Decision analysis of IoT-based big data analytics in smart cities of Saudi Arabia

The smart cities are being created to enhance livability for residents by utilizing Internet of Things (IoT) technology to improve urban infrastructure, increase services, and cut costs globally. Saudi Arabia’s Vision 2030 aims to transform urban living through Smart Cities (SCs), leveraging IoT-based big data analytics to enhance infrastructure, services, and cost-efficiency. Decision analysis is the most important task in a high volume of changing data, in SCs. This study explores how IoT and big data analytics can optimize decision-making for SC development in Saudi Arabia. The data analytics (DA) enables the gathering, processing, and analysis of enormous amounts of data coming from various IoT-based infrastructures across the nation. IoT-based DA is a fast-emerging domain of Information Technology (IT) infrastructure in SC. The quantitative research employs the Fuzzy Analytic Hierarchy Process (FAHP) to evaluate key factors. The analysis reveals economic growth as the top priority (weight: 0.24), followed by good governance (0.103) and transport (0.095), with economic growth’s subfactor (G12) scoring highest at 0.17016. The study proposes a framework for integrating IoT-based big data analytics, examines opportunities and challenges, and offers recommendations for effective implementation, fostering sustainable urban ecosystems. The study also pursues the opportunities and challenges in using IoT-based big data and DA in SCs, and recommends mechanisms for the successful implementation of such initiatives.

Authors

Institutions

Publication Details

Journal
PeerJ Computer Science
Published
2025-12-08
DOI
https://doi.org/10.7717/peerj-cs.3383
Primary Topic
Smart Cities and Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Decision analysis of IoT-based big data analytics in smart cities of Saudi Arabia

Bader Alouffi, Ahmed Almulihi, Abdullah Alharbi, Raees Ahmad Khan et al.
PeerJ Computer Science
Smart Cities and Technologies
article

Decision analysis of IoT-based big data analytics in smart cities of Saudi Arabia

Bader Alouffi, Ahmed Almulihi, Abdullah Alharbi, Raees Ahmad Khan, Mohd Nadeem, Masood Ahmad, Wael Alosaimi, Hashem Alyami
article en

Abstract

The smart cities are being created to enhance livability for residents by utilizing Internet of Things (IoT) technology to improve urban infrastructure, increase services, and cut costs globally. Saudi Arabia’s Vision 2030 aims to transform urban living through Smart Cities (SCs), leveraging IoT-based big data analytics to enhance infrastructure, services, and cost-efficiency. Decision analysis is the most important task in a high volume of changing data, in SCs. This study explores how IoT and big data analytics can optimize decision-making for SC development in Saudi Arabia. The data analytics (DA) enables the gathering, processing, and analysis of enormous amounts of data coming from various IoT-based infrastructures across the nation. IoT-based DA is a fast-emerging domain of Information Technology (IT) infrastructure in SC. The quantitative research employs the Fuzzy Analytic Hierarchy Process (FAHP) to evaluate key factors. The analysis reveals economic growth as the top priority (weight: 0.24), followed by good governance (0.103) and transport (0.095), with economic growth’s subfactor (G12) scoring highest at 0.17016. The study proposes a framework for integrating IoT-based big data analytics, examines opportunities and challenges, and offers recommendations for effective implementation, fostering sustainable urban ecosystems. The study also pursues the opportunities and challenges in using IoT-based big data and DA in SCs, and recommends mechanisms for the successful implementation of such initiatives.

PeerJ Computer ScienceVol. 11
Babasaheb Bhimrao Ambedkar University (IN), Taif University (SA), Shri Ramswaroop Memorial University (IN), GLA University (IN)
Openalex Percentile: Top 49%
Smart Cities and Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.