The Integration of IoT, AI, and Machine Learning in Urban Systems DOI

Mert Dündar

Next frontier., Journal Year: 2024, Volume and Issue: 8(1), P. 185 - 185

Published: Nov. 27, 2024

The integration of the Internet Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) into urban systems represents a transformative approach to addressing challenges modern cities. By enabling real-time data collection, predictive analytics, intelligent decision-making, these technologies enhance efficiency, sustainability, livability environments. IoT sensors collect vast amounts from interconnected systems, including transportation, energy, waste management, public safety. AI ML algorithms analyze this data, offering actionable insights optimizing resource allocation. This research explores synergistic impact IoT, AI, on emphasizing applications such as smart traffic energy-efficient buildings, maintenance infrastructure. Additionally, study addresses ethical technical implementing technologies, privacy, cybersecurity, system scalability. examining real-world case studies innovative frameworks, paper highlights potential integrated redefine planning paving way for sustainable cities future.

Language: Английский

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review DOI Creative Commons
Simon Elias Bibri, Jeffrey Huang,

Senthil Kumar Jagatheesaperumal

et al.

Environmental Science and Ecotechnology, Journal Year: 2024, Volume and Issue: 20, P. 100433 - 100433

Published: May 17, 2024

In the dynamic landscape of sustainable smart cities, emerging computational technologies and models are reshaping data-driven planning strategies, practices, approaches, paving way for attaining environmental sustainability goals. This transformative wave signals a fundamental shift — marked by synergistic operation artificial intelligence (AI), things (AIoT), urban digital twin (UDT) technologies. While previous research has largely explored AI, AIoT, UDT in isolation, significant knowledge gap exists regarding their interplay, collaborative integration, collective impact on context cities. To address this gap, study conducts comprehensive systematic review to uncover intricate interactions among these interconnected technologies, models, domains while elucidating nuanced dynamics untapped synergies complex ecosystem Central four guiding questions: What theoretical practical foundations underpin convergence UDT, planning, how can components be synthesized into novel framework? How does integrating AI AIoT reshape improve performance cities? augment capabilities enhance processes challenges barriers arise implementing what strategies devised surmount or mitigate them? Methodologically, involves rigorous analysis synthesis studies published between January 2019 December 2023, comprising an extensive body literature totaling 185 studies. The findings surpass mere interdisciplinary enrichment, offering valuable insights potential advance development practices. By enhancing processes, integrated offer innovative solutions challenges. However, endeavor is fraught with formidable complexities that require careful navigation mitigation achieve desired outcomes. serves as reference guide, spurring groundbreaking endeavors, stimulating implementations, informing strategic initiatives, shaping policy formulations sustainable, development. These have profound implications researchers, practitioners, policymakers, providing roadmap fostering resiliently designed, technologically advanced, environmentally conscious environments.

Language: Английский

Citations

38
Artificial intelligence and sustainable development during urbanization: Perspectives on AI R&D innovation, AI infrastructure, and AI market advantage DOI Open Access

Qiang Wang,

Fuyu Zhang,

Rongrong Li

et al.

Sustainable Development, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 27, 2024

Abstract This study explores the impact of artificial intelligence (AI) on sustainable development across 51 countries during urbanization. Using panel data, examines AI's effects through three dimensions: R&D innovation, infrastructure, and market advantage. The results demonstrate that AI promotes development, with innovation exerting strongest influence, followed by whereas advantage has smallest impact. Additionally, uncovers regional heterogeneity in impacts. In upper middle levels (60%–70% quantiles), promoting effect is strongest. Moreover, urbanization plays a threshold role relationship between development. When below threshold, infrastructure promote inhibit it. Conversely, when exceeds this inhibits becomes insignificant, begin to recommends governments should consider level crafting policies utilizing AI.

Language: Английский

Citations

29
Artificial intelligence and the local government: A five-decade scientometric analysis on the evolution, state-of-the-art, and emerging trends DOI Creative Commons
Tan Yiğitcanlar,

Sajani Senadheera,

Raveena Marasinghe

et al.

Cities, Journal Year: 2024, Volume and Issue: 152, P. 105151 - 105151

Published: June 8, 2024

Language: Английский

Citations

14
Exploring Deep Computational Intelligence Approaches for Enhanced Predictive Modeling in Big Data Environments DOI Open Access
Madduri Venkateswarlu,

K. Thilagam,

R. Pushpavalli

et al.

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2024, Volume and Issue: 10(4)

Published: Nov. 25, 2024

The rapid growth of big data has created a pressing need for advanced predictive modeling techniques that can efficiently extract meaningful insights from massive, complex datasets. This study explores deep computational intelligence approaches to enhance in environments, focusing on the integration learning, swarm intelligence, and hybrid optimization techniques. proposed framework employs Deep Neural Network (DNN) enhanced with Particle Swarm Optimization (PSO) Adaptive Gradient Descent (AGD) dynamic parameter tuning, leading improved learning efficiency accuracy. is evaluated real-world applications, including healthcare diagnostics, financial risk prediction, energy consumption forecasting. Experimental results demonstrate significant improvement model performance, an accuracy 97.8% precision 95.2% mean absolute percentage error (MAPE) 3.4% Additionally, approach achieves 35% reduction overhead compared traditional DNNs 28% convergence speed due optimization. work highlights potential integrating analytics achieve robust, scalable, efficient modeling. Future research will focus extending accommodate real-time streams exploring its applicability across other domains.

Language: Английский

Citations

13
Unlocking Artificial Intelligence Adoption in Local Governments: Best Practice Lessons from Real-World Implementations DOI Creative Commons
Tan Yiğitcanlar, Anne David, Wenda Li

et al.

Smart Cities, Journal Year: 2024, Volume and Issue: 7(4), P. 1576 - 1625

Published: June 28, 2024

In an era marked by rapid technological progress, the pivotal role of Artificial Intelligence (AI) is increasingly evident across various sectors, including local governments. These governmental bodies are progressively leveraging AI technologies to enhance service delivery their communities, ranging from simple task automation more complex engineering endeavours. As governments adopt AI, it imperative understand functions, implications, and consequences these advanced technologies. Despite growing importance this domain, a significant gap persists within scholarly discourse. This study aims bridge void exploring applications context government provision. Through inquiry, seeks generate best practice lessons for smart city initiatives. By conducting comprehensive review grey literature, we analysed 262 real-world implementations 170 worldwide. The findings underscore several key points: (a) there has been consistent upward trajectory in adoption over last decade; (b) China, US, UK at forefront adoption; (c) among technologies, natural language processing robotic process emerge as most prevalent ones; (d) primarily deploy 28 distinct services; (e) information management, back-office work, transportation traffic management leading domains terms adoption. enriches existing body knowledge providing overview current sphere governance. It offers valuable insights policymakers decision-makers considering adoption, expansion, or refinement urban Additionally, highlights using guide successful integration optimisation future projects, ensuring they meet evolving needs communities.

Language: Английский

Citations

10
A Review of Multi-Source Data Fusion and Analysis Algorithms in Smart City Construction: Facilitating Real Estate Management and Urban Optimization DOI Creative Commons
Binglin Liu, Qian Li, Zhihua Zheng

et al.

Algorithms, Journal Year: 2025, Volume and Issue: 18(1), P. 30 - 30

Published: Jan. 8, 2025

In the context of booming construction smart cities, multi-source data fusion and analysis algorithms play a key role in optimizing real estate management improving urban efficiency. this review, we comprehensively systematically review relevant algorithms, covering types, characteristics, techniques, their synergies data. We found that data, including sensors, social media, citizen feedback, GIS face challenges such as quality privacy security when being fused. Data are diverse have own advantages disadvantages. help areas spatial deep learning. Algorithm collaboration can improve decision-making accuracy efficiency promote rational allocation resources. future, algorithm development will focus on quality, real-time, mining, interdisciplinary research, protection, collaborative application expansion, providing strong support for sustainable cities.

Language: Английский

Citations

1
Combining deep learning and machine learning techniques to track air pollution in relation to vegetation cover utilizing remotely sensed data DOI
Mashoukur Rahaman, Jane Southworth,

Amobichukwu Chukwudi Amanambu

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 376, P. 124323 - 124323

Published: Feb. 5, 2025

Language: Английский

Citations

1
Economic, Policy, Social, and Regulatory Aspects of AI-Driven Smart Buildings DOI

M. Arun,

Debabrata Barik,

Sreejesh S.R. Chandran

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 111666 - 111666

Published: Dec. 1, 2024

Language: Английский

Citations

5
Unlocking the power of machine learning in big data: a scoping survey DOI Creative Commons
Fadil Mohammed Surur,

Abiy Abinet Mamo,

Bealu Girma Gebresilassie

et al.

Data Science and Management, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 1, 2025

Language: Английский

Citations

0
Application Status, Hotspots, and Future Trends of Artificial Intelligence in the Field of Sustainable Environmental Governance DOI Creative Commons
Xuemei Jiang, Tingyin Deng, Wenying Zhang

et al.

Published: March 6, 2025

Amidst the increasingly severe global environmental crisis, application of artificial intelligence (AI) in fields governance and sustainable development has become a hot topic current scientific research practice. The complexity urgency issues have made integration AI technology particularly important pressing. To comprehensively understand status, hotspots, future trends this field, study employed Citespace VOSviewer literature analysis tools to construct knowledge map based on data from 2004 2024. results reveal that, terms regions, Asia (especially China) most significant contributions, while North America Europe (particularly United States some EU countries) closely collaborated, forming core regions. top five authors publication volume are Liu J, Vinuesa R, Nishant Bag S, Benzidia S. Regarding themes field focus four clusters: intelligent management green innovation for performance lifecycle assessment, smart cities development, AI-enabled management. These highlight vast potential enhancing efficiency promoting development. As trends, number publications shown continuous upward trend recent years, with predictions indicating that will continue concentrate keywords such as AI, life cycle, Internet Things. In summary, is an active expanding within governance, deepen understanding topic, explore science, address challenges, drive towards smart, efficient, direction.

Language: Английский

Citations

0