Artificial Intelligence in Smart City Applications: An overview

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Artificial Intelligence in Smart City Applications:
An overview
Ashwini B P
Department of Computer Science and
Engineering
Siddaganga Institute of Technology
Tumakuru, India
ashvinibp@sit.ac.in
Savithramma R M
Department of Computer Science and
Engineering
Siddaganga Institute of Technology
Tumakuru, India
savirmrl@sit.ac.in
R Sumathi
Department of Computer Science and
Engineering
Siddaganga Institute of Technology
Tumakuru, India
rsumathi@sit.ac.in
Abstract— Recently, the smart city has evolved as a global
model and several institutions have adopted this concept to
facilitate the citizens with the comfort and quality of life
exploiting the progress in the capabilities of computing,
networking, and data management. All probable aspects of a
smart city are modeled as different components such as
governance, transportation, waste and energy management, and
so on. Artificial Intelligence (AI) based technology is widely
applied in the development of most of the components of a smart
city. In this context, the current article presents a detailed
survey of the latest AI-based solutions in smart city
implementation. The following inferences are made through this
review: (a) AI-based applications for a smart city have been
adopted by various developing and developed countries
worldwide. (b) The components such as town planning,
governance, and education are less explored as compared to
other components. (c) Network-based models including deep
learning models are the most popular as compared to other
models like trees, genetic, linear, and naive models. Finally, it is
observed from the review that AI is an indispensable part of a
smart city currently and will continue to be in the future.
Keywords— Artificial Intelligence, Information and
Communication Technology, Smart transportation, Smart city
components, Smart governance, Smart grid, Smart education.
I. INTRODUCTION
An extreme increase in population has resulted in several
issues, particularly in urban areas [1]. The major issues
experienced in urban areas are congestion due to heavy traffic,
environmental pollution, health issues, insufficient water
supply, waste accumulation, etc. The government has initiated
various projects to address these issues and the smart city
mission is one of the projects. The objective of the smart city
project is to facilitate a quality life along with eco-friendly and
reliable services to the city residents by exploiting the advent
of state-of-art technology. The researchers and experts are
working towards the objective and have come up with several
frameworks concerned with smart cities development. The
proposed smart city framework involves various aspects of
urban management resulting in different components. Further,
researchers across the globe have proposed a variety of
solutions to address each component using Internet and
Communication Technology (ICT).
The technologies used in various levels of developing smart
city components can be broadly classified into (a) Learning
techniques (b) Computing technology (c) Communication
technology (d) Data management technology (e) Sensor
technology as represented in figure 1.
Fig. 1. Schematic of various levels of smart city components
implementation, and technologies used
Artificial Intelligence (AI) is the widely applied
technology to implement smart city components. The
environment is the main concern when designing a solution
for any of the smart city components. The advancement in AI
and 5G has laid a broad prospectus for various ubiquitous
applications of a smart city including automated vehicles,
healthcare, smart homes, etc., and regular monitoring of the
environment is essential to sustain the quality of living in
society. The study presented in [2][3] has discovered the
potential use of AI in monitoring the environment's health
parameters such as radiation, air quality, water contamination,
emissions, etc. The data plays an important role in AI-based
solutions and the necessary data can be collected from various
sensors as summarized in [4]. The objective of this study is to
explore on use of AI in the design and development of various
components of the smart city framework.
The remaining parts of the article are discussed in the
following order; Next section describes the smart city
components considered in this study. The observation of the
current study concerning the AI and Smart city components
are highlighted and summarized under section III. Finally, the
article is summarized with major inferences of the survey.
II. COMPONENTS OF SMART CITY
The Smart City aims at improving the economic status and
lifestyle of city residents by exploiting the advancement in
technology in recent decades. The government, researchers,
and experts across the globe are striving in this direction and
as a result, various smart city frameworks are available
currently. The framework includes different elements
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covering all probable areas of city functioning. The vital
components (figure 2) from different frameworks are
identified and the role of artificial intelligence in these
components is focused on in this article.
Fig. 2. Components of smart city framework
A. Governance and digitization
Decision-making is a crucial aspect of governance
towards the development of a city or a country. The
capabilities of AI are exploited in the design and development
of smart decision-making systems for smart cities. In an
article [5] the authors have developed a chatbot application to
communicate the necessary information between the
government and the common people using Artificial Neural
Network (ANN). Whereas the authors in [6] have outlined a
decision-making process using the regression technique
which guides the government in building smart cities with the
involvement of social innovations. A comprehensive review
of existing research works on the use of AI in implementing
smart governance is presented in [7][8]. The importance of
digitization and the role of AI in the process of transformation
towards digitization is presented in [9] and the key
contributions of AI in building a smart society are discussed
in the research article [10].
B. Smart manufacturing
Smart production and manufacturing are the building
blocks of a city's development. AI is widely applied in the
field of manufacturing at various levels to enhance product
quality and quantity. The article [11] presents a surface
roughness prediction method for 3D printing and [12]
developed a prediction model for lead times. The authors in
[13] have given a material feeding and scrap rate prediction
model whereas a novel model for predicting tensile strength
and plasticity of steel is proposed in [14].
C. Smart security
Smart city applications must ensure the privacy and
security of city residents in all respects. The applications must
be able to take necessary actions to protect users from
intruders and hackers. Artificial intelligence plays an
important role in enhancing the security of smart city
applications. The authors in [15] have presented a technique
to identify the burglars entering the house so that the
counteractions can be performed. The data including
movements, body temperature, and facial features of a person
who entered the house are collected through various sensors.
This data is used to recognize the illegal person's entry inside
the house by using an ANN. The article [16] is focused on the
security aspect of e-governance by ensuring the privacy-
preserve in the smart city environment and the authors have
investigated currently available solutions and the challenges.
Authors have proposed a decentralized secure e-governance
framework using a combination of blockchain technology
and artificial intelligence to enhance privacy through mutual
trust and confidence.
A new paradigm of technology i.e. explainable artificial
intelligence for cyber applications concerning smart cities is
presented in [17]. The authors have discussed the
understandability, transparency, and interoperability
transitions of artificial intelligence while applying it to
implement different autonomous systems of a smart city. The
article [18] presented an intelligent system to monitor and
respond autonomously to the detection of illegal entry into
the house. A deep neural network (Convolution Neural
Network) is used to detect motion and facial recognition. The
security issues faced by the smart city applications and the
countermeasures to overcome the problems are addressed by
the authors in [19]. The privacy and security issues are
addressed at the data collection level using machine learning
techniques. The proposed solution was demonstrated through
smart city applications such as smart health, smart energy,
and smart transportation
D. Smart education
With the belief, that the individual student has a unique
learning style the authors in [20] have presented a machine
learning-based e-learning material suggesting framework.
Suitable study material is suggested for different students
based on their interests and learning styles. The AI-based
classroom applications help the students to enhance their
learning capabilities. There exist various applications
concerning smart classrooms and a review of AI-based
solutions used in classrooms for the teaching-learning
process is discussed in [21]. The authors in [22] have
presented the potential abilities of artificial intelligence in
education from the teacher and student points of view. The
deep insights are presented through the conduction of
interviews with AI experts and technical firm associates. AI-
based learning in education is undergoing various paradigms,
however the authors in [23] have characterized it into three
paradigms namely AI-empowered: learner-as-leader, AI-
directed: learner-as-recipient, and AI-supported: learner-as-
collaborator. The role of AI in fixing the issues concerning
instructional methods in education is systematically
summarized in the article. The vital role of AI in education is
explored by the authors in [24].
E. Smart water
Water is one of the most valuable natural resources
necessary for every living being. Due to industrialization and
population growth water is contaminated and causes life
threats. The presence of trihalomethanes (THM) in
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chlorinated water is poisonous but detecting this element in
water is a complicated and time-consuming process. Hence,
the authors in [25] have proposed a THM prediction model
using ANN, SVM, and Gene Expression Programming
(GEP) approaches. Even though the earth is covered with
71% of water, the freshwater percentage is very less.
Conserving the available freshwater is the responsibility of
everyone. Statistics across the globe revealed that most of the
water is wasted because of leakage in the water network.
Therefore, the implementation of an efficient and smart water
network can minimize the water leakage thereby reducing the
water wastage. In this direction, the authors in [26] have
presented a cloud-based model to assess and monitor the
water network across the city using ANN.
The article [27] presents a novel data-driven idea to
forecast the water demand around the city using Machine
Learning (ML) by considering various seasonalities like
daily, weekly, monthly and yearly. Whereas, the authors in
[28] have used Internet of Things to forecast the water
demand thereby ensuring the distributed water supply. The
inflow of reservoirs is predicted through recurrent neural
network in [29]. The application of AI to implement smart
water management schemes is discussed in [30]. A
comprehensive review of existing water management
systems and the technologies used at various levels of their
implementation is presented in [31].
F. Smart town planning
The vital role of AI in the development of autonomous
cities through smart applications is explored and presented in
[32] along with available frameworks. Also, the authors have
proposed a research scope for autonomous city
implementation. A comprehensive study on AI techniques
and their pivotal applications in town planning and
development is discussed in an article [33]. The most popular
and sustainable applications are tested through a case study
from Australia on a testbed. Whereas the research article [34]
is a study that aims at motivating all the stakeholders
concerned with smart city development by providing ideas
and guidance about how AI can be exploited in smart cities.
The vital role of AI in building urban areas and its effect on
socio-economic development is explored in [35]. The authors
observed that the expectation of using AI in city applications
is high particularly in initiatives for smart cities. The article
[36] presents a comprehensive review of how town planning
with AI solves issues of modern society that evolve with the
population growth.
G. Smart energy
The energy is overused with the growth of the population
which leads to environmental and other serious issues. Hence,
the management of energy in an optimized way is essential
for modern society. The authors of the article [37] have
presented a deep study on the application of computational
intelligence to predict the energy load along with the
challenges of energy management and future scope of
research. A reinforcement learning approach is used to
manage the energy consumed in a community through the
pooling method in [38]. The domestic users in a community
are associated with a local energy pool and the users are
permitted to trade freely with a renewable energy pool for a
lower cost. The authors in [39] have proposed a smart
infrastructure for conserving the renewable resources for
smart cities for the future generation. AI technology is the
core part of the infrastructure, and the solution is
demonstrated through a case study from Europe.
The article [40] presents an autonomous system to
manage the energy of an existing smart building using AI.
The authors in [41] have proposed a smart energy
management framework for a smart home using Internet of
Things (IoT) and AI techniques. There are two control
strategies in the proposed framework: scheduling from power
dispatch (supply-side) and control of appliances (demand
side). Both strategies are combined through AI algorithms. A
novel multi-objective approach with the combination of three
distinct AI algorithms is proposed in [42] for efficient
management of energy in smart homes. Whereas the article
[43] presents an energy management system for IoT devices
by considering the aspects of both software and hardware.
And a deep study on AI-based energy management systems
used in smart homes is presented in [44].
H. Smart waste management
An intelligent waste collection system based on location
data has been proposed in [45] using optimization and AI
intelligence algorithms. [46] presents an IoT-based garbage
alert system where the concerned authorities are informed
about the garbage in a specific locality. The authors in [47]
and [48] have proposed an intelligent system for collecting
the garbage by estimating the optimum route of waste
collection using ant colony optimization and evolutionary
algorithms respectively. Recycling of garbage using AI and
ML is tested in [49] and a holistic solution to recycle the
waste is presented in [50] whereas the optimal way of waste
collection using AI is discussed in [51][52]. The role of
technology in waste management is reviewed in [53] and the
authors suggest AI as a prominent technology for the
application.
I. Smart grid
Effective utilization of electricity is essential in the
current situation as the world is facing plenty of problems
with respect to the environment because of population
growth. The smart energy management system can solve the
problem of the overutilization of electricity. The authors in
[54] have proposed an efficient model to forecast the
electricity load ahead of the day which can reduce the
wastage of electricity. The proposed model uses deep
learning (ANN) technology. Along with the overutilization,
electricity theft is one of the biggest problems faced by the
electricity corporations and it can harm the power grids as
well thereby reducing the profit and quality of power supply.
This issue is addressed in [55] and the authors have proposed
a novel hybrid method to detect electricity theft in grids using
Random Forest (RF) and deep learning techniques.
The analysis of short and long-term load prediction for
power grids using ML techniques is been carried out and
presented in [56]. A comprehensive review of recent research
solutions available to tackle the energy resources and power
grids using various technologies is conducted and presented
in the article [57]. The study highlighted state-of-art
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technologies including machine learning. The proposed
solutions, challenges, and trends in smart power grid
implementation are elaborated in the study. Whereas the
article [58] presents a deep survey on the application of AI to
assess the grid stability and the article [59] surveyed on the
potential use of AI to implement Smart Grid.
J. Smart agriculture
Agriculture is the backbone for most developing
countries, with the growing population the demand for
agricultural products is exponentially growing. Replacing the
conventional methods with smart automated solutions [60] is
the need of the hour. Meteorological predictions [61],
decision support for precision farming, and digital solutions
through automation are the main areas presently addressed
through AI. Recent advances of AI in image processing have
led to solutions to identify the diseases in crops [62] at
various levels, predict the maturity of the crops [63], and also
in driving out pests, rodents, and insects through automated
repellent systems [64]. The authors in [65] and [66] have
used ML algorithms to predict the amount of water required
for cultivation and the probable amount of rainfall
respectively.
K. Transportation
Transportation is an indispensable component of society
and plays a pivotal role in the development of a country. With
the population growth and limited spatial infrastructure,
transportation suffers plenty of problems, and congestion is
the major issue. Advancement in technology is supporting
the concerned authorities to handle the issues by developing
smart applications. The Intelligent Transport Management
System (TMS) [67] has used ICT and AI techniques to
facilitate a safe, sustainable, and reliable [68] mode of travel
in the city. The TMS constitutes various components such
sensor deployment for data collection [69], Passenger
Information System (PIS) [70], traffic signal control system
[71], public transport integration [72], , etc.
The authors in [73] [74] have proposed an intelligent
transportation system using AI for transportation operations
like safe travel, traffic control, flow prediction, and crime risk,
etc. in smart cities. Various ML models are compared for
travel time prediction which has a key role in PIS. The authors
in [75] have presented a comparative analysis of different ML
algorithms to estimate adaptive traffic signal configuration to
manage traffic at a signalized intersection. The review articles
[76] and [77] quoted that the combination of AI algorithms is
best suitable for ITS implementation whereas the authors in
[78] have presented a detailed survey on smart mobility. The
new perspectives of ITS and ICT in smart cities are presented
in [79] and the various dimensions of smart mobility and AI
are discussed in [80] along with the future scope.
III. DISCUSSION
Cities today are facing issues such as degradation of
environment, scarcity of resources, smooth governance, etc.
The advancements in ICT, sensor networks, and computing
have prepared the society to ingest intelligent solutions to
solve the current issues of a modern city. The penetration of
digitization, social media, and availability of several data
sources through IoT and ICT have paved the way to innovate
new AI-based solutions. From naive to deep models, several
AI-based models are being explored in the implementation of
intelligent components of a smart city. Existing works that
have exploited artificial intelligence in implementing smart
city components are discussed in the previous section. A total
of 81 latest articles were shortlisted out of 200 articles
including reviews, empirical analysis, and theoretical
frameworks from reputed publishers and conference
proceedings. Out of the selected articles, 40 were surveyed to
explore the possible smart city issues addressed and AI
models used for the implementation of the solutions. The
summary of the survey is presented in table 1.
From the review, it is observed that research on possible
AI-based applications has penetrated every component of the
smart city and indeed deep into society signifying AI is
indispensable for a sustainable and citizen-friendly society.
Few components such as energy management, water
management, manufacturing, agriculture, transportation,
infrastructure, and security have been extensively explored as
compared to the town planning, governance, and education
components. The review also highlights the location of the
proposed solution, which indicates AI-based applications for
a smart city has been adopted by various developing and
developed countries worldwide.
TABLE I. SUMMARY OF THE REVIEW
Ref. Model Application Location/Dataset
Smart governance, digitization, and education
[5]
• Artificial Neural Networks
• K-means
• Decision Trees
• Naïve Bayes
•
Support Vector Machines
Chatbots Greece
[6] • Least Square regression Smart decision making Pakistan & South Korea
[20] • Decision tree
• Multi-
layer perceptron
Determining learning styles India
Smart manufacturing
[11] • Classification & Regressions Trees
• Random vector functional link
•
Ridge Regression
Prediction of surface roughness in 3D
printing Florida USA
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Ref. Model Application Location/Dataset
• Support Vector Regression
• Random Forest Regression
[12] • k-Nearest Neighbour
• Support Vector Machine
• Artificial Neural Network prediction of manufacturing lead times Austria
Hungary
[13] • Multiple Structural Change Model
• Artificial Neural Network Material feeding and scrap rate
prediction for PCB template production Shenzhen, China
[14] • Linear Regression
• Support Vector Machine
•
Extreme Gradient Boosting
Steel tensile strength and plasticity
prediction Jiangsu Province, China
Smart security
[15] • Artificial Neural Network Smart house security Ukraine
[16] • Block chain and AI Intrusion detection and prevention for
e-
governance
KDD99, NETRESEC
[18] • Convolution Neural Network Smart home security system Camera sensor data
Smart water
[25] • Artificial Neural Network
• Support Vector Machine
•
Gene Expression Programming
Forecasting trihalomethanes in water Online data
[26] • Artificial Neural Network Leakage detection and smart water
management
Berkshire,
England
[27] • Short-term pattern similarity
• k-Nearest Neighbour Water demand forecasting Spain
[28] • Linear regression Water demand forecasting Austin, Texas, USA
[29] • Long Short-Term Memory Reservoir Inflow Forecasting Turkey
Smart town planning and grid
[33] • Text mining Social media-based sentiment analyses
for Urb
an planning
Australia
[54] • Stacked Denoising Auto-Encoders
• support vector regression Electricity load forecasting Brazil
[55] • Random forests
• Convolution Neural Network Electricity Theft detection Ireland
[56] • Support Vector Regression Load forecasting for smart grid India
Smart agriculture
[62] • k-means
• Deep Neural Network Plant disease detection Plantvillage Dataset
Master
[63] • Radial Basis Network
• Multi-layer perceptron Estimating peanut maturity Georgia, USA
[65]
• Bayes algorithm Predict water needed for cultivation Tamilnadu, India
[66] • Random forest classification
•
Partial Least Square Regression
Rainfall prediction
Automatic Irrigation System India
Smart Energy
[38] • Fuzzy Q-learning Local energy trading application British electricity board
[40] • Fuzzy logic Smart home automation system Generic setup
[41] • Artificial Neural Network
• Fuzzy logic Smart home energy management Testbed simulation
[42]
• Elitist non-dominated sorting genetic
algorithm
• Support Vector Regression
• K-
means clustering
Smart home energy management
Meteorological station
Federal University of
Espírito Santo
São Mateus Campus
[43] • Genetic algorithm Energy optimization IoT devices OpenModelica
MATLAB Simulink
Smart waste management
[45] • Genetic algorithm
• K-
means clustering
Waste collection routing Copenhagen
[47] • Ant colony algorithm Dynamic routing of waste collection Kayseri, Turkey
[48] • Evolutionary genetic algorithm Shortest route estimation Istanbul
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Ref. Model Application Location/Dataset
[50] • Image processing using machine learning Waste recycling Bangsaen beach
Saensuk city, Thailand
[51] • Convolution Neural network Waste collection schedule Generic model
[52] • Convolution Neural network Waste volume estimation India
Smart transportation
[73] • Short-term regression
• Recurrent neural network Automobile traffic flow prediction Simulation
[74] • Artificial Neural Network Crime risk transportation areas
forecasting
Chicago
[81] • Random forest regression Travel time prediction Tumakuru, India
[75] • Gradient boosting regression trees Traffic signal green length prediction Tumakuru, India
Among the AI models used for implementing the
components of a smart city, network-based models including
deep learning models are the most experimented with as
compared to other AI-based models like trees, genetic, linear,
and naive models. With the abundant computing, storage, and
networking resources network models are the popular models
being implemented. A comparison of the AI-based models
for smart city components implementation is presented in
figure 2.
Fig. 2. Leading models of AI in Smart City Application
IV. CONCLUSION
A technology perspective survey on smart city solutions is
presented in this article. The smart city is emerging as a global
standard in recent decades and the basic intention of a smart
city is to provide a good quality of living to its residents
through smart applications. The researchers and experts have
proposed various smart city frameworks consisting of
different components addressing different sectors of a city. In
this article, eleven components including smart governance,
smart manufacturing, smart security, smart education, smart
water, smart town planning, smart grid, smart agriculture,
smart energy, smart waste management, and smart
transportation are selected for the review. The contribution of
technology toward the implementation of smart city
components is tremendous. However, Artificial Intelligence
(AI) has proved its potential in smart city application
development in most sectors. The current study is conducted
to illustrate the potential use of AI in the implementation of
smart city applications. 81 recent research articles from
reputed journals and conferences are shortlisted for the
review. The study revealed that AI-based applications are
being adopted globally. The components such as
manufacturing, security, water management, agriculture,
energy, waste management, and transportation are well
explored among the existing works as compared to town
planning, governance, and education. Neural network-based
models including deep network models are the most popular
models among AI-based models experimented with for smart
city applications. Overall, it is concluded that AI is an
indispensable part of smart city implementation in the present
and future.
REFERENCES
[1] A. A. Khan, S. Arshad, and M. Mohsin, “Population Growth and Its
Impact on Urban Expansion: A Case Study of Bahawalpur, Pakistan,”
Univers. J. Geosci., vol. 2, no. 8, pp. 229–241, 2014, doi:
10.13189/ujg.2014.020801.
[2] K. D., “A study on artificial intelligence for monitoring smart
environments,” Mater. Today Proc., no. xxxx, 2021, doi:
10.1016/j.matpr.2021.06.046.
[3] L. Liu and Y. Zhang, “Smart environment design planning for smart
city based on deep learning,” Sustain. Energy Technol. Assessments,
vol. 47, no. June, p. 101425, 2021, doi: 10.1016/j.seta.2021.101425.
[4] P. B. Ashwini and R. Sumathi, “Data sources for urban traffic
prediction: A review on classification, comparison and technologies,”
Proc. 3rd Int. Conf. Intell. Sustain. Syst. ICISS 2020, pp. 628–635,
2020, doi: 10.1109/ICISS49785.2020.9316096.
[5] A. Androutsopoulou, N. Karacapilidis, E. Loukis, and Y.
Charalabidis, “Transforming the communication between citizens and
government through AI-guided chatbots,” Gov. Inf. Q., vol. 36, no. 2,
pp. 358–367, 2019, doi: 10.1016/j.giq.2018.10.001.
[6] S. A. A. Bokhari and S. Myeong, “Use of Artificial Intelligence in
Smart Cities for Smart Decision-Making: A Social Innovation
Perspective,” Sustain., vol. 14, no. 2, 2022, doi: 10.3390/su14020620.
[7] J. Reis, P. E. Santo, and N. Melão, “Artificial Intelligence in
Government Services: A Systematic Literature Review,” Adv. Intell.
Syst. Comput., vol. 930, pp. 241–252, 2019, doi: 10.1007/978-3-030-
16181-1_23.
[8] W. G. de Sousa, E. R. P. de Melo, P. H. D. S. Bermejo, R. A. S. Farias,
and A. O. Gomes, “How and where is artificial intelligence in the
public sector going? A literature review and research agenda,” Gov.
Inf. Q., vol. 36, no. 4, p. 101392, 2019, doi:
10.1016/j.giq.2019.07.004.
[9] A. V. Lavrentyeva, A. A. Dzikia, A. E. Kalinina, D. P. Frolov, E. A.
0246810 12 14 16 18 20 22
Network
Trees
Optimization
k-means
Linear
SVM
Fuzzy lozic
Naïve
Others
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Akhverdiev, and A. S. Barakova, “Artificial intelligence and digital
transformations in the society,” IOP Conf. Ser. Mater. Sci. Eng., vol.
483, no. 1, 2019, doi: 10.1088/1757-899X/483/1/012019.
[10] R. Foresti, S. Rossi, M. Magnani, C. Guarino Lo Bianco, and N.
Delmonte, “Smart Society and Artificial Intelligence: Big Data
Scheduling and the Global Standard Method Applied to Smart
Maintenance,” Engineering, vol. 6, no. 7, pp. 835–846, 2020, doi:
10.1016/j.eng.2019.11.014.
[11] Z. Li, Z. Zhang, J. Shi, and D. Wu, “Prediction of surface roughness
in extrusion-based additive manufacturing with machine learning,”
Robot. Comput. Integr. Manuf., vol. 57, no. January, pp. 488–495,
2019, doi: 10.1016/j.rcim.2019.01.004.
[12] L. Lingitz, V. Gallina, F. Ansari, D. Gyulai, A. Pfeiffer, and W. Sihn,
“Lead time prediction using machine learning algorithms: A case
study by a semiconductor manufacturer,” Procedia CIRP, vol. 72, no.
June, pp. 1051–1056, 2018, doi: 10.1016/j.procir.2018.03.148.
[13] S. Lü, Q. Yue, and T. Liu, “Optimization of Material Release for
Printed Circuit Board Template Based on Data Mining,” Xitong
Fangzhen Xuebao / J. Syst. Simul., vol. 30, no. 7, pp. 2656–2665,
2018, doi: 10.16182/j.issn1004731x.joss.201807028.
[14] K. Song, F. Yan, T. Ding, L. Gao, and S. Lu, “A steel property
optimization model based on the XGBoost algorithm and improved
PSO,” Comput. Mater. Sci., vol. 174, no. December 2019, p. 109472,
2020, doi: 10.1016/j.commatsci.2019.109472.
[15] K. Artem and T. Vasyl, “Structure and model of the smart house
security system using machine learning methods,” 2nd Int. Conf. Adv.
Inf. Commun. Technol. AICT 2017 - Proc., pp. 105–108, 2017, doi:
10.1109/AIACT.2017.8020076.
[16] L. Yang, N. Elisa, and N. Eliot, “Privacy and security aspects of E-
government in smart cities,” Smart Cities Cybersecurity Priv., no.
June, pp. 89–102, 2018, doi: 10.1016/B978-0-12-815032-0.00007-X.
[17] M. H. Kabir, K. F. Hasan, and M. Kamrul, “Explainable Artificial
Intelligence for Smart City Application : A Secure and Trusted
Platform Smart Cities : the Better Livings for Dwellers,” Springer
Nat., pp. 1–17, 2021.
[18] O. Taiwo, A. E. Ezugwu, O. N. Oyelade, and M. S. Almutairi,
“Enhanced Intelligent Smart Home Control and Security System
Based on Deep Learning Model,” Wirel. Commun. Mob. Comput.,
vol. 2022, 2022, doi: 10.1155/2022/9307961.
[19] C. Ahmed, S., Hossain, M.F., Kaiser, M.S., Noor, M.B.T., Mahmud,
M., Chakraborty, “Artificial Intelligence and Machine Learning for
Ensuring Security in Smart Cities.,” in Advanced Sciences and
Technologies for Security Applications. Springer, Springer, 2021.
[20] R. Bajaj and V. Sharma, “Smart Education with artificial intelligence
based determination of learning styles,” Procedia Comput. Sci., vol.
132, pp. 834–842, 2018, doi: 10.1016/j.procs.2018.05.095.
[21] G. AlFarsi, R. M. Tawafak, A. ElDow, S. I. Malik, J. Jabbar, and A.
Al Sideiri, “Smart Classroom Technology in Artificial Intelligence: A
Review Paper,” in International Conference on Culture Heritage,
Education, Sustainable Tourism, and Innovation Technologies
(CESIT2020), 2021, no. Cesit 2020, pp. 229–235, doi:
10.5220/0010306502290235.
[22] A. Jaiswal and C. J. Arun, “Potential of Artificial Intelligence for
transformation of the education system in India,” Int. J. Educ. Dev.
using Inf. Commun. Technol., vol. 17, no. 1, pp. 142–158, 2021.
[23] F. Ouyang and P. Jiao, “Artificial intelligence in education: The three
paradigms,” Comput. Educ. Artif. Intell., vol. 2, no. March, 2021, doi:
10.1016/j.caeai.2021.100020.
[24] S. F. Ahmad, M. K. Rahmat, M. S. Mubarik, M. M. Alam, and S. I.
Hyder, “Artificial intelligence and its role in education,” Sustain., vol.
13, no. 22, pp. 1–11, 2021, doi: 10.3390/su132212902.
[25] K. P. Singh and S. Gupta, “Artificial intelligence based modeling for
predicting the disinfection by-products in water,” Chemom. Intell.
Lab. Syst., vol. 114, pp. 122–131, 2012, doi:
10.1016/j.chemolab.2012.03.014.
[26] S. R. Mounce, C. Pedraza, T. Jackson, P. Linford, and J. B. Boxall,
“Cloud based machine learning approaches for leakage assessment
and management in smart water networks,” Procedia Eng., vol. 119,
no. 1, pp. 43–52, 2015, doi: 10.1016/j.proeng.2015.08.851.
[27] J. C. Preciado, Á. E. Prieto, R. Benitez, R. Rodríguez-Echeverría, and
J. M. Conejero, “A High-Frequency Data-Driven Machine Learning
Approach for Demand Forecasting in Smart Cities,” Sci. Program.,
vol. 2019, 2019, doi: 10.1155/2019/8319549.
[28] L. K. Narayanan and S. Sankaranarayanan, “IoT-based water demand
forecasting and distribution design for smart city,” J. Water Clim.
Chang., vol. 11, no. 4, pp. 1411–1428, 2020, doi:
10.2166/wcc.2019.019.
[29] H. Apaydin, H. Feizi, M. T. Sattari, and M. S. Colak, “Comparative
Analysis of Recurrent Neural Network,” Water (Switzerland), vol. 12,
pp. 1–18, 2020.
[30] H. Jenny, Y. Wang, E. G. Alonso, and R. Minguez, “Using Artificial
Intelligence for Smart Water Management Systems,” vol. 5, no. 143,
2020, [Online]. Available:
https://www.adb.org/publications/artificial-intelligence-water-
supply-ufw.
[31] M. Oberascher, W. Rauch, and R. Sitzenfrei, “Towards a smart water
city: A comprehensive review of applications, data requirements, and
communication technologies for integrated management,” Sustain.
Cities Soc., vol. 76, no. October 2021, 2022, doi:
10.1016/j.scs.2021.103442.
[32] F. Cugurullo, “Urban Artificial Intelligence: From Automation to
Autonomy in the Smart City,” Front. Sustain. Cities, vol. 2, no. July,
pp. 1–14, 2020, doi: 10.3389/frsc.2020.00038.
[33] T. Yigitcanlar et al., “Artificial intelligence technologies and related
urban planning and development concepts: How are they perceived
and utilized in Australia?,” J. Open Innov. Technol. Mark. Complex.,
vol. 6, no. 4, pp. 1–21, 2020, doi: 10.3390/joitmc6040187.
[34] Toni Kaatz-Dubberke and L. Kehl, “Artificial Intelligence in Urban
Development Opportunities, challenges and ideas for the German
Development Cooperation,” 2020, [Online]. Available: www.bmz.de.
[35] G. Calzolari, “Requested by the AIDA committee Artificial
Intelligence market and capital flows,” no. May, 2021.
[36] A. K. Jha, A. Ghimire, S. Thapa, A. M. Jha, and R. Raj, “A Review
of AI for Urban Planning: Towards Building Sustainable Smart
Cities,” Proc. 6th Int. Conf. Inven. Comput. Technol. ICICT 2021, no.
February, pp. 937–944, 2021, doi:
10.1109/ICICT50816.2021.9358548.
[37] S. N. Fallah, R. C. Deo, M. Shojafar, M. Conti, and S. Shamshirband,
“Computational intelligence approaches for energy load forecasting in
smart energy management grids: State of the art, future challenges,
and research directions,” Energies, vol. 11, no. 3, 2018, doi:
10.3390/en11030596.
[38] S. Zhou, Z. Hu, W. Gu, M. Jiang, and X.-P. Zhang, “Artificial
intelligence based smart energy community management: A
reinforcement learning approach,” CSEE J. Power Energy Syst., no.
June 2020, 2019, doi: 10.17775/cseejpes.2018.00840.
[39] A. C. Serban and M. D. Lytras, “Artificial intelligence for smart
renewable energy sector in europe - Smart energy infrastructures for
next generation smart cities,” IEEE Access, vol. 8, pp. 77364–77377,
2020, doi: 10.1109/ACCESS.2020.2990123.
[40] J. Basnayake, R. Amarasinghe, R. Attalage, and T. Udayanga,
“Artificial Intelligence Based Smart Building Automation Controller
for Energy Efficiency Improvements in Existing Buildings,” no.
September, 2015, doi: 10.15693/ijaist/2015.v4i8.85-91.
[41] A. Rochd et al., “Design and implementation of an AI-based & IoT-
enabled Home Energy Management System: A case study in
Benguerir — Morocco,” Energy Reports, vol. 7, no. May, pp. 699–
719, 2021, doi: 10.1016/j.egyr.2021.07.084.
[42] H. R. O. Rocha, I. H. Honorato, R. Fiorotti, W. C. Celeste, L. J.
Silvestre, and J. A. L. Silva, “An Artificial Intelligence based
scheduling algorithm for demand-side energy management in Smart
Homes,” Appl. Energy, vol. 282, no. July 2020, 2021, doi:
10.1016/j.apenergy.2020.116145.
[43] M. Raval, S. Bhardwaj, A. Aravelli, J. Dofe, and H. Gohel, “Smart
energy optimization for massive IoT using artificial intelligence,”
Internet of Things (Netherlands), vol. 13, 2021, doi:
10.1016/j.iot.2020.100354.
[44] Y. Ma, X. Chen, L. Wang, and J. Yang, “Study on Smart Home
Energy Management System Based on Artificial Intelligence,” J.
Sensors, vol. 2021, 2021, doi: 10.1155/2021/9101453.
[45] J. M. Gutierrez, M. Jensen, M. Henius, and T. Riaz, “Smart Waste
Collection System Based on Location Intelligence,” Procedia
Comput. Sci., vol. 61, pp. 120–127, 2015, doi:
10.1016/j.procs.2015.09.170.
[46] N. Sathish Kumar, B. Vuayalakshmi, R. J. Prarthana, and A. Shankar,
“IOT based smart garbage alert system using Arduino UNO,” IEEE
Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 0, pp. 1028–
1034, 2016, doi: 10.1109/TENCON.2016.7848162.
[47] Z. Oralhan, B. Oralhan, and Y. Yiğit, “Smart city application: Internet
of Things (IoT) technologies based smart waste collection using data
mining approach and ant colony optimization,” Int. Arab J. Inf.
Technol., vol. 14, no. 4, pp. 423–427, 2017.
[48] I. Aktemur, K. Erensoy, and E. Kocyigit, “Optimization of Waste
Collection in Smart Cities with the use of Evolutionary Algorithms,”
Proceedings of the Sixth International Conference on Intelligent Computing and Control Systems (ICICCS 2022)
IEEE Xplore Part Number: CFP22K74-ART; ISBN: 978-1-6654-1035-9
978-1-6654-1035-9/22/$31.00 ©2022 IEEE 992
Authorized licensed use limited to: Indian Institute of Technology Hyderabad. Downloaded on June 22,2022 at 05:18:43 UTC from IEEE Xplore. Restrictions apply.

HORA 2020 - 2nd Int. Congr. Human-Computer Interact. Optim.
Robot. Appl. Proc., 2020, doi: 10.1109/HORA49412.2020.9152865.
[49] P. K. Gupta, V. Shree, L. Hiremath, and S. Rajendran, The use of
modern technology in smart waste management and recycling:
Artificial intelligence and machine learning, vol. 823. Springer
International Publishing, 2019.
[50] K. Thibuy, S. Thokrairak, and P. Jitngernmadan, “Holistic Solution
Design and Implementation for Smart City Recycle Waste
Management Case Study: Saensuk City,” InCIT 2020 - 5th Int. Conf.
Inf. Technol., pp. 233–237, 2020, doi:
10.1109/InCIT50588.2020.9310948.
[51] F. El Bouanani and F. Ayoub, “Toward a Deep Smart Waste
Management System based on Pattern Recognition and Transfer
learning,” 2020.
[52] A. B. Jude et al., “An Artificial Intelligence Based Predictive
Approach for Smart Waste Management,” Wirel. Pers. Commun., no.
0123456789, 2021, doi: 10.1007/s11277-021-08803-7.
[53] G. U. Fayomi et al., “Smart Waste Management for Smart City:
Impact on Industrialization,” IOP Conf. Ser. Earth Environ. Sci., vol.
655, no. 1, 2021, doi: 10.1088/1755-1315/655/1/012040.
[54] C. Tong, J. Li, C. Lang, F. Kong, J. Niu, and J. J. P. C. Rodrigues, “An
efficient deep model for day-ahead electricity load forecasting with
stacked denoising auto-encoders,” J. Parallel Distrib. Comput., vol.
117, pp. 267–273, 2018, doi: 10.1016/j.jpdc.2017.06.007.
[55] S. Li, Y. Han, X. Yao, S. Yingchen, J. Wang, and Q. Zhao, “Electricity
Theft Detection in Power Grids with Deep Learning and Random
Forests,” J. Electr. Comput. Eng., vol. 2019, 2019, doi:
10.1155/2019/4136874.
[56] S. Rai and M. De, “Analysis of classical and machine learning based
short-term and mid-term load forecasting for smart grid,” Int. J.
Sustain. Energy, vol. 40, no. 9, pp. 821–839, 2021, doi:
10.1080/14786451.2021.1873339.
[57] M. S. Ibrahim, W. Dong, and Q. Yang, “Machine learning driven
smart electric power systems: Current trends and new perspectives,”
Appl. Energy, vol. 272, no. May, 2020, doi:
10.1016/j.apenergy.2020.115237.
[58] S. You et al., “A review on artificial intelligence for grid stability
assessment,” 2020 IEEE Int. Conf. Commun. Control. Comput.
Technol. Smart Grids, SmartGridComm 2020, 2020, doi:
10.1109/SmartGridComm47815.2020.9302990.
[59] O. A. Omitaomu and H. Niu, “Artificial intelligence techniques in
smart grid: A survey,” Smart Cities, vol. 4, no. 2, pp. 548–568, 2021,
doi: 10.3390/smartcities4020029.
[60] I. Farkas, “Artificial intelligence in agriculture,” Comput. Electron.
Agric., vol. 40, no. 1–3, pp. 1–3, 2003, doi: 10.1016/S0168-
1699(03)00007-3.
[61] T. Talaviya, D. Shah, N. Patel, H. Yagnik, and M. Shah,
“Implementation of artificial intelligence in agriculture for
optimisation of irrigation and application of pesticides and
herbicides,” Artif. Intell. Agric., vol. 4, pp. 58–73, 2020, doi:
10.1016/j.aiia.2020.04.002.
[62] J. S. H. Al-Bayati and B. B. Ustundag, “Artificial Intelligence in
Smart Agriculture: Modified Evolutionary Optimization Approach for
Plant Disease Identification,” 4th Int. Symp. Multidiscip. Stud. Innov.
Technol. ISMSIT 2020 - Proc., 2020, doi:
10.1109/ISMSIT50672.2020.9255323.
[63] A. F. Santos et al., “Using UAV and Multispectral Images to Estimate
Peanut Maturity Variability on Irrigated and Rainfed Fields Applying
Linear Models and Artificial Neural Networks,” Remote Sens., vol.
14, no. 1, 2022, doi: 10.3390/rs14010093.
[64] P. Silakari, P. Silakari, L. Bopche, and A. Gupta, “Smart Ultrasonic
Insects Pets Repeller for Farms Inventories Purpose,” 2018 Int. Conf.
Adv. Comput. Telecommun. ICACAT 2018, no. September 2020, pp.
1–3, 2018, doi: 10.1109/ICACAT.2018.8933607.
[65] S. David, R. S. Anand, and M. Sagayam, “Enhancing AI based
evaluation for smart cultivation and crop testing using agro-datasets,”
J. Artif. Intell. Syst., vol. 2, no. 1, pp. 149–167, 2020, doi:
10.33969/ais.2020.21010.
[66] S. Choudhary, V. Gaurav, A. Singh, and S. Agarwal, “Autonomous
crop irrigation system using artificial intelligence,” Int. J. Eng. Adv.
Technol., vol. 8, no. 5 Special Issue, pp. 46–51, 2019.
[67] M. Rath, “Smart Traffic Management System for Traffic Control
using Automated Mechanical and Electronic Devices,” IOP Conf. Ser.
Mater. Sci. Eng., vol. 377, no. 1, 2018, doi: 10.1088/1757-
899X/377/1/012201.
[68] D. Bamwesigye and P. Hlavackova, “Analysis of sustainable transport
for smart cities,” Sustain., vol. 11, no. 7, 2019, doi:
10.3390/SU11072140.
[69] J. Guerrero-Ibáñez, S. Zeadally, and J. Contreras-Castillo, “Sensor
technologies for intelligent transportation systems,” Sensors
(Switzerland), vol. 18, no. 4, pp. 1–24, 2018, doi: 10.3390/s18041212.
[70] C. Louis, T. Tcheumadjeu, D. Suske, A. Sohr, and X. Bei, “Traffic
Information Systems for Smart Mobility as part of Smart Cities,” Int.
Symp. Smart Mobil. Smart Cities (Smart Cities, 2016), no. SmartCity,
pp. 1–14, 2016.
[71] R. M. Savithramma and R. Sumathi, “Road traffic signal control and
management system: A survey,” Proc. 3rd Int. Conf. Intell. Sustain.
Syst. ICISS 2020, pp. 104–110, 2020, doi:
10.1109/ICISS49785.2020.9315970.
[72] Polytechnique Montreal, “Smart cities and integrated mobility: A
white paper,” Next Gener. Integr. Moblity Driv. Smart City, pp. 1–78,
2018.
[73] A. Boukerche, Y. Tao, and P. Sun, “Artificial intelligence-based
vehicular traffic flow prediction methods for supporting intelligent
transportation systems,” Comput. Networks, vol. 182, no. October
2019, 2020, doi: 10.1016/j.comnet.2020.107484.
[74] G. N. Kouziokas, “The application of artificial intelligence in public
administration for forecasting high crime risk transportation areas in
urban environment,” Transp. Res. Procedia, vol. 24, pp. 467–473,
2017, doi: 10.1016/j.trpro.2017.05.083.
[75] R. M. Savithramma, R. Sumathi, and H. S. Sudhira, “A Comparative
Analysis of Machine Learning Algorithms in Design Process of
Adaptive Traffic Signal Control System,” J. Phys. Conf. Ser., vol.
2161, no. 1, 2022, doi: 10.1088/1742-6596/2161/1/012054.
[76] P. K. Agarwal, J. Gurjar, A. K. Agarwal, and R. Birla, “Application
of Artificial Intelligence for Development of Intelligent Transport
System in Smart Cities,” Jtets, vol. 1, no. 1, pp. 20–30, 2015, [Online].
Available: www.journalspub.com.
[77] M. Machin, J. A. Sanguesa, P. Garrido, and F. J. Martinez, “On the
use of Artificial Intelligence techniques in Intelligent Transportation
Systems,” IEEE Wirel. Commun. Netw. Conf. Work. WCNCW 2015,
pp. 332–337, 2015.
[78] B. P. A. and R. S. R. M. Savithramma, “Smart Mobility
Implementation in Smart Cities: A Comprehensive Review on State-
of-art Technologies,” in 2022 4th International Conference on Smart
Systems and Inventive Technology (ICSSIT), 2022, pp. 10-17, doi:
10.1109/ICSSIT53264.2022.9716288.
[79] G. Pau, A. Severino, and A. Canale, “Special issue ‘new perspectives
in intelligent transportation systems and mobile communications
towards a smart cities context,’” Futur. Internet, vol. 11, no. 11, pp.
11–13, 2019, doi: 10.3390/fi11110228.
[80] A. Nikitas, K. Michalakopoulou, E. T. Njoya, and D. Karampatzakis,
“Artificial intelligence, transport and the smart city: Definitions and
dimensions of a new mobility era,” Sustain., vol. 12, no. 7, pp. 1–19,
2020, doi: 10.3390/su12072789.
[81] B. P. Ashwini, R. Sumathi, and H. S. Sudhira, “Bus Travel Time
Prediction: A Comparative Study of Linear and Non-Linear Machine
Learning Models,” J. Phys. Conf. Ser., vol. 2161, no. 1, 2022, doi:
10.1088/1742-6596/2161/1/012053.
Proceedings of the Sixth International Conference on Intelligent Computing and Control Systems (ICICCS 2022)
IEEE Xplore Part Number: CFP22K74-ART; ISBN: 978-1-6654-1035-9
978-1-6654-1035-9/22/$31.00 ©2022 IEEE 993
Authorized licensed use limited to: Indian Institute of Technology Hyderabad. Downloaded on June 22,2022 at 05:18:43 UTC from IEEE Xplore. Restrictions apply.