the increased
interest in how AI systems interact and impact the development of smart cities [64].
Leading authors are affiliated with institutions in Europe (n = 28), East Asia (n = 24) and North
America (n = 21). Nonetheless, the Oceania context, particularly in Australia, is often referenced
among the best applications of AI in practice [65,66]. Regarding the academic journals, the articles
published in IEEE Access appear most often (n = 8), followed by Sensors (n = 5), and then three articles
in both Artificial Intelligence in Medicine (n = 3) and Procedia Computer Science (n = 3).
Articles were categorized under four groups. These groups were based on the main smart city
domains of: (a) Economy; (b) Society; (c) Environment; (d) Government. More than one-sixth of them
(n = 17) were in the economy domain, slightly more than a quarter (n = 24) in the society domain, over
one-third (n = 33) in the environment domain, and slightly less than quarter of them (n = 19) were in
the governance domain.
With reference to the ‘AI Knowledge Map’ (Figure 1), the majority of articles discusses AI
applications or techniques that fit within the machine learning paradigm (n = 71), followed by
probabilistic methods (n = 25), knowledge-based (n = 21), search and optimization (n = 21), logic-based
(n = 18), and embodied intelligence (n = 15). Regarding AI applications, the most commonly mentioned
in the selected literature are neural networks (n = 55), evolutionary algorithms (n = 21), distributed
artificial intelligence (n = 15), computer vision (n = 13), and decision networks (n = 9). The most
common AI paradigms and applications identified in the literature are outlined in Table 2. A complete
list of all articles and the identified AI paradigms, applications, techniques, and supporting technology
are shown in Appendix A. Please note, there is some crossover with the number of AI paradigms and
applications identified in the literature as many articles discuss multiple, or hybrid AI systems.
We acknowledge that there is a potential risk in bias resulting from our strategy for selecting
literature to be included in the review. Our review focused on journals only, whereas cutting edge works
in engineering, design, and technology field are often published in conference papers. Nonetheless,
in order to complement the reviewed 93 journal articles, the paper also cited or quoted 112 relevant
literature pieces on the ‘AI and smart cities’ topic.

4.2. AI in the Economy Dimension of Smart Cities

Papers categorized under the economy dimensions of smart cities are those that provide insights
into ‘how AI can contribute to and enhance the productivity and innovation of smart cities’ [33].
Research in this area focused predominately on technological innovation, economy and management
areas, and the contribution of AI can be summarized as: (a) Enhancing productivity and innovation
by automating data management and analysis; (b) Reducing costs and increasing resources through
pattern recognition; (c) Supporting decision-making by analyzing large volumes of data from multiple
sources, and; (d) Drawing conclusions based on logic, reason and intuition. Appendix A lists the
analysis highlights of the reviewed literature.
Energies 2020, 13, 1473 9 of 38

Table 2. The common AI paradigms and applications identified in the literature.

Category Element Reference

AI paradigms Machine learning [26]; [63]; [67–70]; [71–135]
Probabilistic methods [70]; [73–75]; [77]; [80–82]; [87]; [90]; [93,94]; [96–101]; [112–114]; [134]; [136–138]
Knowledge-based [26]; [63]; [67–71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [139–143]
Search and optimization [26]; [67–69]; [77]; [79,80]; [82]; [86]; [92]; [98]; [101]; [106]; [112]; [116]; [128]; [130]; [134]; [144–146]
Logic-based [63]; [67,68]; [69]; [71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [140–143]
Embodied intelligence [26]; [70]; [73]; [75,76]; [80]; [82]; [98]; [104]; [116]; [128]; [134]; [137]; [147]
AI applications Neural networks [26]; [63]; [67–70]; [71]; [73]; [75]; [77–85]; [87]; [89,90]; [92]; [95]; [97–103]; [105–107]; [109–112]; [116–126]; [128–134]
Evolutionary algorithms [26]; [67–69]; [77]; [79,80]; [82]; [86]; [92]; [98]; [101]; [106]; [112]; [116]; [128]; [130]; [134]; [144–146]
Expert systems [26]; [63]; [67–70]; [71]; [73]; [77,78]; [82]; [92]; [98]; [100]; [112]; [136]; [140–143]
Distributed artificial intelligence [26]; [70]; [73]; [75,76]; [80]; [82]; [92]; [98]; [116]; [128]; [134]; [137]; [147,148]
Computer vision [73–75]; [87]; [93,94]; [96]; [99]; [101]; [113,114]; [118]; [138]
Decision networks [70]; [77]; [80]; [82]; [90]; [98]; [112]; [134]; [136]
Energies 2020, 13, 1473 10 of 38

AI is a useful tool to quickly and accurately manage and analyze large volumes of data to
support business decisions [103,112]. This is particularly relevant in combination with IoT—a
system enabled by the internet, which allows communication between a large network of devices
without the need for human intervention [77,106,110]. Together with technologies such as blockchain,
cloud storage, and fog computing—which help facilitate the recording, distribution, storage, and
decentralized processing of data [128,149]—, AI could improve productivity by automating the data
management process and removing the need for intermediaries, and hence increasing profitability [126].
Furthermore, AI can improve the stability and effectiveness of IoT contributing to improved network
communication. In return, this would help to improve knowledge sharing, and foster innovation and
entrepreneurship [106,127,128].
AI can be used to recognize patterns in datasets, helping to optimize the data management
process, improve the overall productivity of the data management system [111,149], and identify
cyber-attacks [107], coding errors, and other inefficiencies [103]. Deep learning has already had success
recognizing patterns from a wide range of data sources including images, audio, video, and other
sensors [104]. The application of AI has the potential to remove the need for humans to complete many
repetitive business tasks—particularly those relying on observation—,potentially reducing costs and
freeing up resources for more productive or innovative fields [112,130].
In analyzing the large amounts of data collected by the sensors, devices, and other sources in
a smart city, AI has the potential to accelerate the decision-making process by automating complex
statistical analyses [111,130,150]. This is particularly relevant with regards to the application of deep
learning technology to the process of data fusion—i.e. the processes of taking data from a variety of
sources, combining it, and improving its quality and usefulness by producing sophisticated statistical
models [126]. AI has the capability to automate this process and conduct statistical analyses that are
much larger and more complex than could be completed with human intervention. This information
can then be used to reduce economic uncertainty and assist business decisions [77,103,107], and/or
create marketplaces that are more responsive to user needs and desires [130].
The ability of AI to complete complex statistical analysis can also be used to automate
decision-making [128,130,150]. The ability to learn, can ensure that AI systems are responsive
to uncertainty, particularly when working with and around humans [94,119]. This can reduce the
possibilities of accidents, errors, and improve the operational efficiency of business and industries [119].
Smart control systems can monitor traffic, collect and analyze data and in combination with
connected-AVs there is potential to make real-time decisions which enhance the efficiency of transport
operations—including freight and supply chain logistics [104,130]. AI systems can be developed with
human-like abilities, such as creativity, design, intuition, inventiveness, trust, ethics, and values to
perceive, understand, and make informed, reason-based decisions that would benefit companies [76,94].

4.3. AI in the Society Dimension of Smart Cities

Papers in this category provide insights into ‘how AI can contribute to and improve the livability
and wellbeing of citizens in smart cities’ [33]. Research in this area focused predominately on the
health and education sectors, and the contributions of AI can be summarized as: (a) Improving health
monitoring; (b) Enhancing health diagnosis outcomes, and; (c) Providing autonomous tutoring systems
that are highly individualized and adaptive to needs and external changes. Appendix A lists the
analysis highlights of the reviewed literature.
AI systems, in combination with sensors, cameras and other data collection devices, have been
developed to monitor the health and wellbeing of individuals [70,87]. Machine learning techniques
can be used to improve the cost and efficiency of fall detection devices [99], and detect changes in
sleep, mood, heartbeat, respiration, and other vital signs [118,129]. Wearable devices, or ’smart textiles,
enabled by AI, can detect changes in the human body and report findings to health care providers [129].
In intensive care settings, monitoring devices equipped with AI can be used to adjust the settings
on bedside devices reducing total healthcare costs and improving patient outcomes [67]. In rural
Energies 2020, 13, 1473 11 of 38

settings, the ability to monitor patients remotely could contribute to reduced inequities and improve
access to healthcare [89]. This is a particularly critical issue in countries like Australia, where in some
remote areas the nearest health service provider or hospital could be located 1,000 km or further [151].
Additionally, the smart tracking of health symptoms could improve communication between patients
and health care professionals [142].
In addition to health monitoring, AI systems can greatly improve health diagnosis by providing
an effective repository of medical knowledge and the ability to access, analyze, and apply complex
medical data more efficiently [68,139]. Assisting health care workers with tasks involving the collection
and recording of data and knowledge could increase the amount of resources available for patient
care [68,139], improve the quality of life for patients [120], and expand the professional learning
capabilities of professionals [122]. Similarly, the improved analytics and reasoning capabilities of AI
would provide a decision-support mechanism with the potential to reduce chances of misdiagnosis [137],
facilitate greater communication and collaboration between health care professionals [70], and assist
with the development of more personalized medical treatments [91,137].
With regards to the education sector, intelligent tutoring systems that mimic the one-on-one
interaction between tutor and student can provide highly individualized teaching programs for
students [140]. Furthermore, these systems can develop multiple paths to answer any given question
and provide highly detailed feedback [140]. Advances in AI technology can increase the effectiveness
of these, and similar systems by automatically collecting information from the web, ensuring the
most up-to-date content, and using machine learning to increase the adaptability to individualized
learner requirements [81,146]. There is potential to create systems that are far more effective than
one-on-one tutoring [143], with improved communication between student and teacher, and superior
assessment methods [85]. This is especially promising with regards to identifying and adapting
syllabus to the individual strengths and needs of students with learning disabilities or other special
learning requirements [71].
Increased adaptability is important as rapid technological changes are likely to result in an unstable
job market [135,143]. AI could potentially bring new skill requirements across multiple sectors and the
education sector needs to be at the cutting edge of these changes to ensure students are prepared for
future job markets [152]. For example, even in the education sector itself, AI will replace many of the
time-consuming tasks, changing teacher roles to one based on student support and the management of
AI systems [88,135,153]. Managing the education needs of residents is therefore important to ensure
they are able to take advantages of the potential benefits of AI including improved working conditions,
better work-life balance, and improved quality of life [135].
Lastly, AI has been used for the modelling of the spread of the recent COVID-19 epidemic. The
predictions seem to be showing reliable results as the modelling predicts COVID-19 infections with
an accuracy of 96%, and deaths with an accuracy of 99%, up to one week into the future. This
information would help governments implement effective contingency plans, and prevent the virus’s
spread and turn into a global pandemic [154]. Similarly, Lin et al. [155] utilize blockchain with AI to
efficiently manage water use under the changing climate conditions, and contribute to climate change
adaptation efforts.

4.4. AI in the Environment Dimension of Smart Cities

These papers provide insights into ‘how AI could contribute to sustainable urban development
and improved accessibility in smart cities’ [33]. Research in this area focused predominately on
the transport, energy, land use, and environment sectors/areas, and the contribution of AI can be
summarized as: (a) Monitoring changes in the environment; (b) Using smart energy systems to
optimize energy consumption and production; (c) Planning, development, and use of households to
reduce energy consumption, and; (d) Operationalizing smart transport systems. Appendix A lists the
analysis highlights of the reviewed literature.
Energies 2020, 13, 1473 12 of 38

When faced with complex environmental issues and large quantities of data, AI systems have
the potential to make knowledge-based decisions that balance the environmental outcomes of the
city against the social and economic wellbeing of its residents [63,136]. AI systems can be used to
monitor changes in the environment including, noise, temperature, humidity, emissions [90], water
pollutants [133], fish stock, and other environmental indicators [136]. AI systems can respond to these
changes, and quickly implement solutions for dealing with any issues [156]. Furthermore, improved
data quality from AI systems can contribute to more robust and accurate environmental modelling
systems [69,124].
AI has also been identified as a means of creating more energy efficient cities [156]. Smart grid
systems, integrated with AI technology, can be used to control power systems and optimize energy
consumption [78,79]. Including the planning and management of electric vehicle charging [145],
public lighting [75], and data [121]. AI can also assist with the distribution of renewable electricity
generated from multiple, often non-traditional sources—including body heat [125]—, the identification
of inefficiencies, and future forecasting [134,157]. By optimizing the management of resources,
monitoring energy consumption, and better planning for future requirements, cities will be able to use
resources more efficiently and better achieve renewable energy goals [80,86].
Smarter homes can be developed with AI systems that monitor changes in the environment, adapt
to user requirements, and improve energy efficiency [73,92,116]. AI systems could be used to predict
future household energy requirements which can help identify inefficiencies, faults, and control future
energy use [81]. In addition, AI can contribute to reduced energy consumption in the construction
process [102], and improved environmental outcomes in the design [146] and planning process [26].
With regards to sustainable transportation, the goal of AI in the context of smart cities is to
calculate the most efficient means of moving people and goods between places, reducing the number
of vehicle kilometers traveled (VKT). This in turn leads to a reduction in energy consumption which in
turn leads to lower air and noise pollution, congestion, and other externalities such as the requirements
for transportation and parking infrastructure [98,156]. AI can be used for transport optimization by
analyzing real-time measurements—such as traffic signal control—to adjust routes [74,97], balance
user demands [96], and make parking more efficient [104,113]. Particularly in AVs, these changes can
result in substantial reductions in travel time and energy savings [108]. From a transport planning
perspective AI can also be used to differentiate spatial structures in aerial images [113] and collect
masses of data for the development of more accurate, and responsive models which can be used to
develop a more environmentally efficient transportation system [117].
Despite no studies in the reviewed literature directly focus on how AI can tackle climate change,
we are aware of some relevant research. For instance, in their paper entitled ‘tackling climate change
with machine learning’, Rolnick et al. [158] offer areas where machine learning can be deployed.
These areas include better climate predictions and modeling, energy production, CO2 removal,
education, solar geoengineering, and finance. Within these areas, the possibilities include more
energy-efficient buildings, creating new low-carbon materials, better monitoring of deforestation,
and greener transportation. They state that “although AI is not a silver bullet, it brings new insights
into the problem”. Another study, by O’Gorman & Dwyer [159], demonstrates the use of machine
learning to parameterize moist convection and climate change, and extreme event modelling. Likewise,
Dayal et al. [160] model Queensland (Australia) droughts based on AI and neural network algorithms
for decision-makers and local inhabitants to take precautions.

4.5. AI in the Governance Dimension of Smart Cities

How AI can contribute to establishing good governance and planning in smart cities is the focus
of this set of papers [33]. Research in this area focused predominately on the security, governance
and decision-making areas, and the contributions of AI can be summarized as: (a) Enhancing the
operability of surveillance systems; (b) Improving cyber security; (c) Aiding the disaster management
planning and operations, and; (d) Assisting citizens with new technology to support citizen scientist
Energies 2020, 13, 1473 13 of 38

and contribute to the urban decision-making process. Appendix A lists the analysis highlights of the
reviewed literature.
Advanced AI surveillance technologies, enabled by motion detection, predictive analytics, drones,
and other autonomous devices, can be used to monitor urban areas, recognize threats, such as
crime [72,93,100,148], fraud [109], accidents, and fire [101,123,160]. On a broader scale, AI can be
used to monitor communication networks and recognize potential terrorist threats, trafficking, crime
syndicates, and other illegal behavior [100]. Once targets are identified, intelligent surveillance systems
can evaluate and track targets [93], and collect forensic evidence—such as video recording [138]. AI
can also be used to better predict future crime incidents and ensure the optimal allocation of crime law
enforcement [144].
Cyber threats also pose significant risks to smart cities both in terms data privacy and the protection
of connected infrastructure [83,161,162]. AI can be used to identify irregular behavior, determine what
is a threat and implement mitigation measures at speeds beyond that of human ability [83,100]. This
together with encryption technologies such as blockchain [100], and a focus on data security at all levels
of design [131], can alleviate individual concerns regarding data security and contribute to increased
transparency and trust regarding online systems [83,141]. This would allow increased avenues for
citizen engagement in policy decisions and citizen scientist engagement with policy development
via crowdsourcing [163], along with other online services such as electronic voting [141] and smart
contracts [115].
Given the ability of AI to analyze large amounts of data, scenarios to deal with potential threats
could be constructed simultaneous to the detection of threats [161]. This would give decision-makers
and other authorities more time to respond to threats such as natural disasters [132], house fires [123],
or other incidents. Furthermore, AI can be used to assess the extent of damage caused by these events
helping authorities better respond to, and mitigate any damage caused [101,132].
Finally, AI systems can be used to both assist and analyze acceptance of new systems—particularly
those associated with new technologies [109,163]. Online ‘chatbots’ can help residents navigate new
websites and online platforms [109], with training and tips customized based on individualized needs
and interests [164]. Furthermore, AI use reasoning and intuition to assist decision-makers understand
the reasons behind citizen acceptance, or non-acceptance, of new technology [165]. Where new
approaches are required, AI can develop innovative solutions [95] and address future challenges [84].

5. Discussion
This review study investigated the impact of the two very powerful and highly popular phenomena
of our time, i.e., AI and smart cities. On the one hand, the smart city notion is seen as a potential
blueprint for the development of future cities to provide improved productivity, innovation, livability,
wellbeing, sustainability, accessibility, governance, and planning [166,167]. Nevertheless, we still do not
have the technical capabilities to develop these technologically advanced futuristic cities [168]. On the
other hand, AI provides a hope for overcoming the limits of human capabilities, in the computational
sense [169]. Hence, in theory, a happy marriage of AI and the smart cities concept would bring us
closer to producing smarter cities [22].
In this paper, we attempted to generate insights into forming a better understanding on how AI
can contribute to the development of smarter cities by undertaking a systematic review of the literature.
Appendix A lists the analysis highlights of the reviewed literature. The results of the review disclosed
the following main points, and some of the critical issues are discussed further:

1. AI has an evident potential to provide a positive change in our cities, societies and businesses by
promoting a more efficient, effective and sustainable transition/transformation;
2. AI, with its technology, algorithms, and learning capabilities, can be a useful vehicle in automating
the problem solving and decision-making processes; that in return could reform urban landscapes,
and support the development of smarter cities;
Energies 2020, 13, 1473 14 of 38

3. AI in the context of smart cities is an emerging field of research and practice. Hence, further
research is needed to consolidate the knowledge in the field;
4. The central focus of the literature is on AI technologies, algorithms, and their current and
prospective applications;
5. AI applications in the context of smart cities mainly concentrate on business efficiency, data
analytics, education, energy, environmental sustainability, health, land use, security, transport,
and urban management areas, and;
6. Upcoming disruptions of AI on cities and societies have not been adequately investigated in the
literature; thus, further investigations are needed on that issue.

The results of the review revealed that AI-inspired computational systems are bound to make
a profound impact on our cities. The impact will not only be on the physical setup of our cities but
also in how our cities operate and achieve system-level objectives (e.g., livability, resilience, and so
on). In order to ensure that cities advance in keeping with the values and aspirations of their key
stakeholders (i.e., residents, businesses, and so on), it will be vital for us to ensure that AI systems
are designed take on a value-sensitive design approach [170]. AI systems will need to account for the
multiple aspects of diversity that are omnipresent in our cities. In addition, these systems will need
to possess a degree of transparency, adaptability (to respond to varying environmental conditions),
and accountability (for levels of performance). Doing so is non-trivial, but paramount to achieving
responsible innovation in the context of AI and cities.
While advances in computational science and technologies will continue to progress at an
astounding rate, the level of impact they will have on our future cities comes down to the level of trust
individuals and organizations place in these systems. As we continue to live through times where
levels of trust in government are at an all-time low [171], planners and public managers needs to
consider how social license [172] impacts their ability to deploy emerging technologies. Engaging
stakeholders into the design processes when it comes to AI-systems will be critical. Stakeholders
should be allowed to shape the elements of these systems and their expected deployment trajectories.
Engaging stakeholders will also enable a city to increase the overall knowledge of the community
when it comes to the innovative potential of these technologies. To date, we have limited frameworks
on how to engage many diverse stakeholders, who have varying knowledge of the intricacies of AI
systems, into design processes for urban innovation [173].
We need to enable multiple stakeholders to contribute their technology solutions. Cities need
to build platforms that promote the co-creation and sharing of technology solutions [174,175]. While
cities have embraced the notion of open data [176] and have created periodic programs to source
innovation from external stakeholders (e.g., Hackathons) [177], much more is needed when it comes to
designing platforms for co-creation in the context of AI technologies. Stakeholders can be engaged
in the auditing of algorithms that underlie AI applications. In addition, they can provide feedback
on the performance of these systems, and identify critical choke points. As an example, consider the
following innovation by a resident in Berlin, who was able to create traffic jam alerts on Google Maps
by slowly moving 99 phones with location services turned on around the city [178]. Residents, such as
this individual, enable us to see the limits of AI technologies and their failure points. Such perspectives
are critically important as we infuse and design next-generation smart urban technologies.
AI systems will impact cities at multiple levels, from the individual, to the local community
(the residents), the neighborhood to the organizational (the city), and even the ecosystem (the city is
connected to other cities) level. Impacts at the local level, will have effects up and down the hierarchy.
Consider the case of algorithms used to promote sharing economy platforms (e.g., Uber, Lyft, AirBnB,
and so on). These algorithms not only provide opportunities to individuals to earn rent on their
assets and fees for their services, they also impact zoning rules, they impact the use of established
public transport networks, they in turn impact the creation of new service opportunities for existing
businesses, and even shape the nature of public finances of a city. More effort is needed to understand
the cascading effects of AI innovations across the various levels of a city’s functions. In addition, the
Energies 2020, 13, 1473 15 of 38

interdependencies between functions and the implications for overall objectives (i.e., ensuring that
local optimization does not compromise global performance) is also critical.
From a design perspective, research on how to design AI technologies in a more agile [179]
and frugal [180,181] manner is of critical importance. The public sector has a notorious record of
accomplishment when it comes to managing, and delivering on, information systems projects [182].
Cities around the world have had to contend with failed deployments of information system projects
that has wasted significant public resources. The study by Desouza et al. [183] provides examples of
both success and failure factors of technology-driven smart city attempts, including AI.
Given the significance of technology investments in our cities, we need to see vast improvements
in projects management to deliver on their intended value. In this regard, two considerations are
critical. First, we must build technologies that are agile, i.e., they have the ability to adopt, adjust, and
have the capacity for transformation under changing environmental conditions. Second, we must
build technologies in a manner that is in keeping with frugal engineering. Doing so will require us to
move away from mega-scale smart cities projects and reconsider the issue of scale. Today, a dominant
design paradigm is to build AI technologies that can scale and promote the re-use of components. This
thinking is outdated. Today, it is possible to build technologies that work for specific contexts, in an
agile and frugal manner, to promote personalization to a specific context and purpose [184].
The security of our next-generation urban technologies is of paramount importance. AI technologies,
like most technologies, should be secured and this normally takes the form of traditional information
security. Technologies that traverse our urban environments are already targets of hackers and have
vulnerabilities. For instance, Greenberg [185] highlights a deep flaw in cars that lets hackers shut
down safety features. But, even beyond what one thinks of when it comes to traditional security,
today, AI-driven systems can cause harm even if they are not hacked. For example, as stressed by the
BBC [186], China coronavirus misinformation spreads online about its origin and scale, i.e., AI inspired
platforms that are used for different urban functions can also be manipulated to spread fake news.
Cities need to be aware of this as they use these platforms to share information on urban functions, such
as the use of social media to engage with citizens. For instance, as reported by Martinez [187], “Rumors
of child abductors spread through WhatsApp in a small town in Mexico . . . . The rumors were fake,
but a mob burned two men to death before anyone checked.” Likewise, as we have discussed earlier,
cities have had their information systems been held for ransom. The incidents will only increase as
cities infuse more technologies into their environments. Hence, there is a need for research to examine
the security and risk implications of AI-enabled system deployments.
The success of AI deployment to make our cities smarter will depend on the knowledge and
care with which such technologies are deployed responsibility and in keeping with our public values.
If done well, AI can help us tackle some our most complex urban challenges. However, it can also
make our cities more fragile [54]. As stated by Stephen Hawking on the BBC, “The development of
full artificial intelligence could spell the end of the human race. It would take off on its own, and
re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution,
couldn’t compete, and would be superseded.”
On that very point, Gherhes and Obrad [188] report the findings of their study on technical and
humanities student perspectives on the development and sustainability of AI. The study discloses that
out of 928 participants 58.3% considers that AI will have a positive influence on the society. On the
other hand, the percentage of those confessing to being confused or concerned is also quite significant
(41.7%). The probability that AI might destroy humankind and replace people in certain activities and
jobs are among the greatest fears [188].
As the AI applications are becoming more common, there grows the skepticism on the misuse of
the technology. For example, most recently the Clearview AI facial recognition system has generated
major concerns on the privacy issues. Australian police have been using this unaccountable facial
recognition service that combines machine learning and wide-ranging data gathering practices to
identify members of the public from online photographs. As stated by Golenfein [189] “Beyond the
Energies 2020, 13, 1473 16 of 38

ethical arguments around facial recognition, Clearview AI reveals Australian law enforcement agencies
have such limited technical and organizational accountability that we should be questioning their
competency even to evaluate, let alone use, this kind of technology”. Similarly, the New South Wales
state government of Australia is using AI to spot drivers with mobile phones (often mixed phones
with other rectangular items), and the Australian government welfare agency Centrelink is using AI
(often incorrectly) to issue debt notices to welfare recipients [190].
Furthermore, scholars warn us of the possible risks of advanced AI. For instance, these risks
range from unsafe recommendations for treating illnesses [191] to fatal autonomous car accidents [192],
and from racist chatbots [193] to social manipulation [194]. While various dystopian futures have
been advanced, including those in which humans eventually become obsolete, with the subsequent
extinction of the human race, [195] put forward the following scenarios to think about the ways
to protect us from the risks of advanced AI: (a) An AI system tasked with preventing HIV decides
to eradicate the problem by killing everybody who carries the disease, or one tasked with curing
cancer decides to kill everybody who has any genetic predisposition for it; (b) An autonomous AI
military drone decides the only way to guarantee an enemy target is destroyed is to wipe out an entire
community, and; (c) An environmentally protective AI decides the only way to slow or reverse climate
change is to remove technologies and humans that induce it.
Lastly, abovementioned challenges also relate to the specific characteristics of AI technologies that
include opacity (‘black box-effect’), complexity, unpredictability and partially autonomous behavior,
may make it hard to verify compliance with and may hamper the effective enforcement of rules of
existing laws meant to protect fundamental rights [196]. In order to address this issue, the [197] white
paper entitled ‘Artificial intelligence: a European approach to excellence and trust’ underlined the
following seven key requirements for a successful AI utilization: (a) Human agency and oversight;
(b) Technical robustness and safety; (c) Privacy and data governance; (d) Transparency; (d) Diversity,
non-discrimination, and fairness; (e) Societal and environmental well-being, and; (f) Accountability.
On that very point, Salmon et al. [195] propose the immediate application of the following three sets of
controls for AI development and testing: (a) The controls required to ensure AI system designers and
developers create safe AI systems; (b) The controls that need to be built into the AIs themselves, such
as ‘common sense’, morals, operating procedures, decision-rules, and so on, and; (c) The controls that
need to be added to the broader systems in which AI will operate, such as regulation, codes of practice,
standard operating procedures, monitoring systems, and infrastructure. As Elon Musk stated, “we
need to regulate AI to combat an ’existential threat’ before it’s too late” [198]. Fortunately, we are not
short of ideas and plans to tackle these issues, and now is the time to implement them before it is too
late [199].

6. Conclusions
The study reported in this paper offers a novel contribution to the literature by mapping out the
scientific landscape of the understudied ‘AI and the smart city’ area. This study helps not only in
identifying the current and potential contributions of AI to the development of smarter cities—to aid
urban policymakers, planners and researchers—, but also in determining the gaps in the literature to
bridge them in prospective studies. The study also gives a heads up for urban policymakers, planners
and scholars for them to prepare for the disruptions that AI will cause in our cites, societies and
businesses [200].
The broad findings of our systematic literature review findings reveal that: (a) AI has an evident
potential—but only if utilized responsibly [201]—to provide a positive change in our cities, societies, and
businesses by promoting a more efficient, effective and sustainable transition/transformation [202,203],
and; (b) Particularly, AI, with its technology, algorithms, and learning capabilities, can be a useful
vehicle in automating the problem solving and decision-making processes that, in return, could reform
urban landscapes and support the development of smarter cities [62].
Energies 2020, 13, 1473 17 of 38

The specific findings of our systematic literature review disclose that: (a) AI in the context of
smart cities is an emerging field of research and practice; (b) The central focus of the literature is on AI
technologies, algorithms, and their current and prospective applications; (c) AI applications in the
context of smart cities mainly concentrate on business efficiency, data analytics, education, energy,
environmental sustainability, health, land use, security, transport, and urban management areas;
(d) There is limited scholarly research investigating the risks of wider AI utilization, and; (e) Upcoming
disruptions of AI on cities and societies have not been adequately investigated in the literature.
AI provides a new hope for addressing some of the urbanization problems we failed to solve due to
the complexities involved. Nevertheless, AI is not a silver bullet. While we are currently far away from
such advance application of AI, there are numerous contributions of the rapidly developing technology
for our cities and societies. Some of these contributions are presented in the paper and some warnings
have been made for the good use of the technology. While there is a promise of the emerging advanced
technologies, such as AI, our rapid urbanization, industrialization, and globalization practices are
perhaps making even technology struggle with coming up solution. The recent anthropogenic climate
change triggered environmental catastrophes and disasters—such as 2020 Australian Bushfires—and
urbanization and globalization triggered epidemics—such as COVID-19—require more than technology
for them not to be repeated again.
The paper opened with a viewpoint on technocentric solutions being widely seen as remedies for
global issues—including climate change and urbanization problems. Indeed, AI and other technologies
will definitely equip us with better data analytics and prediction models in more efficient and effective
ways. To date, there are two different approaches to AI: rules-based (coded algorithms of if-then
statements that are basically meant to solve simple problems) and learning-based (diagnoses problems
by interacting with the problem), where both AI approaches have valid use cases when it comes to
studying the environment and solving climate change. In other words, when it comes to helping solve
climate change, a learning-based AI could essentially do more than just crunch CO2 emission numbers,
where a learning-based AI could actually record those numbers, study causes and solutions, and then
recommend the best solution [204]—’in theory’. We say ‘in theory’, because “fully functioning AI
systems do not yet exist, and it has been estimated that they will be with us anywhere between 2029
and the end of the century” [195].
While we do not disagree with the positive contributions of technological prescriptions—such as
AI and other urban technologies—[205], we close the paper with the following quote by Andrew Ng,
co-founder and lead of Google Brain. “Much has been written about AI’s potential to reflect both the
best and the worst of humanity. For example, we have seen AI providing conversation and comfort
to the lonely; we have also seen AI engaging in racial discrimination. Yet the biggest harm that AI
is likely to do to individuals in the short term is job displacement, as the amount of work we can
automate with AI is vastly bigger than before. As leaders, it is incumbent on all of us to make sure we
are building a world in which every individual has an opportunity to thrive.”

Author Contributions: T.Y. designed the study, supervised the systematic review, and prepared the first draft
of the manuscript. L.B. and F.R. undertook the systematic review tasks, and identified the key findings. K.C.D.
contributed to the write up and improved the rigor, relevance and reach of the study. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Acknowledgments: This research did not receive any specific grant from funding agencies in the public,
commercial or not-for-profit sectors. The authors thank the managing editor and anonymous referees for their
invaluable comments on an earlier version of the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
Energies 2020, 13, 1473 18 of 38

Appendix A

Table A1. Analysis Highlights of the Reviewed Literature.

Author Title Journal Aim Relevance Domain Paradigm Application Method Technology
Advocates that AI in the LB
ES
transport field is aimed at KB FS
To provide an overview DN
Abduljabbar et al. Applications of artificial decreasing VKT thus PM DL Smart
Sustainability of AI techniques Environment NN
[98] intelligence in transport reducing emissions and ML SI Transport
applied to transport. DAI
other environmental EI GA
EA
degradation. SO
Wireless To develop a framework Describes the use of
A real-time patient
Communications that uses edge machine learning in PM NN IoT
Ajerla et al. [99] monitoring framework for Society AR
and Mobile computing to send data improving fall detection ML CV Smart Health
fall detection
Computing from wearable devices. devices.
LB
To review existing ES FS
Data fusion and IoT for Discusses the benefits of KB
literature on data fusion DN BN
Alam et al. [77] smart ubiquitous IEEE Access AI in relation to data Economy PM IoT
and IoT with a focus on NN DL
environments fusion. ML
mathematical models. EA GA
SO
Provides insights LB
To provide information
Allama & On big data, artificial regarding the application KB ES
Cities on the use of AI and big Governance FS IoT
Dhunny [100] intelligence and smart cities of AI to public safety and PM NN
data in smart cities.
security. ML
Survey on collaborative Provides insights into AR
To show how drones PM NN
Alsamhi et al. smart drones and internet how autonomous drones IR IoT
IEEE Access and IoT can improve the Governance ML CV
[101] of things for improving can be used for security, MV Drones
smartness of cities. SO EA
smartness of smart cities safety measures. GA
Describes how block
A unified framework for To address data
Altulyan et al. Multimedia Tools chain and fog computing
data integrity protection in integrity from an Economy n/a n/a n/a IoT
[149] and Applications can be used to manage
people-centric smart cities end-to-end perspective.
data integrity.
Prediction of environmental Journal of
To develop AI
Alzoubi et al. indicators in land levelling Environmental Discusses using AI in
techniques in land Environment ML NN n/a n/a
[102] using artificial intelligence Health Science and land levelling.
levelling.
techniques Engineering
LB ES FS
To develop a framework
Smart education with KB DN BN
for student learning Develops a framework for
Bajaj & Sharma artificial intelligence-based Procedia Computer PM PP BPS Smart
styles using learning AI to improve adaptivity Society
[82] determination of learning Science ML NN MAS Education
models and ratification in teaching.
styles EI DAI SI
intelligence.
SO EA GA
To developing a
Artificial intelligence Artificial Describes how AI could
Bennett & Hauser framework for using AI PM
framework for simulating Intelligence in lead to improvements in Society DAI MAS Smart Health
[137] to address healthcare EI
clinical decision-making Medicine diagnosis and treatment.
challenges.
Energies 2020, 13, 1473 19 of 38

Table A1. Cont.

Author Title Journal Aim Relevance Domain Paradigm Application Method Technology
Artificial intelligence To explain application Provides insights into the
LB ES
techniques in smart grid Proceedings of the of AI in smart grids and use of smart grids for FS
Bose [78] Environment KB NN Smart Energy
and renewable energy IEEE renewable energy prediction, estimation and DL
ML AS
systems systems control of power systems.
To identify innovative Provides insights into
methods for answering how AI can improve
The challenge of big data Annual Review of
Brady [103] previously decision-making, Economy ML NN n/a n/a
and data science Political Science
hard-to-tackle questions efficiency, and reduce
about society. errors and uncertainty.
To identify possible Provides insights into the
Security and privacy Sustainable Cities Smart
Braun et al. [83] solutions to five smart use of AI for cyber Governance ML NN n/a
challenges in smart cities and Society Surveillance
city challenges. security.
Describes the potential for
To develop a
AI and smart traffic
computational
control systems to
Computational negotiation approach on
Information communicate with ML
Bui & Jung [104] negotiation-based edge IoT systems where Economy AS n/a IoT
Sciences connected-AV, and make EI
analytics for smart objects distributed edge devices
real-time decisions to
can make their own
improve the efficiency of
decisions.
the transport network.
To develop an accurate
Deep learning-based video Discusses using AI for
and real-time video
system for accurate and IEEE Internet of real-time measurements IoT
Cai et al. [105] system for future IoT Environment ML NN DL
real-time parking Things Journal to make parking more Smart Parking
and smart cities
measurement efficient.
applications
To identify AI
The brain of the future and Identifies the potential for
implications and the
Casares [84] the viability of democratic Futures AI to contribute to public Governance ML NN DL n/a
potential challenges in
governance governance.
democratic societies.
Journal of Ambient To combine a version of Describes the use of AI in
Castelli et al. Predicting per capita violent Intelligence and genetic programming crime prediction and
Governance SO EA GA n/a
[144] crimes in urban areas Humanized with a local search optimal allocation of law
Computing method enforcement.
Identifies the potential for Smart
To identify the
AI to develop innovative Education
Chassignol et al. Artificial Intelligence trends Procedia Computer prospective impact of
teaching methods, and Society ML NN n/a Augment.
[85] in education Science AI technologies on the
improve student Reality
study process.
outcomes. Virtual Reality
To combine IoT with AI
in smart machines to Describes the use of AI to
Government
Chatterjee et al. Success of IoT in smart simulate intelligent obtain data from IoT to IoT
Information Governance n/a n/a n/a
[164] cities of India behavior and assist understand acceptance of ICT
Quarterly
autonomous decision new technologies.
making.
Energies 2020, 13, 1473 20 of 38

Table A1. Cont.

Author Title Journal Aim Relevance Domain Paradigm Application Method Technology
To reviewing the current Provides insights into LB ILP
A review on integration of FS
Marine Pollution state-of-the-art AI and how AI can be used to KB ES Smart
Chau [69] artificial intelligence into Environment DL
Bulletin its application in water develop more accurate ML NN Environment
water quality modelling GA
quality modelling. water quality modelling SO EA
Identifies how AI can
reduce uncertainty in
relation to robustness
An intelligent robust IEEE To enhance the
optimization, improve the ML NN DL IoT
Chen et al. [107] networking mechanism for Communications robustness of IoT Economy
cost and efficiency of SO EA GA Smart Energy
the internet of things Magazine topologies
network communications
and protect against
cyber-attacks.
To provide information
IEEE Transactions Provides insights into
regarding current
on Green how AI can be used to
Chen et al. [106] Cognitive-LPWAN wireless communication Economy ML NN DL IoT
Communications improve communication
technologies, and other
and Networking networks
technologies
To investigate using
intelligent transport Describes using
Multimedia Tools systems for improving intelligent transport PM IR Smart
Chmiel [74] INSIGMA Environment CV
and Applications safety, mobility and systems to improve ML MV Transport
environmental congestion.
outcomes.
To show ways in which Provides insights in the
Energy sustainability in ML IoT
Chui et al. [86] Energies AI can support energy use of AI to monitor Environment EA GA
smart cities SO Smart Energy
sustainability. energy consumption.
To provide an overview
Artificial intelligence and Identifies how AI can LB
Applied of the impact of AI on Expert System Smart
Cortes et al. [136] environmental decision assist in environmental Environment KB n/a
intelligence environmental decision DN Environment
support systems decision-making PM
support systems.
Big data analytics and
To develop a new online
network calculus enabling IoT
IEEE Internet of AV fleet management Discusses using AI to
Cui et al. [108] intelligent management of Environment ML n/a n/a Smart
Things Journal scheme that controls reduce travel time in AV.
autonomous vehicles in a Transport
congestion in cities.
smart city
Intelligent system for To develop a new Describes the use of AI to PM CV IR
Information
De Paz et al. [75] lighting control in smart intelligent lighting control public lighting to Environment ML NN MV Smart Energy
Sciences
cities system for cities. optimize power usage. EI DAI MAS
Energies 2020, 13, 1473 21 of 38

Table A1. Cont.

Author Title Journal Aim Relevance Domain Paradigm Application Method Technology
Discusses how cognitive
computing systems are
able simulate human
Designing, developing, and To reflect and provide
Desouza et al. thought and learning and
deploying artificial Business Horizons insights from AI projects Governance ML NN DL n/a
[109] can be used for fraud
intelligence systems in the public sector.
detection,
decision-support, and
online assistance.
Discusses how AI can
To survey important
improve adaptability in
Web intelligence and Educational aspects of web
learning environments, Smart
Devedzic [147] artificial intelligence in Technology & intelligence in the Society EI DAI ABM
and create more Education
education Society context of AI in
comfortable learning
education
environments.
Identifies machine
To examine different IoT learning as an important
Machine learning in the Future Generation
Din et al. [110] based machine learning component for IoT Economy ML NN DL IoT
internet of things Computer Systems
mechanisms particularly regarding
data management.
Presents the benefits and
DL
To present trends, disadvantages of NLP
Dobrescu & Global Economic PM IR
Artificial intelligence (AI) analyses and integration of AI into all Society CV n/a
Dobrescu [87] Observer ML NLU
perceptions of AI. areas of socio-economic NN
NLG
life
Identifies IoT and AI as
To examine the
Energy-efficient fair two of the most important
convexity of the
cooperation fog computing IEEE Internet of technologies to help
Dong et al. [111] optimization problem Economy ML n/a n/a IoT
in mobile edge networks for Things Journal enable smart cities
and design a fairness
smart city particularly regarding big
cooperation algorithm.
data analysis.
Discusses how AI can
stimulate problem
International To review studies that
solving, particularly in LB
Drigas & Artificial intelligence in Journal of use AI methods in ES Smart
special needs students, to Society KB FS
Ioannidou [71] special education Engineering making accurate NN Education
enhance the way children ML
Education diagnosis.
interact with their
environment.
I, teacher: using artificial
Examines the role of Social Robots
Edwards et al. intelligence (AI) and social Communication To argue the importance
teacher in an AI enabled Society ML NLP NLG Smart
[88] robots in communication Education of using AI in teaching.
education system. Education
and instruction
Energies 2020, 13, 1473 22 of 38

Table A1. Cont.

Author Title Journal Aim Relevance Domain Paradigm Application Method Technology
To develop AI Discusses the use of AI
International
PTZ-surveillance coverage algorithm for adjusting technology to IoT
Eldrandaly et al. Journal of
based on artificial the orientation of automatically improve Governance EI DAI SI Smart
[148] Information
intelligence for smart cities pan-tilt-zoom the field of view of Surveillance
Management
surveillance cameras. surveillance cameras
A master attack Identifies the potential for
To identify solutions for
methodology for an automated tools to
Falco et al. [162] IEEE Access cyber safety of critical Governance n/a n/a n/a IoT
AI-based automated attack evaluate cyber threats to
infrastructure.
planner for smart cities infrastructure.
Describers how AI can
To present a hybrid AI LB
Hybrid artificial intelligence assist with ES FS
Feng & Xu [63] Expert Systems system for use in urban Environment KB n/a
approach to urban planning knowledge-based NN DL
planning. ML
decision making.
An intelligent surveillance To maximize the Presents an intelligent
Fernández et al. platform for large number of deployable surveillance platform for Smart
Sensors Governance PM CV IR
[138] metropolitan areas with units in surveillance surveillance of public Surveillance
dense sensor deployment while minimizing costs. spaces
The application of artificial
Intelligence in the process To monitor and control
Garlík Neural Network Discusses the use of AI for ML NN
of optimizing energy the operation of selected Environment GA Smart Energy
[79] World energy optimization SO EA
consumption in intelligent smart objects.
areas
To identify use of AI in
assessing education and
the relations between Identifies new roles for Smart
Guilherme [153] AI and education AI & Society Society n/a n/a n/a
teachers and students, teachers in education. Education
and students and
students.
To review the literature
The application of medical concerning the Identifies AI as a means to
artificial intelligence prospects of medical AI improve equality between
Guo & Li [89] Health Equity Society ML NN n/a Smart Health
technology in rural areas of technology, and rural and urban health
developing countries application in rural areas.
areas.
Describes how AI can
Artificial intelligence-based To discuss the links
contribute to PM DN BN
Guo et al. [90] semantic internet of things Sensors between AI and IoT in Environment IoT
environmental ML NN DL
in a user-centric smart city the context of smart city
monitoring.
Ipsum: an approach to IoT
smart volatile To create smart volatile ICT
Procedia Computer Discusses using AI for
Håkansson [91] ICT-infrastructures for ICT infrastructures in Society ML n/a n/a Cyber-Physical
Science customized health care.
smart cities and cities. Smart
communities Infrastructure
Energies 2020, 13, 1473 23 of 38

Table A1. Cont.

Author Title Journal Aim Relevance Domain Paradigm Application Method Technology
Describes how AI as a
monitoring tool can assist LB
Artificial intelligence ES FS
Hanson & Critical Care To review application of intensive care providers KB
applications in the intensive Society NN DL Smart Health
Marshall [67] Medicine AI in intensive care. and resulting in reduced ML
care unit EA GA
costs and improved SO
patient outcomes
LB
Identifies AI techniques
KB ES
Uncertainty in big data To review big data as beneficial to the FS
Hariri et al. [112] Journal of Big Data Economy PM DN IoT
analytics analytics. accurate and timely BN
ML EA
analysis of big data.
SO
URBAN-i: from urban
To develop framework
scenes to mapping slums,
for multipurpose Describes using deep
Ibrahim et al. transport modes, and Environment and PM CV IR
realistic-dynamic urban learning to differentiate Environment n/a
[113] pedestrians in cities using Planning B ML NN DL
modelling using deep spatial structures.
deep learning and
CNN
computer vision
Reflects, critically, on the
optimistic viewpoint of
AI in relation to its
Journal of Artificial potential to respond to
Inclezan & Overview: a critical view To advocate using AI to
Intelligence urban problems (e.g. Environment n/a n/a n/a n/a
Prádanos [156] on smart cities and AI solve urban problems.
Research congestion, population
growth, energy efficiency,
environmental
degradation and safety).
Intelligent remote
AR
monitoring of parking To develop an Describes using AI for
PM CV IR IoT
Iqbal et al. [114] spaces using licensed and IEEE Network intelligent parking parking utilization and Environment
ML NN MV Smart Parking
unlicensed wireless system model optimization.
DL
technologies
To summarize reviews DN BN
PM
Renewable and and state-of-the-art Describes the use of AI to PP BPS
ML
Jha et al. [80] Renewable energy Sustainable Energy research outcomes achieve renewable energy Environment NN GA Smart Energy
EI
Reviews related to renewable goals DAI MAS
SO
energies EA SI
To discuss the Identifies the opportunity
Smart cities: opportunities, Journal of Strategic
importance and for AI and smart cities to
Khalifa [161] challenges, and security Innovation and Governance n/a n/a n/a n/a
consequences of smart achieve better security
threats Sustainability
city development. measures
Smart home and artificial To determine smart LB ES FS
Provides insights into the
Kopytko et al. intelligence as environment homes and AI as KB NN DL IoT
Path of Science use of AI in smart homes Environment
[92] for the implementation of combined innovative ML DAI MAS Smart Homes
to achieve energy savings.
new technologies tools. SO EA GA
Energies 2020, 13, 1473 24 of 38

Table A1. Cont.

Author Title Journal Aim Relevance Domain Paradigm Application Method Technology
To provide insights into
Identifies trust as a
Blockchain and trust in a Environment and institutions that can be IoT
Kundu [115] fundamental part of smart Governance ML n/a n/a
smart city Urbanization Asia governed on blockchain Blockchain
city governance.
through smart contracts.
A comparative study of
PSO-ANN, GA-ANN,
To propose four new AI
ICA-ANN, and ABC-ANN Discusses the use of AI to ML NN DL
techniques for
Le et al. [116] in estimating the heating Applied Sciences improve energy efficiency Environment EI DAI SI Smart Energy
forecasting the heating
load of buildings’ energy in buildings SO EA GA
load of buildings
efficiency for smart city
planning
To present an AI-based
AI-based sensor system which supports IoT
Describes using AI-based
information fusion for deep supervised GNS
Leung et al. [117] Sensors sensor to collect data from Environment ML NN DL
supporting deep supervised learning of transport GPS
multiple sources.
learning data collected from GIS
sensors
To propose the use of a
smart metasurface Identifies the potential for
IoT
Intelligent metasurface Light: Science & imager and recognizer, AI enabled sensors and NN DL
Li et al. [118] Society ML Smart
imager and recognizer Applications empowered by a other devices to monitor CV MV
Surveillance
network of ANN to health.
control data flow
To improve the Describes the use of
Object tracking in vary
robustness and accuracy intelligent surveillance
lighting conditions for fog AR
of the correlation systems in detecting PM Smart
Liu et al. [93] based intelligent IEEE Access Governance CV IR
filter-based trackers for abnormal circumstances, ML Surveillance
surveillance of public MV
handling intense identifying and tracking
spaces
illumination change. targets.
To highlight the
influence of the
Modeling and simulation of
hyper-parameter Develops a model that
robot inverse dynamics
settings on model uses deep learning to
Liu et al. [119] using LSTM-based deep IEEE Access Economy ML NN DL Robotics
performance and to make robots more
learning algorithm for
explore the applicability responsive to uncertainty.
smart cities and factories
of the Long Short-Term
Memory model.
To advocate for AI
systems to focus on
enhancing human
Discusses goals required
Lukowicz & How to avoid an AI cognitive capabilities, PM CV IR
Interactions to improve AI Economy n/a
Slusalle [94] interaction singularity and develop creativity, ML NLP NLU
decision-making.
inventiveness, and
intuition, trust, ethics,
and values
Energies 2020, 13, 1473 25 of 38

Table A1. Cont.

Author Title Journal Aim Relevance Domain Paradigm Application Method Technology
To identify use of AI to Describes how smart
Data analytics in smart improve quality of life healthcare analytics can IoT
Lytras et al. [120] Applied Sciences Society ML NN DL
healthcare and relieve medical improve quality of life for Smart Health
shortages patients.
To analyze the impact
Revista de and perspectives on Describes how cognitive
Towards smart city Tecnologia da adopting processing could allow
Martins [95] Governance ML NN DL n/a
innovation Informação e software-defined innovative solutions to
Comunicação networking and AI for complex problems.
smart city projects.
To summarize current
Journal of
McArthur et al The roles of artificial applications of ideas Identifies future uses of LB Smart
Educational Society ES n/a
[140] intelligence in education from Al to education AI in the education field. KB Education
Technology
field.
Provides insights into the
Mobile power
To maximize the profit use of AI to optimize
infrastructure planning and
Meena et al. [145] Energy Procedia of utility and electric energy consumption Environment SO EA GA IoT
operational management
vehicle owners. particularly electrical
for smart city applications
vehicles.
To highlight the key
challenges of data
Intelligent and
IEEE prioritization, its future Discusses the use of AI to
Muhammad et al. energy-efficient data
Communications requirements, and improve the efficiency of Environment ML NN DL IoT
[121] prioritization in green smart
Magazine propositions for data prioritization.
cities
integration into green
smart cities
To describe the general
MUSA–I: towards new Multidisciplinary architecture and current
Discusses using AI for AR
social tools for advanced Digital Publishing implementation of an PM Smart
Nápoles et al. [96] transport demand Environment CV IR
multi-modal transportation Institute explicit multi-modal ML Transport
management. MV
in smart cities Proceedings transport demand
system for smart cities.
Using design science and To describe how the use Discusses how AI can
Neuhauser et al. artificial intelligence to Patient Education of AI can improve the improve the effectiveness LB
Society ES n/a Smart Health
[142] improve health and Counselling effectiveness of health of communication in KB
communication communication. health settings.
To review the Identifies AI potential to
Artificial intelligence
Noorbakhsh-Sabet The American applications for increase learning and
transforms the future of Society ML NN DL Smart Health
[122] Journal of Medicine machine learning in decision support in the
healthcare
healthcare. health sector.
Energies 2020, 13, 1473 26 of 38

Table A1. Cont.

Author Title Journal Aim Relevance Domain Paradigm Application Method Technology
To propose new fire
detection system using
Dependable fire detection
a multifunctional AI Describes how machine IoT
system with
Park et al. [123] Sensors framework and data learning can improve fire Governance ML NN DL Smart Fire
multifunctional artificial
transfer delay detection systems. Detection
intelligence framework
minimization
mechanism.
To analyze discussions KB ES
The coming of age of
which reflect on AI in Discusses the use of AI in PM DN BN
Patel et al. [70] artificial intelligence in Path of Science Society Smart Health
the medical research medical care. ML NN ABM
medicine
field. EI DAI
Identifies the need for
Journal of education to be adaptive
Pence Artificial intelligence in To explore the use of AI
Educational in the face of rapid Society n/a n/a n/a n/a
[152] higher education in education
Technology Systems technology advances, and
changes to employment.
To investigate the
Ethics and Describes the importance
Pieters relationship between LB
Explanation and trust Information on online security for Governance ES n/a n/a
[141] explanation and trust in KB
Technology trust in AI systems.
the context of AI
An indoor predicting To predict the
Identifies methods of IoT
climate conditions approach temperature of remote
Ponce & incorporating AI in Artificial
using internet-of-things and Measurement locations using field Environment ML NN n/a
Gutiérrez [124] weather monitoring to Hydrocarbon
artificial hydrocarbon sensors and information
better predict changes. Networks
networks from network.
Hybrid artificial intelligence
To develop an IoT based Provides insights into the
and internet of things
system to generate use of piezoelectric IoT
Puri et al. [125] model for generation of IEEE Access Environment ML NN n/a
electrical energy from sensors to generate Smart Energy
renewable resource of
multiple sensors. energy from body heat.
energy
To develop a smart
design framework Provides insights into the
Artificial intelligence-aided Environment and
Quan et al. [146] which uses AI to assist use of AI in the design Environment SO EA GA Smart Design
design Planning B
urban design process
decision-making.
To propose
blockchain-based Identifies benefits of AI in
Blockchain and IoT-based
infrastructure to helping with data
Rahman et al. cognitive edge framework IoT
IEEE Access support security- and collection, fusing Economy ML NN DL
[126] for sharing economy Blockchain
privacy-oriented information from
services in a smart city
spatio-temporal smart multiple sources.
contract services.
Energies 2020, 13, 1473 27 of 38

Table A1. Cont.

Author Title Journal Aim Relevance Domain Paradigm Application Method Technology
Provides insights into LB
Annals of The Royal To explore the ES FS
Artificial intelligence in how AI can help with the KB
Ramesh et al. [68] College of Surgeons proficiency of AI in Society NN DL Smart Health
medicine analysis of complex ML
of England medicine. EA GA
medical data. SO
To develop a platform
LB ES
Artificial intelligence which serves as a Identifies how AI can be
Acta Technica KB CV FS
Reaz techniques for advanced reference point for used to provide more
Corvininesis-Bulletin Environment PM NN AR Smart Home
[73] smart home developing more efficient power
of Engineering ML AS MAS
implementation cutting-edge smart consumption
EI DAI
home technologies.
Advanced issues in artificial
Engineering To review topics
intelligence and pattern Describes the use of AI in Smart
Applications of strongly related to the
Rho et al. [72] recognition for intelligent home surveillance Governance ML n/a n/a Surveillance
Artificial intelligent surveillance
surveillance system in systems Smart Homes
Intelligence systems in smart homes.
smart home environment
To review papers to
International Describes how AI will
Evolution and revolution in identify the focus and
Journal of Artificial impact the job market and LB Smart
Roll & Wylie [143] artificial intelligence in typical scenarios that Society ES n/a
Intelligence in create effective KB Education
education occupy the field of AI
Education educational system.
and education.
To present the practical Identifies shared learning
Technology
viewpoints, cases and and cooperation as
Ruohomaa et al. Towards smart city concept Innovation
experiences relating to important factors in Economy ML n/a n/a IoT
[127] in small cities Management
the planning of smart increasing innovation and
Review
cities. growth in smart cities.
Describes the potential for
Artificial intelligence To reveal the potential ML NN
Sgantzos & Grigg AI to be an independent MAS IoT
implementations on the Future Internet combined applications Economy EI DAI
[128] source of knowledge and GA Blockchain
blockchain of AI and blockchain. SO EA
innovation.
Smart textile-integrated To provide an overview Describes the use of smart
Advanced
Shi et al. [129] microelectronic systems for of the progress of the textiles for health care Society ML NN n/a Smart Textiles
Materials
wearable applications smart textile field. monitoring.
Wiley Provides insights into the
To present classification
Interdisciplinary potential for machine
model that studies four
Soomro et al. Reviews: Data learning to complete ML NN
Smart city big data analytics aspects of research in Economy GA n/a
[130] Mining and complex statistical SO EA
the big data analytics
Knowledge analysis and make more
domain.
Discovery informed decisions.
Energies 2020, 13, 1473 28 of 38

Table A1. Cont.

Author Title Journal Aim Relevance Domain Paradigm Application Method Technology
Identifies AI as an
The socio-organizational Artificial To explore the great effective way to manage
Stefanelli [139] age of artificial intelligence Intelligence in challenges for AI in medical knowledge, and Society KB n/a n/a Smart Health
in medicine Medicine medicine. increase resources for
patient care.
Identifies the need for
Beyond ‘smart-only’ cities: Journal of Ambient To present the various privacy-by-design to
Streitz redefining the Intelligence and manifestations of the empower people and
Governance ML NN DL IoT
[131] ‘smart-everything’ Humanized smart everything enforce a citizen centric
paradigm Computing paradigm. approach to data
collection.
To develop a Provides insights into the
An artificial intelligence
classification of pre- and use of AI subsets artificial
application for
post-earthquake satellite neural networks and
Syifa et al. [132] post-earthquake damage Sensors Governance ML NN n/a n/a
images using ANN and support vector machine
mapping in Palu, Central
support vector machine classifiers to identify areas
Sulawesi, Indonesia
classifiers. affected by earthquakes
Value-based deep Describes using traffic
To identify the use of
Wan & Hwang reinforcement learning for IET Intelligent signal control methods for PM PP Smart
reinforcement learning Environment DL
[97] adaptive isolated Transport Systems transport system ML NN Transport
in signal controls.
intersection signal control optimization.
To better understand of
A review of artificial Renewable and Discusses the use of AI in
Wang & the use of ensemble PM PP
intelligence-based building Sustainable Energy building use energy Environment n/a n/a
Srinivasan [81] models for predicting ML NN
energy use prediction Reviews predictions.
building energy use.
To develop an AI
Exploring the application of scheme for identifying
artificial intelligence spatiotemporal water
Identifies the potential for
technology for quality distributions
Science of The Total AI to monitor water Smart
Wang et al. [133] identification of water and the relationships Environment ML NN DL
Environment pollutant levels and Environment
pollution characteristics between water quality
changes
and tracing the source of indicators and
water quality pollutants industrial point sources
of pollutants.
Describes how Ai can be
Conventional models and To review conventional
used in energy forecasting PM DN BN
artificial intelligence-based Journal of models and AI based
to assist with identifying ML NN DL
Wei et al. [134] models for energy Petroleum Science models in energy Environment Smart Energy
inefficiencies in energy EI DAI SI
consumption forecasting: a and Engineering consumption
consumption and SO EA GA
review forecasting.
pollution prevention.
Artificial intelligence, smart To investigate impact of
Journal of Cases on Describes the potential
classrooms and online AI innovations in the Smart
Wogu et al. [135] Information changes AI will bring to Society ML n/a n/a
education in the 21st education sector and on Education
Technology the education sector.
century human development
Energies 2020, 13, 1473 29 of 38

Table A1. Cont.

Author Title Journal Aim Relevance Domain Paradigm Application Method Technology
To increase
KB ES FS
Artificial intelligence understanding of how Discusses the use of AI in
Journal of Planning ML NN ABM
Wu & Silva [26] solutions for urban land AI approaches urban identifying the dynamics Environment n/a
Literature EI DAI SI
dynamics and land dynamics of urban land use.
SO EA GA
modelling processes.
To design a blockchain
Decentralized big data Identifies the potential for
instantiation and
auditing for smart city AI to processing and
Yu et al. [150] IEEE Access conduct a comparison Economy n/a n/a n/a Blockchain
environments leveraging analyzing large amounts
between the existing
blockchain technology of data
and proposed schemes.
Not deep learning but
To build an interaction Investigates the potential
autonomous learning of
model between direct for AI to develop ML AS
Yun et al. [76] open innovation for Sustainability Economy SI n/a
and autonomous autonomous learning EI DAI
sustainable artificial
learning. capabilities.
intelligence
Exploring urban population
Computer
forecasting and spatial To improve the
Modeling in Describes the use of AI in
Zou et al. [157] distribution modeling with precision of small area Environment n/a n/a n/a n/a
Engineering & population forecasting
artificial intelligence population forecasting.
Sciences
technology
Notes: n/a= not available as not identified in the article. AI Paradigms: Logic-based (LB), Knowledge-based (KB), Probabilistic Methods (PM), Machine Learning (ML), Embodied
Intelligence (EI), and Search and Optimization (SO). AI Applications: Autonomous Systems (AS), Computer Vision (CV), Distributed Artificial Intelligence (DAI), Decision Networks (DN),
Evolutionary Algorithms (EA), Expert Systems (ES), Inductive Logic Programming (ILP), Natural Language Processing (NLP), Neural Networks (NN), and Probabilistic Programming
(PP). AI Methods: Agent-Based Modelling (ABM), Activities Recognition (AR), Bayesian Networks (BN), Bayesian Program Synthesis (BPS), Deep Learning (DL), Fuzzy Systems (FS),
Genetic Algorithms (GA), Image Recognition (IR), Multi-Agent Systems (MAS)., Machine Vision (MV), Natural Language Generation (NLG), Natural Language Understanding (NLU), and
Swarm Intelligence (SI).
Energies 2020, 13, 1473 30 of 38

References
1. Oreskes, N. The scientific consensus on climate change. Science 2004, 306, 1686. [CrossRef]
2. Cook, J.; Nuccitelli, D.; Green, S.A.; Richardson, M.; Winkler, B.; Painting, R.; Skuce, A. Quantifying the
consensus on anthropogenic global warming in the scientific literature. Environ. Res. Lett. 2013, 8, 024024.
[CrossRef]
3. Yigitcanlar, T.; Foth, M.; Kamruzzaman, M. Towards post-anthropocentric cities: Reconceptualizing smart
cities to evade urban ecocide. J. Urban Technol. 2019, 26, 147–152. [CrossRef]
4. Kankanamge, N.; Yigitcanlar, T.; Goonetilleke, A.; Kamruzzaman, M. Determining disaster severity through
social media analysis: Testing the methodology with South East Queensland Flood tweets. Int. J. Disaster
Risk Reduct. 2020, 42, 101360. [CrossRef]
5. Sotto, D.; Philippi, A.; Yigitcanlar, T.; Kamruzzaman, M. Aligning urban policy with climate action in the
global South: Are Brazilian cities considering climate emergency in local planning practice? Energies 2019,
12, 3418. [CrossRef]
6. Yu, P.; Xu, R.; Abramson, M.J.; Li, S.; Guo, Y. Bushfires in Australia: A serious health emergency under
climate change. Lancet Planet. Health 2020, 4, 7–8. [CrossRef]
7. Zhenmin, L.; Espinosa, P. Tackling climate change to accelerate sustainable development. Nat. Clim. Chang.
2019, 9, 494–496. [CrossRef]
8. Kinniburgh, C. Can extinction rebellion survive? Dissent 2020, 67, 125–133. [CrossRef]
9. Tomitsch, M.; Haeusler, M.H. Infostructures: Towards a complementary approach for solving urban
challenges through digital technologies. J. Urban Technol. 2015, 22, 37–53. [CrossRef]
10. Yigitcanlar, T.; Kamruzzaman, M. Does smart city policy lead to sustainability of cities? Land Use Policy 2018,
73, 49–58. [CrossRef]
11. Yigitcanlar, T.; Inkinen, T. Geographies of Disruption: Place Making for Innovation in the Age of Knowledge Economy;
Springer International Publishing: Cham, Switzerland, 2019.
12. Yigitcanlar, T.; Kamruzzaman, M. Smart cities and mobility: Does the smartness of Australian cities lead to
sustainable commuting patterns? J. Urban Technol. 2019, 26, 21–46. [CrossRef]
13. Arbolino, R.; De Simone, L.; Carlucci, F.; Yigitcanlar, T.; Ioppolo, G. Towards a sustainable industrial ecology:
Implementation of a novel approach in the performance evaluation of Italian regions. J. Clean. Prod. 2018,
178, 220–236. [CrossRef]
14. Yigitcanlar, T.; Kamruzzaman, M.D.; Buys, L.; Perveen, S. Smart Cities of the Sunshine State: Status of
Queensland’s Local Government Areas. Available online: https://eprints.qut.edu.au/118349/ (accessed on
10 February 2020).
15. Desouza, K.C.; Swindell, D.; Smith, K.L.; Sutherland, A.; Fedorschak, K.; Coronel, C. Local government 2035:
Strategic trends and implications of new technologies. Issues Technol. Innov. 2015, 27, 27.
16. Kyriazopoulou, C. Smart city technologies and architectures: A literature review. In Proceedings of the
2015 International Conference on Smart Cities and Green ICT Systems, Lisbon, Portugal, 20–22 May 2015;
pp. 1–12.
17. Ab-Rahman, A.; Hamid, U.Z.; Chin, T.A. Emerging technologies with disruptive effects: A review. Perintis
e-J. 2017, 7, 111–128.
18. Gatzweiler, F.W. Advancing urban health and wellbeing through collective and artificial intelligence: A
system approach 3.0. In Urban Health and Wellbeing Programme; Springer: Singapore, 2020; pp. 33–38.
19. King, B.A.; Hammond, T.; Harrington, J. Disruptive technology: Economic consequences of artificial
intelligence and the robotics revolution. J. Strateg. Innov. Sustain. 2017, 12, 53–67.
20. Tegmark, M. Life 3.0: Being Human in the Age of Artificial Intelligence; Knopf: New York, NY, USA, 2017.
21. Yigitcanlar, T. Technology and the City: Systems, Applications and Implications; Routledge: Abingdon upon
Thames, UK, 2016.
22. Batty, M. Artificial intelligence and smart cities. Environ. Plan. Urban Anal. City Sci. 2018, 45, 3–6. [CrossRef]
23. Schalkoff, R.J. Artificial Intelligence: An Engineering Approach; McGraw-Hill: New York, NY, USA, 1990.
24. Pannu, A. Artificial intelligence and its application in different areas. Artif. Intell. 2015, 4, 79–84.
25. Corea, F. AI Knowledge Map: How to Classify AI Technologies. Available online: https://www.forbes.
com/sites/cognitiveworld/2018/08/22/ai-knowledge-map-how-to-classify-ai-technologies/#5e99db627773
(accessed on 18 January 2020).
Energies 2020, 13, 1473 31 of 38

26. Wu, N.; Silva, E.A. Artificial intelligence solutions for urban land dynamics: A review. J. Plan. Lit. 2010, 24,
246–265.
27. Jiafeng, Z.; Tian, L.; Lin, Z. Artificial intelligence approach to creative data manipulation for optimisation of
livelihood oriented urban planning and management. Int. J. Perform. Eng. 2019, 15, 602–610.
28. Wirtz, B.W.; Weyerer, J.C.; Geyer, C. Artificial intelligence and the public sector: Applications and challenges.
Int. J. Public Adm. 2019, 42, 596–615. [CrossRef]
29. Mendling, J.; Decker, G.; Hull, R.; Reijers, H.A.; Weber, I. How do machine learning, robotic process
automation, and blockchains affect the human factor in business process management? Commun. Assoc. Inf.
Syst. 2018, 43, 297–320. [CrossRef]
30. Faisal, A.; Yigitcanlar, T.; Kamruzzaman, M.; Currie, G. Understanding autonomous vehicles: A systematic
literature review on capability, impact, planning and policy. J. Transp. Land Use 2019, 12, 45–72. [CrossRef]
31. Japan Times. ANA Starts Testing Semi-Autonomous Bus at Haneda Airport for Passengers and Staff.
Available online: https://www.japantimes.co.jp/news/2020/01/22/national/ana-starts-testing-autonomous-
bus-operation-haneda-airport/#.XjN_rGgzZnI (accessed on 31 January 2020).
32. Yigitcanlar, T.; Wilson, M.; Kamruzzaman, M. Disruptive impacts of automated driving systems on the built
environment and land use: An urban planner’s perspective. J. Open Innov. Technol. Mark. Complex. 2019, 5,
24. [CrossRef]
33. Yigitcanlar, T.; Kamruzzaman, M.; Foth, M.; Sabatini-Marques, J.; da Costa, E.; Ioppolo, G. Can cities become
smart without being sustainable? A systematic review of the literature. Sustain. Cities Soc. 2019, 45, 348–365.
[CrossRef]
34. Houston Chronicle. Robot Police Coming to Houston Transit Center, Rail Platform, Park and Ride Lot.
Available online: https://www.houstonchronicle.com/news/transportation/article/Robot-police-coming-to-
Houston-transit-center-14999004.php (accessed on 23 January 2020).
35. Washington Post. One Solution for Keeping Traffic Stops from Turning Violent: A Robot that Separates
Police Officers from Drivers. Available online: https://www.washingtonpost.com/technology/2019/05/14/
one-solution-keeping-traffic-stops-turning-violent-robot-that-separates-police-officers-drivers (accessed on
25 January 2020).
36. Swindell, D.; Desouza, K.C.; Hudgens, R. Dubai Offers Lessons for Using Artificial Intelligence in Local Government;
Brookings: Washington, DC, USA, 2018.
37. Chang, C.W.; Lee, H.W.; Liu, C.H. A review of artificial intelligence algorithms used for smart machine tools.
Inventions 2018, 3, 41. [CrossRef]
38. Desouza, K.C.; Smith, K.L. Big Data and Planning; American Planning Association: Washington, DC, USA,
2016; Volume 585, pp. 2–102.
39. Desouza, K.C. Delivering Artificial Intelligence in Government: Challenges and Opportunities; IBM Center for the
Business of Government: Washington, DC, USA, 2018.
40. Thakuriah, P.V.; Tilahun, N.Y.; Zellner, M. Seeing Cities through Big Data; Springer: Singapore, 2017; pp. 1–9.
41. Sadilek, A.; Kautz, H.; DiPrete, L.; Labus, B.; Portman, E.; Teitel, J.; Silenzio, V. Deploying nEmesis: Preventing
foodborne illness by data mining social media. In Proceedings of the 13th IAAI Conference on AI, Seattle,
WA, USA; 2016; pp. 3982–3989.
42. BBC. Durham Police AI to Help with Custody Decisions. Available online: http://www.bbc.com/news/
technology-39857645 (accessed on 31 January 2020).
43. Accenture. Accenture Helps Seattle Police Department Implement Data Analytics Platform. Available
online: https://newsroom.accenture.com/news/accenture-helps-seattle-police-department-implement-data-
analytics-platform.htm (accessed on 1 February 2020).
44. Fastcompany. San Diego’s Massive, 7-Year Experiment with Facial Recognition Technology Appears to be a
Flop. Available online: https://www.fastcompany.com/90440198/san-diegos-massive-7-year-experiment-
with-facial-recognition-technology-appears-to-be-a-flop (accessed on 17 February 2020).
45. Madaio, M.; Haimson, O.L.; Zhang, W.; Cheng, X.; Hinds-Aldrich, M.; Dilkina, B.; Chau, D.H.P. Identifying
and Prioritizing Fire Inspections: A Case Study of Predicting Fire Risk in Atlanta; Bloomberg: New York, NY,
USA, 2015.
46. Yigitcanlar, T.; Han, H.; Kamruzzaman, M. Approaches, advances, and applications in the sustainable
development of smart cities: A commentary from the guest editors. Energies 2019, 12, 4554. [CrossRef]
Energies 2020, 13, 1473 32 of 38

47. New York Times. Another Hacked Florida City Pays a Ransom, This Time for $460,000. Available
online: https://www.nytimes.com/2019/06/27/us/lake-city-florida-ransom-cyberattack.html (accessed on
1 February 2020).
48. MIT News. System Predicts 85 Percent of Cyber-Attacks Using Input from Human Experts. Available
online: http://news.mit.edu/2016/ai-system-predicts-85-percent-cyber-attacks-using-input-human-experts-
0418 (accessed on 30 January 2020).
49. Smith, S.F.; Barlow, G.J.; Xie, X.F.; Rubinstein, Z.B. Smart urban signal networks: Initial application of the
SURTRAC adaptive traffic signal control system. In Proceedings of the 23rd International Conference on
Automated Planning and Scheduling, Rome, Italy, 10–14 June 2013; pp. 434–442.
50. Curbed. Criticism Mounts over Detroit Police Department’s Facial Recognition Software.
Available online: https://detroit.curbed.com/2019/7/8/20687045/project-green-light-detroit-facial-recognition-
technology (accessed on 1 February 2020).
51. The Guardian. What is Fake News? How to Spot It and What You Can Do to Stop It. Available online: https:
//www.theguardian.com/media/2016/dec/18/what-is-fake-news-pizzagate (accessed on 1 February 2020).
52. Washington Post. N.C. Man Told Police He Went to D.C. Pizzeria with Gun to Investigate Conspiracy Theory.
Available online: https://www.washingtonpost.com/news/local/wp/2016/12/04/d-c-police-respond-to-report-
of-a-man-with-a-gun-at-comet-ping-pong-restaurant (accessed on 1 February 2020).
53. Azcentral. A Slashed Tire, A Pointed Gun, Bullies on the Road: Why do Waymo Self-Driving Vans Get So
Much Hate? Available online: https://www.azcentral.com/story/money/business/tech/2018/12/11/waymo-
self-driving-vehicles-face-harassment-road-rage-phoenix-area/2198220002 (accessed on 1 February 2020).
54. Selby, J.D.; Desouza, K.C. Fragile cities in the developed world: A conceptual framework. Cities 2019, 91,
180–192. [CrossRef]
55. Desouza, K.C.; Selby, J.D. How Technological Progress Can Cause Urban Fragility; Brookings Institute: Washington,
DC, USA, 2019.
56. NHTSA. The Automatic Emergency Braking (AEB) or Autopilot Systems May Not Function as Designed,
Increasing the Risk of a Crash. Available online: https://static.nhtsa.gov/odi/inv/2016/INCLA-PE16007-7876.
pdf (accessed on 1 February 2020).
57. CNN. Chrysler Recalls 1.4 Million Hackable Cars. Available online: http://money.cnn.com/2015/07/24/
technology/chrysler-hack-recall/index.html (accessed on 1 February 2020).
58. CNN. Chryslers Can be Hacked over the Internet. Available online: http://money.cnn.com/2015/07/21/
technology/chrysler-hack/index.html (accessed on 1 February 2020).
59. Slate. Autonomous Vehicles will Cost Local Governments Big Bucks. Available online: http://www.slate.
com/blogs/future_tense/2015/06/16/autonomous_vehicles_will_cost_local_governments_big_bucks.html
(accessed on 1 February 2020).
60. Az Central. Self-Driving Cars may Cost Cities. Available online: http://www.azcentral.com/story/news/
arizona/politics/2015/07/01/self-driving-cars-cityrevenue/29598929 (accessed on 1 February 2020).
61. Yigitcanlar, T.; Kamruzzaman, M.; Buys, L.; Ioppolo, G.; Sabatini-Marques, J.; da Costa, E.M.; Yun, J.J.
Understanding ‘smart cities’: Intertwining development drivers with desired outcomes in a multidimensional
framework. Cities 2018, 81, 145–160. [CrossRef]
62. Yigitcanlar, T.; Han, H.; Kamruzzaman, M.; Ioppolo, G.; Sabatini-Marques, J. The making of smart cities: Are
Songdo, Masdar, Amsterdam, San Francisco and Brisbane the best we could build? Land Use Policy 2019, 88,
104187. [CrossRef]
63. Feng, S.; Xu, L. An intelligent decision support system for fuzzy comprehensive evaluation of urban
development. Expert Syst. Appl. 1999, 16, 21–32. [CrossRef]
64. Shrivastava, R.; Mahajan, P. Artificial intelligence research in India: A scientometric analysis. Sci. Technol.
Libr. 2016, 35, 136–151. [CrossRef]
65. Makarynskyy, O.; Makarynska, D.; Kuhn, M.; Featherstone, W.E. Predicting sea level variations with artificial
neural networks at Hillarys Boat Harbour, Western Australia. Estuar. Coast. Shelf Sci. 2004, 61, 351–360.
[CrossRef]
66. Aziz, K.; Haque, M.M.; Rahman, A.; Shamseldin, A.Y.; Shoaib, M. Flood estimation in ungauged catchments:
Application of artificial intelligence-based methods for Eastern Australia. Stoch. Environ. Res. Risk Assess.
2017, 31, 1499–1514. [CrossRef]
Energies 2020, 13, 1473 33 of 38

67. Hanson, C.W.; Marshall, B.E. Artificial intelligence applications in the intensive care unit. Crit. Care Med.
2001, 29, 427–435. [CrossRef] [PubMed]
68. Ramesh, A.N.; Kambhampati, C.; Monson, J.R.; Drew, P.J. Artificial intelligence in medicine. Ann. R. Coll.
Surg. Engl. 2004, 86, 334–338. [CrossRef] [PubMed]
69. Chau, K.W. A review on integration of artificial intelligence into water quality modelling. Mar. Pollut. Bull.
2006, 52, 726–733. [CrossRef]
70. Patel, V.L.; Shortliffe, E.H.; Stefanelli, M.; Szolovits, P.; Berthold, M.R.; Bellazzi, R.; Abu-Hanna, A. The
coming of age of artificial intelligence in medicine. Artif. Intell. Med. 2009, 46, 5–17. [CrossRef]
71. Drigas, A.S.; Ioannidou, R.E. Artificial intelligence in special education: A decade review. Int. J. Eng. Educ.
2012, 28, 1366.
72. Rho, S.; Min, G.; Chen, W. Advanced issues in artificial intelligence and pattern recognition for intelligent
surveillance system in smart home environment. Eng. Appl. Artif. Intell. 2012, 25, 1299–1300. [CrossRef]
73. Reaz, M.B. Artificial intelligence techniques for advanced smart home implementation. Acta Tech. Corvininesis
Bull. Eng. 2013, 6, 51–57.
74. Chmiel, W.; Dańda, J.; Dziech, A.; Ernst, S.; Kadłuczka, P.; Mikrut, Z.; Pawlik, P.; Szwed, P.; Wojnicki, I.
INSIGMA: An intelligent transportation system for urban mobility enhancement. Multimed. Tools Appl. 2016,
75, 10529–10560. [CrossRef]
75. De Paz, J.F.; Bajo, J.; Rodríguez, S.; Villarrubia, G.; Corchado, J.M. Intelligent system for lighting control in
smart cities. Inf. Sci. 2016, 372, 241–255. [CrossRef]
76. Yun, J.; Lee, D.; Ahn, H.; Park, K.; Yigitcanlar, T. Not deep learning but autonomous learning of open
innovation for sustainable artificial intelligence. Sustainability 2016, 8, 797. [CrossRef]
77. Alam, F.; Mehmood, R.; Katib, I.; Albogami, N.N.; Albeshri, A. Data fusion and IoT for smart ubiquitous
environments: A survey. IEEE Access 2017, 5, 9533–9554. [CrossRef]
78. Bose, B.K. Artificial intelligence techniques in smart grid and renewable energy systems: Some example
applications. Proc. IEEE 2017, 105, 2262–2273. [CrossRef]
79. Garlík, B. The application of artificial intelligence in the process of optimizing energy consumption in
intelligent areas. Neural Netw. World 2017, 27, 415–446. [CrossRef]
80. Jha, S.K.; Bilalovic, J.; Jha, A.; Patel, N.; Zhang, H. Renewable energy: Present research and future scope of
artificial intelligence. Renew. Sustain. Energy Rev. 2017, 77, 297–317. [CrossRef]
81. Wang, Z.; Srinivasan, R.S. A review of artificial intelligence-based building energy use prediction: Contrasting
the capabilities of single and ensemble prediction models. Renew. Sustain. Energy Rev. 2017, 75, 796–808.
[CrossRef]
82. Bajaj, R.; Sharma, V. Smart education with artificial intelligence-based determination of learning styles.
Procedia Comput. Sci. 2018, 132, 834–842. [CrossRef]
83. Braun, T.; Fung, B.C.; Iqbal, F.; Shah, B. Security and privacy challenges in smart cities. Sustain. Cities Soc.
2018, 39, 499–507. [CrossRef]
84. Casares, A.P. The brain of the future and the viability of democratic governance: The role of artificial
intelligence, cognitive machines, and viable systems. Futures 2018, 103, 5–16. [CrossRef]
85. Chassignol, M.; Khoroshavin, A.; Klimova, A.; Bilyatdinova, A. Artificial intelligence trends in education: A
narrative overview. Procedia Comput. Sci. 2018, 136, 16–24. [CrossRef]
86. Chui, K.; Lytras, M.; Visvizi, A. Energy sustainability in smart cities: Artificial intelligence, smart monitoring,
and optimization of energy consumption. Energies 2018, 11, 2869. [CrossRef]
87. Dobrescu, E.M.; Dobrescu, E.M. Artificial intelligence (AI): The technology that shapes the world. Glob. Econ.
Obs. 2018, 6, 71–81.
88. Edwards, C.; Edwards, A.; Spence, P.R.; Lin, X. I, teacher: Using artificial intelligence (AI) and social robots
in communication and instruction. Commun. Educ. 2018, 67, 473–480. [CrossRef]
89. Guo, J.; Li, B. The application of medical artificial intelligence technology in rural areas of developing
countries. Health Equity 2018, 2, 174–181. [CrossRef]
90. Guo, K.; Lu, Y.; Gao, H.; Cao, R. Artificial intelligence-based semantic internet of things in a user-centric
smart city. Sensors 2018, 18, 1341. [CrossRef]
91. Håkansson, A. Ipsum: An approach to smart volatile ICT-infrastructures for smart cities and communities.
Procedia Comput. Sci. 2018, 126, 2107–2116. [CrossRef]
Energies 2020, 13, 1473 34 of 38

92. Kopytko, V.; Shevchuk, L.; Yankovska, L.; Semchuk, Z.; Strilchuk, R. Smart home and artificial intelligence
as environment for the implementation of new technologies. Traektoriâ Nauk. Path Sci. 2018, 4, 2007–2012.
[CrossRef]
93. Liu, G.; Liu, S.; Muhammad, K.; Sangaiah, A.K.; Doctor, F. Object tracking in vary lighting conditions for fog
based intelligent surveillance of public spaces. IEEE Access 2018, 6, 29283–29296. [CrossRef]
94. Lukowicz, P.; Slusallek, P. How to avoid an AI interaction singularity. Interactions 2018, 25, 72–78. [CrossRef]
95. Martins, J.S. Towards smart city innovation under the perspective of software-defined networking, artificial
intelligence and big data. Rev. Tecnol. Inf. Comun. 2018, 8, 1–8.
96. Nápoles, V.M.; Rodríguez, M.D.; Páez, D.G.; Penelas, J.L.; García-Ochoa, A.G.; Pérez, A.L. MUSA–I: Towards
new social tools for advanced multi-modal transportation in smart cities. Multidiscip. Digit. Publ. Inst. Proc.
2018, 2, 1215. [CrossRef]
97. Wan, C.H.; Hwang, M.C. Value-based deep reinforcement learning for adaptive isolated intersection signal
control. IET Intell. Transp. Syst. 2018, 12, 1005–1010. [CrossRef]
98. Abduljabbar, R.; Dia, H.; Liyanage, S.; Bagloee, S. Applications of artificial intelligence in transport: An
overview. Sustainability 2019, 11, 189. [CrossRef]
99. Ajerla, D.; Mahfuz, S.; Zulkernine, F. A real-time patient monitoring framework for fall detection. Wirel.
Commun. Mob. Comput. 2019, 2019. [CrossRef]
100. Allam, Z.; Dhunny, Z.A. On big data, artificial intelligence and smart cities. Cities 2019, 89, 80–91. [CrossRef]
101. Alsamhi, S.H.; Ma, O.; Ansari, M.S.; Almalki, F.A. Survey on collaborative smart drones and internet of
things for improving smartness of smart cities. IEEE Access 2019, 7, 128125–128152. [CrossRef]
102. Alzoubi, I.; Delavar, M.R.; Mirzaei, F.; Arrabi, B.N. Prediction of environmental indicators in land leveling
using artificial intelligence techniques. J. Environ. Health Sci. Eng. 2019, 16, 65–80. [CrossRef]
103. Brady, H.E. The challenge of big data and data science. Annu. Rev. Political Sci. 2019, 22, 297–323. [CrossRef]
104. Bui, K.H.; Jung, J.J. Computational negotiation-based edge analytics for smart objects. Inf. Sci. 2019, 480,
222–236. [CrossRef]
105. Cai, B.Y.; Alvarez, R.; Sit, M.; Duarte, F.; Ratti, C. Deep learning-based video system for accurate and real-time
parking measurement. IEEE Internet Things J. 2019, 6, 7693–7701. [CrossRef]
106. Chen, M.; Miao, Y.; Jian, X.; Wang, X.; Humar, I. Cognitive-LPWAN: Towards intelligent wireless services in
hybrid low power wide area networks. IEEE Trans. Green Commun. Netw. 2019, 3, 409–417. [CrossRef]
107. Chen, N.; Qiu, T.; Zhou, X.; Li, K.; Atiquzzaman, M. An intelligent robust networking mechanism for the
internet of things. IEEE Commun. Mag. 2019, 57, 91–95. [CrossRef]
108. Cui, Q.; Wang, Y.; Chen, K.C.; Ni, W.; Lin, I.C.; Tao, X.; Zhang, P. Big data analytics and network calculus
enabling intelligent management of autonomous vehicles in a smart city. IEEE Internet Things J. 2018, 6,
2021–2034. [CrossRef]
109. Desouza, K.C.; Dawson, G.S.; Chenok, D. Designing, developing, and deploying artificial intelligence
systems: Lessons from and for the public sector. Bus. Horiz. 2019, 63, 205–213. [CrossRef]
110. Din, I.U.; Guizani, M.; Rodrigues, J.J.; Hassan, S.; Korotaev, V.V. Machine learning in the internet of things:
Designed techniques for smart cities. Future Gener. Comput. Syst. 2019, 100, 826–843. [CrossRef]
111. Dong, Y.; Guo, S.; Liu, J.; Yang, Y. Energy-efficient fair cooperation fog computing in mobile edge networks
for smart city. IEEE Internet Things J. 2019, 6, 7543–7554. [CrossRef]
112. Hariri, R.H.; Fredericks, E.M.; Bowers, K.M. Uncertainty in big data analytics: Survey, opportunities, and
challenges. J. Big Data 2019, 6, 44. [CrossRef]
113. Ibrahim, M.R.; Haworth, J.; Cheng, T. Urban-i: From urban scenes to mapping slums, transport modes, and
pedestrians in cities using deep learning and computer vision. Environ. Plan. B Urban Anal. City Sci. 2019.
[CrossRef]
114. Iqbal, R.; Maniak, T.; Karyotis, C. Intelligent remote monitoring of parking spaces using licensed and
unlicensed wireless technologies. IEEE Netw. 2019, 33, 23–29. [CrossRef]
115. Kundu, D. Blockchain and trust in a smart city. Environ. Urban. Asia 2019, 10, 31–43. [CrossRef]
116. Le, L.T.; Nguyen, H.; Dou, J.; Zhou, J. A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and
ABC-ANN in estimating the heating load of buildings’ energy efficiency for smart city planning. Appl. Sci.
2019, 9, 2630. [CrossRef]
117. Leung, C.K.; Braun, P.; Cuzzocrea, A. AI-based sensor information fusion for supporting deep supervised
learning. Sensors 2019, 19, 1345. [CrossRef] [PubMed]
Energies 2020, 13, 1473 35 of 38

118. Li, L.; Shuang, Y.; Ma, Q.; Li, H.; Zhao, H.; Wei, M.; Liu, C.; Hao, C.; Qiu, C.W.; Cui, T.J. Intelligent metasurface
imager and recognizer. Light Sci. Appl. 2019, 8, 1–9. [CrossRef]
119. Liu, N.; Li, L.; Hao, B.; Yang, L.; Hu, T.; Xue, T.; Wang, S. Modeling and simulation of robot inverse dynamics
using LSTM-based deep learning algorithm for smart cities and factories. IEEE Access 2019, 7, 173989–173998.
[CrossRef]
120. Lytras, M.D.; Chui, K.T.; Visvizi, A. Data analytics in smart healthcare: The recent developments and beyond.
Appl. Sci. 2019, 9, 2812. [CrossRef]
121. Muhammad, K.; Lloret, J.; Baik, S.W. Intelligent and energy-efficient data prioritization in green smart cities:
Current challenges and future directions. IEEE Commun. Mag. 2019, 57, 60–65. [CrossRef]
122. Noorbakhsh-Sabet, N.; Zand, R.; Zhang, Y.; Abedi, V. Artificial intelligence transforms the future of healthcare.
Am. J. Med. 2019, 132, 795–801. [CrossRef]
123. Park, J.H.; Lee, S.; Yun, S.; Kim, H.; Kim, W.T. Dependable fire detection system with multifunctional artificial
intelligence framework. Sensors 2019, 19, 2025. [CrossRef]
124. Ponce, H.; Gutiérrez, S. An indoor predicting climate conditions approach using Internet-of-Things and
artificial hydrocarbon networks. Measurement 2019, 135, 170–179. [CrossRef]
125. Puri, V.; Jha, S.; Kumar, R.; Priyadarshini, I.; Abdel-Basset, M.; Elhoseny, M.; Long, H.V. A hybrid artificial
intelligence and internet of things model for generation of renewable resource of energy. IEEE Access 2019, 7,
111181–111191. [CrossRef]
126. Rahman, M.A.; Rashid, M.M.; Hossain, M.S.; Hassanain, E.; Alhamid, M.F.; Guizani, M. Blockchain and
IoT-based cognitive edge framework for sharing economy services in a smart city. IEEE Access 2019, 7,
18611–18621. [CrossRef]
127. Ruohomaa, H.; Salminen, V.; Kunttu, I. Towards a smart city concept in small cities. Technol. Innov. Manag.
Rev. 2019, 9, 5–14. [CrossRef]
128. Sgantzos, K.; Grigg, I. Artificial intelligence implementations on the blockchain: Use cases and future
applications. Future Internet 2019, 11, 170. [CrossRef]
129. Shi, J.; Liu, S.; Zhang, L.; Yang, B.; Shu, L.; Yang, Y.; Chai, Y. Smart textile-integrated microelectronic systems
for wearable applications. Adv. Mater. 2019, 32, 1901958. [CrossRef]
130. Soomro, K.; Bhutta, M.N.; Khan, Z.; Tahir, M.A. Smart city big data analytics: An advanced review. Wiley
Interdiscip. Rev. Data Min. Knowl. Discov. 2019, 9, e1319. [CrossRef]
131. Streitz, N. Beyond ‘smart-only’ cities: Redefining the ‘smart-everything’ paradigm. J. Ambient Intell. Humaniz.
Comput. 2019, 10, 791–812. [CrossRef]
132. Syifa, M.; Kadavi, P.R.; Lee, C.W. An artificial intelligence application for post-earthquake damage mapping
in Palu, Central Sulawesi, Indonesia. Sensors 2019, 19, 542. [CrossRef]
133. Wang, P.; Yao, J.; Wang, G.; Hao, F.; Shrestha, S.; Xue, B.; Peng, Y. Exploring the application of artificial
intelligence technology for identification of water pollution characteristics and tracing the source of water
quality pollutants. Sci. Total Environ. 2019, 693, 133440. [CrossRef]
134. Wei, N.; Li, C.; Peng, X.; Zeng, F.; Lu, X. Conventional models and artificial intelligence-based models for
energy consumption forecasting: A review. J. Pet. Sci. Eng. 2019, 181, 106187. [CrossRef]
135. Wogu, I.A.; Misra, S.; Assibong, P.A.; Apeh, H.A.; Olu-Owolabi, F.E.; Awogu-Maduagwu, E.A. Artificial
intelligence, smart class rooms and online education in the 21st century: Implications for human development.
J. Cases Inf. Technol. 2018, 21, 66–79. [CrossRef]
136. Cortès, U.; Sànchez-Marrè, M.; Ceccaroni, L.; R-Roda, I.; Poch, M. Artificial intelligence and environmental
decision support systems. Appl. Intell. 2000, 13, 77–91. [CrossRef]
137. Bennett, C.C.; Hauser, K. Artificial intelligence framework for simulating clinical decision-making: A Markov
decision process approach. Artif. Intell. Med. 2013, 57, 9–19. [CrossRef] [PubMed]
138. Fernández, J.; Calavia, L.; Baladrón, C.; Aguiar, J.; Carro, B.; Sánchez-Esguevillas, A.; Alonso-López, J.;
Smilansky, Z. An intelligent surveillance platform for large metropolitan areas with dense sensor deployment.
Sensors 2013, 13, 7414–7442. [CrossRef] [PubMed]
139. Stefanelli, M. The socio-organizational age of artificial intelligence in medicine. Artif. Intell. Med. 2001, 23,
25–47. [CrossRef]
140. McArthur, D.; Lewis, M.; Bishary, M. The roles of artificial intelligence in education: Current progress and
future prospects. J. Educ. Technol. 2005, 1, 42–80. [CrossRef]
Energies 2020, 13, 1473 36 of 38

141. Pieters, W. Explanation and trust: What to tell the user in security and AI? Ethics Inf. Technol. 2011, 13, 53–64.
[CrossRef]
142. Neuhauser, L.; Kreps, G.L.; Morrison, K.; Athanasoulis, M.; Kirienko, N.; Van Brunt, D. Using design science
and artificial intelligence to improve health communication: ChronologyMD case example. Patient Educ.
Couns. 2013, 92, 211–217. [CrossRef]
143. Roll, I.; Wylie, R. Evolution and revolution in artificial intelligence in education. Int. J. Artif. Intell. Education
2016, 26, 582–599. [CrossRef]
144. Castelli, M.; Sormani, R.; Trujillo, L.; Popovič, A. Predicting per capita violent crimes in urban areas: An
artificial intelligence approach. J. Ambient Intell. Humaniz. Comput. 2017, 8, 29–36. [CrossRef]
145. Meena, N.K.; Parashar, S.; Swarnkar, A.; Gupta, N.; Niazi, K.R.; Bansal, R.C. Mobile power infrastructure
planning and operational management for smart city applications. Energy Procedia 2017, 142, 2202–2207.
[CrossRef]
146. Quan, S.J.; Park, J.; Economou, A.; Lee, S. Artificial intelligence-aided design: Smart design for sustainable
city development. Environ. Plan. B Urban Anal. City Sci. 2019, 46, 1581–1599. [CrossRef]
147. Devedzic, V. Web Intelligence and artificial intelligence in education. Educ. Technol. Soc. 2004, 7, 29–39.
148. Eldrandaly, K.A.; Abdel-Basset, M.; Abdel-Fatah, L. PTZ-surveillance coverage based on artificial intelligence
for smart cities. Int. J. Inf. Manag. 2019, 49, 520–532. [CrossRef]
149. Altulyan, M.; Yao, L.; Kanhere, S.S.; Wang, X.; Huang, C. A unified framework for data integrity protection
in people-centric smart cities. Multimed. Tools Appl. 2019, 79, 4989–5002. [CrossRef]
150. Yu, H.; Yang, Z.; Sinnott, R.O. Decentralized big data auditing for smart city environments leveraging
blockchain technology. IEEE Access 2019, 7, 6288–6296. [CrossRef]
151. Bowman, S.; Easpaig, G.; Nic, B.; Fox, R. Virtually caring: A qualitative study of internet-based mental health
services for LGBT young adults in rural Australia. Rural Remote Health 2020, 20, 31–38. [CrossRef]
152. Pence, H.E. Artificial intelligence in higher education: New wine in old wineskins? J. Educ. Technol. Syst.
2019, 48, 5–13. [CrossRef]
153. Guilherme, A. AI and education: The importance of teacher and student relations. AI Soc. 2019, 34, 47–54.
[CrossRef]
154. The Conversation. COVID-19 Death Toll Estimated to Reach 3900 by Next Friday, According to AI Modelling.
Available online: https://theconversation.com/covid-19-death-toll-estimated-to-reach-3-900-by-next-friday-
according-to-ai-modelling-133052 (accessed on 11 March 2020).
155. Lin, Y.P.; Petway, J.R.; Lien, W.Y.; Settele, J. Blockchain with artificial intelligence to efficiently manage water
use under climate change. Environments 2018, 5, 34. [CrossRef]
156. Inclezan, D.; Prádanos, L.I. A critical view on smart cities and AI. J. Artif. Intell. Res. 2017, 60, 681–686.
[CrossRef]
157. Zou, Y.; Zhang, S.; Min, Y. Exploring urban population forecasting and spatial distribution modelling with
artificial intelligence technology. Comput. Model. Eng. Sci. 2019, 119, 295–310.
158. Rolnick, D.; Donti, P.L.; Kaack, L.H.; Kochanski, K.; Lacoste, A.; Sankaran, K.; Luccioni, A. Tackling climate
change with machine learning. arXiv preprint arXiv 2019, arXiv:1906.05433.
159. O’Gorman, P.A.; Dwyer, J.G. Using machine learning to parameterize moist convection: Potential for
modeling of climate, climate change, and extreme events. J. Adv. Model. Earth Syst. 2018, 10, 2548–2563.
[CrossRef]
160. Dayal, K.; Deo, R.; Apan, A.A. Drought modelling based on artificial intelligence and neural network
algorithms: A case study in Queensland, Australia. In Climate Change Adaptation in Pacific Countries; Springer:
Singapore, 2017; pp. 177–198.
161. Khalifa, E. Smart cities: Opportunities, challenges, and security threats. J. Strateg. Innov. Sustain. 2019, 14,
79–88.
162. Falco, G.; Viswanathan, A.; Caldera, C.; Shrobe, H. A master attack methodology for an AI-based automated
attack planner for smart cities. IEEE Access 2018, 6, 48360–48373. [CrossRef]
163. Kankanamge, N.; Yigitcanlar, T.; Goonetilleke, A.; Kamruzzaman, M. Can volunteer crowdsourcing reduce
disaster risk? A systematic review of the literature. Int. J. Disaster Risk Reduct. 2019, 35, 101097. [CrossRef]
164. Chatterjee, S.; Kar, A.K.; Gupta, M.P. Success of IoT in smart cities of India: An empirical analysis. Gov. Inf.
Q. 2018, 35, 349–361. [CrossRef]
Energies 2020, 13, 1473 37 of 38

165. Luo, X.; Tong, S.; Fang, Z.; Qu, Z. Frontiers: Machines vs. humans: The impact of artificial intelligence
chatbot disclosure on customer purchases. Mark. Sci. 2019, 38, 937–947. [CrossRef]
166. Yigitcanlar, T. Smart cities: An effective urban development and management model? Aust. Plan. 2015, 52,
27–34. [CrossRef]
167. Rotta, M.J.; Sell, D.; Pacheco, R.C.; Yigitcanlar, T. Digital commons and citizen coproduction in smart cities:
Assessment of Brazilian municipal e-government platforms. Energies 2019, 12, 2813. [CrossRef]
168. Yigitcanlar, T. Smart city policies revisited: Considerations for a truly smart and sustainable urbanism
practice. World Technopolis Rev. 2018, 7, 97–112.
169. Rjab, A.B.; Mellouli, S. Smart cities in the era of artificial intelligence and internet of things: Literature review
from 1990 to 2017. In Proceedings of the 19th Annual International Conference on Digital Government
Research: Governance in the Data Age, Delft, The Netherlands, 30 May–1 June 2018; pp. 1–10.
170. Friedman, B.; Hendry, D.G. Value Sensitive Design: Shaping Technology with Moral Imagination; MIT Press:
Cambridge, MA, USA, 2019.
171. LSE. Why Low Trust in Government May Mean Americans Don’t Want Anything Done about Inequality.
Available online: https://blogs.lse.ac.uk/usappblog/2019/12/23/why-low-trust-in-government-may-mean-
americans-dont-want-anything-done-about-inequality (accessed on 20 February 2020).
172. Morishita, L.; van Zyl, D. Exploring the significance of earning a social license to operate in an urban setting.
Geo-Resour. Environ. Eng. 2017, 2, 265–270. [CrossRef]
173. Yuttapongsontorn, N.; Desouza, K.C.; Braganza, A. Complexities of large-scale technology project failure: A
forensic analysis of the Seattle popular monorail authority. Public Perform. Manag. Rev. 2008, 31, 443–478.
[CrossRef]
174. Mergel, I.; Desouza, K.C. Implementing open innovation in the public sector: The case of Challenge. gov.
Public Adm. Rev. 2013, 73, 882–890. [CrossRef]
175. Desouza, K.C.; Bhagwatwar, A. Technology-enabled participatory platforms for civic engagement: The case
of US cities. J. Urban Technol. 2014, 21, 25–50. [CrossRef]
176. Liu, Y. Research on the evaluation of urban open data. World J. Eng. Technol. 2017, 5, 122–134. [CrossRef]
177. Perng, S.Y.; Kitchin, R.; Mac Donncha, D. Hackathons, entrepreneurial life and the making of smart cities.
Geoforum 2018, 97, 189–197. [CrossRef]
178. CNN. Artist Uses 99 Phones to Trick Google into Traffic Jam Alert. Available online: https://us.cnn.com/
style/article/artist-google-traffic-jam-alert-trick-scli-intl/index.html (accessed on 20 February 2020).
179. Desouza, K.C. Agile Information Systems: Conceptualization, Construction, and Management; Routledge:
Abingdon upon Thames, UK, 2006.
180. Watson, R.T.; Kunene, K.N.; Islam, M.S. Frugal information systems (IS). Inf. Technol. Dev. 2013, 19, 176–187.
[CrossRef]
181. Planetizen. Our Fragile Emerging Megacities: A Focus on Resilience. Available online: https://www.
planetizen.com/node/67338 (accessed on 19 February 2020).
182. Purao, S.R.; Desouza, K.C.; Becker, J. Investigating failures in large-scale public sector projects with sentiment
analysis. e-Serv. J. 2012, 8, 84–105. [CrossRef]
183. Desouza, K.C.; Hunter, M.; Yigitcanlar, T. Under the hood: A look at techno-centric smart city development.
Public Manag. 2019, 12, 30–35.
184. Krishnamurthy, R.; Smith, K.L.; Desouza, K.C. Urban informatics: Critical data and technology considerations.
In Seeing Cities Through Big Data; Springer: Singapore, 2017; pp. 163–188.
185. Wired. A Deep Flaw in Your Car Lets Hackers Shut Down Safety Features. Available online: https:
//www.wired.com/story/car-hack-shut-down-safety-features (accessed on 14 February 2020).
186. BBC. China Coronavirus: Misinformation Spreads Online About Origin and Scale. Available online:
https://www.bbc.com/news/blogs-trending-51271037 (accessed on 20 February 2020).
187. BBC. Burned to Death Because of a Rumour on WhatsApp. Available online: https://www.bbc.com/news/
world-latin-america-46145986 (accessed on 30 December 2019).
188. Gherhes, V.; Obrad, C. Technical and humanities students’ perspectives on the development and sustainability
of artificial intelligence (AI). Sustainability 2018, 10, 3066. [CrossRef]
189. The Conversation. Australian Police are Using the Clearview AI Facial Recognition System with No
Accountability. Available online: https://theconversation.com/australian-police-are-using-the-clearview-ai-
facial-recognition-system-with-no-accountability-132667 (accessed on 11 March 2020).
Energies 2020, 13, 1473 38 of 38

190. The Conversation. Airlines Take no Chances with Our Safety. And Neither Should Artificial Intelligence.
Available online: https://theconversation.com/airlines-take-no-chances-with-our-safety-and-neither-should-
artificial-intelligence-132580 (accessed on 11 March 2020).
191. The Verge. IBM’s Watson Gave Unsafe Recommendations for Treating Cancer. Available online: https://www.
theverge.com/2018/7/26/17619382/ibms-watson-cancer-ai-healthcare-science (accessed on 12 March 2020).
192. NTSB. Preliminary Report Released for Crash Involving Pedestrian, Uber Technologies, Inc., Test
Vehicle. Available online: https://www.ntsb.gov/news/press-releases/Pages/NR20180524.aspx (accessed on
13 March 2020).
193. The Conversation. Microsoft’s Racist Chatbot Tay Highlights How Far AI is from being Truly Intelligent.
Available online: https://theconversation.com/microsofts-racist-chatbot-tay-highlights-how-far-ai-is-from-
being-truly-intelligent-56881 (accessed on 13 March 2020).
194. Forbes. Is Artificial Intelligence Dangerous? 6 AI Risks Everyone Should Know About. Available
online: https://www.forbes.com/sites/bernardmarr/2018/11/19/is-artificial-intelligence-dangerous-6-ai-risks-
everyone-should-know-about/#65ee8ba22404 (accessed on 13 March 2020).
195. The Conversation. To Protect us from the Risks of Advanced Artificial Intelligence, We Need to Act
Now. Available online: https://theconversation.com/to-protect-us-from-the-risks-of-advanced-artificial-
intelligence-we-need-to-act-now-107615 (accessed on 13 March 2020).
196. Medium. Summary of EU White Paper on Artificial Intelligence: A European Approach to Excellence
and Trust. Available online: https://medium.com/@tibastar/summary-of-eu-white-paper-on-artificial-
intelligence-a-european-approach-to-excellence-and-trust-e04a1a018b5 (accessed on 11 March 2020).
197. EU. On Artificial Intelligence: A European Approach to Excellence and Trust. Available online: https:
//ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf (accessed
on 11 March 2020).
198. The Guardian. Elon Musk: Regulate AI to Combat ’Existential Threat’ before It’s too Late. Available
online: https://www.theguardian.com/technology/2017/jul/17/elon-musk-regulation-ai-combat-existential-
threat-tesla-spacex-ceo (accessed on 13 March 2020).
199. Schleiger, E.; Hajkowicz, S. Artificial Intelligence in Australia Needs to Get Ethical, So We Have a Plan.
Available online: https://www.themandarin.com.au/107060-artificial-intelligence-in-australia-needs-to-get-
ethical-so-we-have-a-plan (accessed on 15 March 2020).
200. Nica, E.; Manole, C.; Stan, C.I. A laborless society? How highly automated environments and breakthroughs
in artificial intelligence bring about innovative kinds of skills and employment disruptions, altering the
nature of business process and affecting the path of economic growth. J. Self-Gov. Manag. Econ. 2018, 6,
25–30.
201. Gunkel, D.J. The Machine Question: Critical Perspectives on AI, Robots, and Ethics; MIT Press: Cambridge, MA,
USA, 2012.
202. Makridakis, S. The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures
2017, 90, 46–60. [CrossRef]
203. Sheikhnejad, Y.; Yigitcanlar, T. Scientific landscape of sustainable urban and rural areas research: A systematic
scientometric analysis. Sustainability 2020, 12, 1293. [CrossRef]
204. Smashing Magazine. How AI is Helping Solve Climate Change. Available online: https://www.
smashingmagazine.com/2019/09/ai-climate-change (accessed on 11 March 2020).
205. Yigitcanlar, T.; Vella, K.; Desouza, K.; Butler, L.; Kankanamge, N. Smart City or Not? Now You Can See How
Yours Compares. Available online: https://eprints.qut.edu.au/180751/ (accessed on 15 March 2020).

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