Smart city implementation based on Internet of Things integrated with optimization technology

Kolhe, Rushikesh Vilas; William, P.; Yawalkar, Prashant Madhukar; Paithankar, Deepak Narayan; Pabale, Abhijeet Rajendra

doi:10.1016/j.measen.2023.100789

Cited by 34 publications

(6 citation statements)

References 12 publications

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“…Technological innovations have revolutionized interactions in urban spaces and are providing new methods of technological integration in smart cities. Also, information and communications technology is used to improve the responsiveness, interactivity, and effectiveness of urban management services and monitoring [58]. The implementation of new technologies and appropriate management tools contributes to increasing the quality of service provision in urban spaces and socioeconomic development in cities.…”

Section: Ranking Of Thematic Areas In Smart Citiesmentioning

confidence: 99%

Smart City Actions Integrated into Urban Planning: Management of Urban Environments by Thematic Areas

Jacques,

Júnior,

de Paris

et al. 2024

Applied Sciences

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Over the years, the imbalance between population needs and urban spaces’ organized development has been accentuated by increasing urbanization. The implementation of smart city actions began in the 1990s, with the development of integrated solutions in cities, harmonizing social, environmental, and economic aspects. This research measures the impact of thematic areas on smart cities’ management performance. The multi-criteria Decision Making Trial and Evaluation Laboratory (DEMATEL) method was used to identify the interdependent relations between smart cities’ thematic areas, structuring a diagram of cause-and-effect relations using threshold quantification values. Considering their degree of importance in smart city management, the thematic areas of technology and innovation, living environment and infrastructure, education and training, and governance and engagement are highlighted. For the degree of influence, the most influential thematic areas are coexistence and reciprocity, living environment and infrastructure, entrepreneurship, and healthcare. Also, the cause-and-effect analysis identified governance and engagement, education and training, and mobility as central thematic areas for smart city management. Finally, the research construct was developed by ranking the thematic areas’ performance in urban space planning.

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Section: Ranking Of Thematic Areas In Smart Citiesmentioning

confidence: 99%

Smart City Actions Integrated into Urban Planning: Management of Urban Environments by Thematic Areas

Jacques,

Júnior,

de Paris

et al. 2024

Applied Sciences

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“…When the load is too great, new virtual PCs are dispatched to spread it out. If condition 13, as in the declining of network data transmission capacity 17 , the amount of VM may also do so 18 .…”

Section: 𝐷𝑁 ≥ 𝑆𝐿𝐴𝑜 13mentioning

confidence: 99%

Enhancing Smart Cities with IoT and Cloud Computing: A Study on Integrating Wireless Ad Hoc Networks for Efficient Communication

Abdulhadi,

Aldeen,

Yousif

et al. 2023

Baghdad Sci.J

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Smart cities have recently undergone a fundamental evolution that has greatly increased their potentials. In reality, recent advances in the Internet of Things (IoT) have created new opportunities by solving a number of critical issues that are allowing innovations for smart cities as well as the creation and computerization of cutting-edge services and applications for the many city partners. In order to further the development of smart cities toward compelling sharing and connection, this study will explore the information innovation in smart cities in light of the Internet of Things (IoT) and cloud computing (CC). IoT data is first collected in the context of smart cities. The data that is gathered is uniform. The Internet of Things, which enables gadgets to connect with one another mostly without human involvement, is made possible by AI. In line with this, The Ad Hoc Routing Function (ARF) AI computation is used for multi-rule simplification, the use of Adaptive Cloud Computing Virtual Machine Asset Allotment Technique (ACC-VMRA) is advised. To confirm its viability, the applied developments of IoT and CC in smart cities is examined and duplicated. The experiment results show that the recommended enhancement calculation is more productive than other currently used methods.

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“…Smart cities are indispensable for providing sustainable, efficient, and integrated urban environments that improve the quality of life for their residents while driving economic development 1 . The fundamental objective of smart cities is to optimize available resource utilization for the benefit of all stakeholders (e.g., residents, service providers, governments, and businesses) 2 – 4 . One such challenge is the integration of Autonomous Taxis (ATs) into smart cities, where they can play a crucial role in addressing the pressing issues of rapid population growth and urbanization.…”

Section: Introductionmentioning

confidence: 99%

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

Munawar,

Piantanakulchai

2024

Sci Rep

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The concept of Autonomous Taxis (ATs) has witnessed a remarkable surge in popularity in recent years, paving the way toward future smart cities. However, accurately forecasting passenger demand for ATs remains a significant challenge. Traditional approaches for passenger demand forecasting often rely on centralized data collection and analysis, which can raise privacy concerns and incur high communication costs. To address these challenges, We propose a collaborative model using Federated Learning (FL) for passenger demand forecasting in smart city transportation systems. Our proposed approach enables ATs in different regions of the smart city to collaboratively learn and improve their demand forecasting models through FL while preserving the privacy of passenger data. We use several backpropagation neural networks as local models for collaborating to train the global model without directly sharing their data. The local model shares only the model updates with a global model that aggregates them, which is then sent back to local models to improve them. Our collaborative approach reduces privacy concerns and communication costs by facilitating learning from each other’s data without direct data sharing. We evaluate our approach using a real-world dataset of over 4500 taxis in Bangkok, Thailand. By utilizing MATLAB2022b, the proposed approach is compared with popular baseline methods and existing research on taxi demand forecasting systems. Results demonstrate that our proposed approach outperforms in passenger demand forecasting, surpassing existing methods in terms of model accuracy, privacy preservation, and performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared ($$R^2$$ R 2 ). Furthermore, our approach exhibits improved performance over time through the collaborative learning process as more data becomes available.

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Smart city implementation based on Internet of Things integrated with optimization technology

Cited by 34 publications

References 12 publications

Smart City Actions Integrated into Urban Planning: Management of Urban Environments by Thematic Areas

Smart City Actions Integrated into Urban Planning: Management of Urban Environments by Thematic Areas

Enhancing Smart Cities with IoT and Cloud Computing: A Study on Integrating Wireless Ad Hoc Networks for Efficient Communication

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

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