Enhancing Smart City Infrastructure Through IoT-Enabled Predictive Maintenance and Real-Time Data Analytics

The rapid growth of urban populations has increased the demand for smarter, more efficient city infrastructure. This paper explores the integration of Internet of Things (IoT) technologies into smart city infrastructure, focusing on predictive maintenance and real-time data analytics. By embedding IoT sensors in critical infrastructure components such as transportation systems, energy grids, and water networks, cities can continuously monitor and collect data. Using predictive analytics, this data can forecast potential failures, reducing downtime and maintenance costs while ensuring continuous service. Additionally, real-time data analytics allow for better decision-making and dynamic response to emerging situations, improving resource allocation and overall city management. This paper also discusses the challenges, such as data security, scalability, and system interoperability, along with proposed solutions to ensure seamless IoT integration. The findings demonstrate the potential of IoT to enhance urban living through increased efficiency and sustainability.

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Publication Details

Published
2024-12-27
DOI
https://doi.org/10.1109/icpids65698.2024.00029
Citations
1
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
0.27
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Enhancing Smart City Infrastructure Through IoT-Enabled Predictive Maintenance and Real-Time Data Analytics

Ayush, Pradhyumn Singh, Parveen Bajaj, Priti Jain et al.
1 citations
Traffic Prediction and Management Techniques
0.27
article

Enhancing Smart City Infrastructure Through IoT-Enabled Predictive Maintenance and Real-Time Data Analytics

Ayush, Pradhyumn Singh, Parveen Bajaj, Priti Jain, K. Nalini Kumari, V. Saini
article en
1 citations

Abstract

The rapid growth of urban populations has increased the demand for smarter, more efficient city infrastructure. This paper explores the integration of Internet of Things (IoT) technologies into smart city infrastructure, focusing on predictive maintenance and real-time data analytics. By embedding IoT sensors in critical infrastructure components such as transportation systems, energy grids, and water networks, cities can continuously monitor and collect data. Using predictive analytics, this data can forecast potential failures, reducing downtime and maintenance costs while ensuring continuous service. Additionally, real-time data analytics allow for better decision-making and dynamic response to emerging situations, improving resource allocation and overall city management. This paper also discusses the challenges, such as data security, scalability, and system interoperability, along with proposed solutions to ensure seamless IoT integration. The findings demonstrate the potential of IoT to enhance urban living through increased efficiency and sustainability.

Chandigarh University (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 39%
Traffic Prediction and Management Techniques
0.27
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Enhancing Smart City Infrastructure Through IoT-Enabled Predictive Maintenance and Real-Time Data Analytics — Ayush, Pradhyumn Singh, et al. · (2024) | TGRS Research Map | TGRS