# A Review of the Impact of Big Data on Smart Cities

Milankumar Rana

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# A Review of the Impact of Big Data on
Smart Cities

Milankumar Rana a*

a Department of Information Technology, University of the Cumberlands, Williamsburg, KY 40769,
USA.

**Author's contribution**

The sole author designed, analysed, interpreted and prepared the manuscript.

**Article Information**

DOI: https://doi.org/10.56557/jobari/2025/v31i29162

**Open Peer Review History:**

This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers, peer review comments, different versions of the manuscript, comments of the editors, etc are available here: https://prh.ikprress.org/review-history/12823

Received: 01/01/2025

Accepted: 04/03/2025

Published: 07/03/2025

**Review Article**

## ABSTRACT

The purpose of this research paper is to write literature reviews on big data applications on smart cities. Naoui et al. (2020) have done research on how big data with deep learning architecture is used by smart cities. Naoui et al. (2020) have proposed three-layer architecture where smart city layer is used to identify components and IOT sensors used for development of system. Big data layer for data distributions and deep learning for data analysis. (Naoui et al., 2020) analyzed Toluene forecasting for Madrid and Wind Energy forecasting for Australia. (Naoui et al., 2020) have claimed that their approach will be useful in better decision making in Toluene forecasting and accurate prediction in power generation. Tiwari et al. (2019) have conducted literature reviews using the data collected from various sources to analyze the techniques used in the original research to write about the literature. This paper reviews the architecture and technique used to implement and find problems.

Keywords: Bigdata; smart cities; IOT; sensors; deep learning; data analysis; forecasting; predictive analytics.

<footnote>*Corresponding author: E-mail: mrana02098@ucumberlands.edu, milan.dgen@gmail.com;</footnote>

<footnote>Cite as: Rana, Milankumar. 2025. "A Review of the Impact of Big Data on Smart Cities". Journal of Basic and Applied Research International 31 (2):55-64. https://doi.org/10.56557/jobari/2025/v31i29162.</footnote>
# 1. INTRODUCTION

The implementation of Smart City requires great planning and executions. Big data consists of proper framework to generate accurate results can help implement city become better and smarter. To implement ICT with IOT sensors and digital devices with minimum error is required to collect data that can be used to feed big data analysis. Connectivity between two different urban city is most important to compare the data and improve the decision making based on best result outcome. "Big data deals with enormous volume and complex sets of data". Naoui et al. (2020). Big data requires proper data storage and data processing techniques to understand and generate prediction. AS these IOT Devices collects various data from various sources big data needs to review and upgrade with latest database techniques to develop smart cities.

# 2. RESEARCH QUESTIONS

## 2.1 Citizen-Centered Approach

Ju et al. (2018) have described about citizen centered approach to implement big data in smart cities. In a smart city model, governance decisions are bolstered through the utilization of big data analytics approaches. This is achieved by gathering data and deploying analysis-supported algorithms. (Al, et al., 2015). This research study discusses the integration of data, the process of discovering information, public participation in decision-making, and the delivery of services in urban governance. The analytics algorithm operates within the realms of social and computational, which are utilized within the framework (Bates, et al., 2014). This research paper provides a comprehensive analysis of the existing literature on smart cities, with a particular focus on citizen-centered administration and the utilization of big data. The main objective is to establish a conceptual framework for further development in this field. The objective of this research was to develop a framework that utilizes citizen-centered big data techniques to facilitate the construction of a smart city with a focus on governance intelligence. This framework facilitates the decision-making process by coordinating the data analysis procedure with the proposed governance intelligence strategy by Baesens et al. (2016).

## 2.2 Edge Computing

Hossain et al. (2018) have suggested the implementation of edge computing for an Internet

of Things (IoT) system in a smart city. This system aims to assist decision makers in delivering appropriate services in metropolitan areas. This research focused on the implementation concerns, challenges, and outcomes that were identified through a review of existing literature. This research analyzed the present state of transportation, healthcare facilities, and safety in smart cities (Bélissent, 2010). In this literature Tiwari et al. (2019) provided IOT importance but IOT needs to put in proper places to collect more information, also considering different IoT devices, it will be a challenge to parse raw data and perform transformation. IoT devices should not be misplaced or hacked by any means otherwise dummy data will create an issue.

## 2.3 IoT for Smart Cities

Bibri (2018) conducted a comprehensive analysis and synthesis of relevant research to initiate a discussion on the integration of innovative concepts and characteristics that are applicable to sensors and big data. This framework, based on the Internet of Things (IoT), is specifically developed to accomplish sustainability objectives and offer a data processing paradigm for a smart city. The research outlined obstacles to the use of Internet of Things (IoT) and big data in a smart city model. It also examines the process of integrating IoT and big data to attain sustainability. This framework incorporates existing studies on smart cities and encompasses research that is pertinent to the development of sustainable cities through a conceptual and analytics-based paradigm. This article also examined an Internet of Things (IoT)-based solution for the sustainable development of smart cities. Moreover, this literature elucidates diverse implementations of big data and IoT to achieve a particular degree of sustainability.

## 2.4 Compact and Green Cities

Artmann et al. (2019) introduced a theoretical framework for the development of intelligent and environmentally friendly urban areas. This paradigm integrates the notion of artistic advancement and ecological dimensions of progress in dynamic regions. This study aimed to explore strategies for the development of green infrastructure in response to the needs of smart cities. This literature asserts that the implementation of green infrastructure can be accomplished by incorporating the principles of
smart growth. This study examines the correlation between green infrastructure and the development of compact and environmentally friendly cities. This research aims to not only generate a novel signal but also establish intercommunication between existing signals in the context of smart growth. This research study assists researchers in formulating a methodology for creating a smart city and monitoring its progress towards becoming compact and environmentally friendly.

## 2.5 Future of Waste Management in Smart Cities

Esmaeilian et al. (2018) introduced a framework for trash management in smart cities. The framework consists of three components: product lifecycle management, business model, and advanced infrastructure. According to the literature, the utilization of sensors for the purpose of monitoring and controlling trash is referred to as electronic waste management (Zanella, et al., 2014). This research article examines a waste management application that utilizes Internet of Things (IoT) technology. The paper reviews existing literature and proposes a framework to address the shortcomings of current trash management models. Smart cities employ several tactics such as waste avoidance mechanisms, upstream waste separation, garbage collection, and value creation to attain zero waste sustainability. The waste management system in the smart city is connected to the product lifecycle using an IoT-based application.

## 3. RESEARCH METHODOLOGY

Bibliometrics analyzes publication trends, authors, journals, etc. Bibliometric analysis helps uncover relevant research and smart city literature trends. It is a popular research paper review method. Bibliometric analysis began in 1962 and was adopted in 1972 to administer the research library. R runs bibliometric and network

analysis. This study extracts essential statistics and interrelationships from bibliographic data to comprehend and assess a research field.

We chose Scopus because it provides access to scholarly publications. Scopus has more journals and top-rated journals than "Web of Science." The largest peer-reviewed literature database is Scopus. Scopus covers 20% more research items than Web of Science. Scopus also expands keyword and citation analysis journal coverage. To find research papers on Scopus, choose domain-related keywords. The smart city keyword in Table 1 was used to find research publications. Nearly 14,018 research publications were found.

We also searched for research articles using industry 4.0, big data, big data analytics, and IoT. The final set of documents was 6,796. Search terms relating to the paper were used to filter the results. We limited Scopus search to journal articles and found 1,690 documents. Multiple keywords are used for theme search. We specified that only English-language peer-reviewed research articles were included to further restrict our studies. This theme search yielded 774 Scopus research publications after several searches.

### 3.1 Bibliometric Analysis

Bibliometric analysis are useful in identifying research papers related to same cities from various sources. This type of method is useful in identifying latest trend and how countries are adopting big data for smart cities development. Tiwari et al. (2019) considered six years of research papers to identify which country is perform better than other in terms of smart city development including 2013-2018. It has been observed that the research increased over the years and also the big data improvements happened gradually over the years. It shows that China leads in research publication about big data in smart cities followed by USA and Italy.

<table><caption>Table 1. Bibliographical record</caption><thead><tr><td>Levels Keywords</td><td>No. of articles</td></tr></thead><tbody><tr><td>1. "smart city" OR "smart cities"</td><td>14,018</td></tr><tr><td>2. "Industry 4.0" OR "big data" OR "big data analytics" OR "internet-of-things" OR IoT</td><td>6,796</td></tr><tr><td>3. Restricting to journal articles</td><td>1,690</td></tr><tr><td>4. Applying inclusions and exclusions criteria and manual filtering</td><td>774</td></tr></tbody></table>
## 3.2 Network Analysis

To find correlations and patterns in massive graphical data, network analysis is utilized. This descriptive strategy that derives significant insights from data flexibly addresses a large category of data to determine a research question and themes. This approach can be adjusted to meet smart city paper co-citation network concerns. Network analysis is easy to execute and requires few theoretical and technological principles. It conveys the main elements of enormous data sets to produce a well-structured proposal that clarifies the research field. R and Bib-excel are good network analytics software. The cluster network is presented in Fig. 1. Smart cities have the most important group. These clusters interact and provide big data, smart city, and IOT terminology.

## 3.3 Big Data

Big data deals with the massive volume of data that cannot be processed using the traditional

database technique which required to managing the data (Zikopoulos & Eaton, 2011). Portable devices, IoT devices like sensors and Social Media are samples of advanced digital technologies that are useful in our daily routine and living standard. Big data is not only characterized by volume, but it also has some other characteristics such as velocity, variety and veracity. These characteristics are called as four Vs. of Big data.

## 3.4 Smart City

According to the literature, smart cities have different definitions, but the metropolitan region needs more study. According to Hollands (2008), smart cities have infrastructure constructed by many interconnected technical gadgets. It aims to improve urban government, quality of life, and living standards via innovative data processing. ICT is essential for smart city project execution (Nam & Pardo, 2011, Kitchin, 2014 a, b).

<figure><img src="image_3.png" /><figcaption>Fig. 1. Network analysis of massive graphical data</figcaption></figure>
## 3.5 Data Mining

Data mining finds hidden patterns in massive datasets. Sorting utilizing data analytics finds patterns and relationships. These provide future prediction based on present and past trends. The statistical characteristics include categorization, grouping, and forecasting (Hand, 2006, Ranjan & Malik, 2007).

## 3.6 Edge Computing

Computing on smart gadgets or edge computing nodes is distributed computing. It improves computing device interaction with wireless and intelligent devices. Fog computing optimizes a system's cloud by processing applications at the edge of a computer network. Fig. 2 shows the analytics-derived paper network. The R programming language creates this network. It is a Scopus-selected article network. This network has six cluster modules with tiny groups that connect.

## 3.7 Content Analysis

The research method assesses papers and photographs. Qualitative data becomes quantitative. Academics use content analysis to find research patterns. Filling the qualitative-

quantitative research gap. It allows researchers to acquire massive data sets that would be impossible to collect qualitatively and obtain new insights from them (Elo & Kyngäs, 2008). It lets academics research many important and challenging problems utilizing the most data. It helps social scientists identify communication patterns. Open clusters are analyzed for qualitative statistical analysis. It tracks content change (Neuendorf, 2016).

## 3.8 Observation

This smart city literature review grouped 774 Scopus papers by big data, privacy, network security, IoT, information management, and sustainability. Table 2 includes top 10 big data-cited smart city articles. Countries researched smart cities. The participation table shows country-specific smart city research. China dominates big data-driven digital city R&D. India is sixth in smart city research. Indian top 10 status aids studies. US research articles rank second with 134 and Italy third with 92. China and India have 142 and 47 papers on this topic. Stats motivate Indians to work more and rank higher. Emerging nations develop sustainably. Countries aim for SDGs through smart and green development to manage resources.

<figure><img src="image_4.png" /><figcaption>Fig. 2. Edge computing</figcaption></figure>
<table><caption>Table 2. Observational results</caption><thead><tr><td>S. No.</td><td>Title</td><td>Contribution</td><td>Research Methodology</td><td>Citation</td></tr></thead><tbody><tr><td>1</td><td>Internet of Things for smart cities (Zanella, et al., 2014)</td><td>-</td><td>Literature review</td><td>2,688</td></tr><tr><td>2</td><td>The real-time city? Big data and smart urbanism (Kitchin, 2014a, b)</td><td>-</td><td>Project overview</td><td>1,253</td></tr><tr><td>3</td><td>Current trends in smart city initiatives: some stylized facts (Neirotti, et al., 2014)</td><td>The author contributes to providing solutions and guidelines in adopting Padova smart city project by using a comprehensive survey of the enabling technologies, protocols, and architecture of urban IoT Concepts for real-time overview and analysis of smart city data, and suggests a critical reflection on big data and smart urbanization.</td><td>Empirical analysis</td><td>1,028</td></tr><tr><td>4</td><td>Smart cities: definitions, dimensions, performance, and initiatives (Albino, et al., 2015)</td><td>Review of smart city relevant studies and identify its dimensions and triple helix initiatives.</td><td>Literature review</td><td>991</td></tr><tr><td>5</td><td>Integration of cloud computing and the Internet of Things: a survey (Botta, et al., 2015)</td><td>The author identifies the current research direction in the integration of cloud and IoT for a smart city.</td><td>Literature review</td><td>953</td></tr><tr><td>6</td><td>Sensing as a service model for smart cities supported by Internet of Things (Perera, et al., 2017)</td><td>A comprehensive overview of sensing with IoT applications in the smart city model.</td><td>Literature survey</td><td>663</td></tr><tr><td>7</td><td>A survey on Internet of Things: architecture, enabling technologies, security and privacy, edge computing, and IoT-based applications (Lin, et al., 2017)</td><td>The comprehensive review of IoT, along with the integration of fog/edge computing and applications.</td><td>Literature review and comprehensive survey</td><td>475</td></tr><tr><td>8</td><td>Smart cities as corporate storytelling (Söderström, et al., 2014)</td><td>The author analyzes IBM's smart city campaign and aims at making the company an "obligatory passage point" in the implementation of advanced technologies.</td><td>Survey-based, critical discourse analysis, and interview</td><td>380</td></tr><tr><td>9</td><td>Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges (Gravina, et al., 2017)</td><td>The author presented a systematic literature review by identifying various properties and parameters affecting data fusion design techniques.</td><td>State-of-the-art literature review</td><td>275</td></tr><tr><td>10</td><td>Internet of Things and Big Data analytics for smart and connected communities (Sun, et al., 2016)</td><td>The author promotes the concept of smart and connected communities, which is evolving from the concept of urban development.</td><td>Literature review and case analysis</td><td>271</td></tr></tbody></table>
Table 2 lists big data analytics-using smart city researchers' top contributions. Major research journals. Smart city tech phrases divide clusters. These clusters include big data, privacy, IoT, network security, information management, and sustainability.

Big data analytics, research methods, network analysis, and content analysis reveal smart city literary patterns. Analysis clusters are divided into six components to compose this study. The six clusters explore big data analytics-based smart city deployment. IoT, big data, information management, network security, privacy, and sustainability are technical factors. Technical solutions for big data-based smart cities depend on six factors. Technological concepts underpin smart cities.

# 4. IMPORTANT TECHNOLOGIES IN SMART CITIES SYSTEMS

## 4.1 Big data Technologies

Big data is large amounts of data that cannot be managed using typical database methods (Lamba & Singh, 2017). Advanced digital technologies including portable gadgets, IoT sensors, and social media improve our daily lives. Daily use of digital technologies promotes human-to-human, human-to-digital, and machine-to-machine communication, generating huge data. Cyber-physical interaction in smart cities creates a lot of data. To benefit from enormous amounts of data, big data analytics is needed.

## 4.2 IoT and Sensors

The Internet of Things connects computing devices. It uses internet technologies to connect and access physical objects. IoT supports smart city development for several reasons. The many IoT devices needed for smart city deployment create a lot of data. Using IoT in smart cities can improve management and optimization of services such as transportation, healthcare, parking, and CCTV cameras.

## 4.3 Data Privacy

Privacy is crucial when employing big data and IoT. Privacy is essential when using big data analytics systems. Maintaining user data integrity and security is essential to strong user interactions and smart growth (Zhang, et al.,

2017). Protection of transaction logs during data storage and validation of endpoints throughout data storage and processing are critical for privacy (Li, et al., 2015).

## 4.4 Network Security

Secure access and integration of networks and data is called network security. It uses hardware and software to access the system. It prevents numerous dangers from entering the network. The system's edge and center layers are combined to create it (Zhang, et al., 2017). It restricts access to the internet to authorized users and blocks threats and viruses. Access control, antivirus, application security, data loss prevention, e-mail security, firewalls, etc. are network security options (Centenaro, et al., 2016).

## 4.5 Sustainability

Sustainability in smart city development includes social, economic, and environmental aspects. Sustainability balances current demands. Users can improve their quality of life without harming the environment. It helps protect the environment by preserving natural resources for future generations. How consumers use resources determines smart city sustainability.

# 5. CHALLENGES IN USING BIG DATA FOR SMART CITIES

Many data transformation options exist for smart city big data. Change and new approaches are hard to implement. Thus, city transformation challenges must be identified. City alteration issues are discussed here.

Number 1 concerns data quality. Managers organize data quality. Smart city data must be accurate due to missing data collection values (Al, et al., 2015).

Integrating smart city data from many sources may be tough in Challenge 2. Data from multiple sources must be integrated to extract important and high-quality city administration data. Data from different sources has different structures, making linking difficult. Technical skill and time are needed for these tasks.

Challenge 3 addresses data privacy. Smart city participants must safeguard data. Privacy must be addressed in smart cities. To maintain value
and citizen happiness, smart cities must solve
these issues (Kitchin, 2014a, b).

Challenge 4 is recognizing citizen smart city involvement needs. Big data finds citizen-useful information. Data-driven smart city development needs finding valuable data (Afzalan, et al., 2017).

Challenge 5 covers geographic alteration data. In numerous cases, districts and structures have identified information. Geographic information is scarce, making region-based data analysis difficult (Aina, 2017).

Challenge 6 involves building smart city services that deliver urbanization data. Great service enhances unique customer interactions. This strategy enhanced smart city apps. Big data issues for smart city growth require technology expertise, citizen awareness, administration, data management, and service design.

6. CONCLUSION

Global urban development is embracing smart cities. Smart city models are utilized worldwide to solve population increase and resource shortages, which hurts the environment. States globally trust modern city growth to sustainably absorb natural resources. An intelligent city model helps the government save resources, reduce pollution, and plan for urbanization.

Developing nations want smart cities. It shows their commitment to green and smart society. Big data, IoT, cloud, and edge computing are essential for smart city development. These technologies enable smart city setup and monitoring. These current technologies are necessary for smart city deployment. Smart cities interested scholars, as 2017 and 2018 had the most study articles. Analysis revealed the top journals, authors, and nationalities in this study. Analytics helped build smart cities and advanced tech clusters. Cluster shows smart city implementation considerations. The above factors demonstrate smart city concepts. Details on smart city implementation terms are provided. One issue is that this research only addresses smart city technology. Economic, social, and environmental factors may be incorporated to the smart city paradigm. Future smart city model development may depend on national government policy on these topics. Government, academics, and business will develop with smart city economics. The smart city model affects

GDP growth, thus future studies should examine its implications. Each smart city model dimension needs more investigation. Understanding information technology, big data analytics, IoT, and blockchain is crucial before urban development since infrastructure upgrades are tough. This study examines smart city technology.

DISCLAIMER (ARTIFICIAL INTELLIGENCE)

Author(s) hereby declare that NO generative AI
technologies such as Large Language Models
(ChatGPT, COPILOT, etc) and text-to-image
generators have been used during writing or
editing of this manuscript.

COMPETING INTERESTS

Author has declared that no competing interests
exist.

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