A Flexible Waste Bin Number Allocation Plan Applied to Waste Transportation Electric Fleets in Smart Cities

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106223 - 106223

Опубликована: Фев. 1, 2025

Язык: Английский

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Can smart city development alleviate urban shrinkage in the traditional urban development process?

Cities, Год журнала: 2025, Номер 160, С. 105847 - 105847

Опубликована: Фев. 28, 2025

Язык: Английский

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Smart waste management and air pollution forecasting: Harnessing Internet of things and fully Elman neural network

Waste Management & Research The Journal for a Sustainable Circular Economy, Год журнала: 2025, Номер unknown

Опубликована: Март 20, 2025

As the Internet of things (IoT) continues to transform modern technologies, innovative applications in waste management and air pollution monitoring are becoming critical for sustainable development. In this manuscript, a novel smart (SWM) forecasting (APF) system is proposed by leveraging IoT sensors fully Elman neural network (FENN) model, termed as SWM–APF–IoT–FENN. The integrates real-time data from quality including weight, trash level, odour carbon monoxide (CO) that collected bins connected Google Cloud Server. Here, MaxAbsScaler employed normalization, ensuring consistent feature representation. Subsequently, atmospheric contaminants surrounding receptacles were observed using FENN model. This model utilized predict concentration CO categorize bin status filled, half-filled unfilled. Moreover, weight parameter tuned secretary bird optimization algorithm better prediction results. implementation methodology done Python tool, performance metrics analysed. Experimental results demonstrate significant improvements performance, achieving 15.65%, 18.45% 21.09% higher accuracy, 18.14%, 20.14% 24.01% F-Measure, 23.64%, 24.29% 29.34% False Acceptance Rate (FAR), 25.00%, 27.09% 31.74% precision, 20.64%, 22.45% 28.64% sensitivity, 26.04%, 28.65% 32.74% specificity, 9.45%, 7.38% 4.05% reduced computational time than conventional approaches such network, recurrent artificial long short-term memory with gated unit, respectively. Thus, method offers streamlined, efficient framework forecasting, addressing environmental challenges.

Язык: Английский

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IoT-Based Framework for Connected Municipal Public Services in a Strategic Digital City Context

IoT, Год журнала: 2025, Номер 6(2), С. 20 - 20

Опубликована: Март 25, 2025

The use of digital technology resources in public services enhances efficiency, responsiveness, and citizens’ quality life through improved resource management, real-time monitoring, service performance. objective is to create apply an IoT-based framework for connected municipal a strategic city context. research employed modeling process validated Brazilian city, identifying seven related frameworks four themes bibliometric review. original comprises three constructs, eight subconstructs, 12 variables, case study inquiry. results revealed that the researched has yet enlarge IoT into its as part project initiative. Key recommendations implementation include prioritizing preferences citizens, expanding critical suited IoT, updating strategies incorporate IT streamline decision-making. conclusion reiterates effective when actionable information supports planning decision-making highlights transformative potential driving more resilient sustainable cities aligned with needs. This approach allows managers enhance while improving efficiency responsiveness urban management processes services.

Язык: Английский

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Security and Challenges of Blockchain-Based IoT Use Cases

Lecture notes in networks and systems, Год журнала: 2025, Номер unknown, С. 91 - 103

Опубликована: Янв. 1, 2025

Язык: Английский

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Efficient Solid Waste Management in a Smart City Environment Using IoRT Enabled Robots

Lecture notes on data engineering and communications technologies, Год журнала: 2025, Номер unknown, С. 225 - 235

Опубликована: Янв. 1, 2025

Язык: Английский

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Towards sustainable societies: Convolutional neural networks optimized by modified crayfish optimization algorithm aided by AdaBoost and XGBoost for waste classification tasks

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113086 - 113086

Опубликована: Апрель 1, 2025

Язык: Английский

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A bibliometric and content analysis of internet of things (IoT) in the public sector

Journal of Science and Technology Policy Management, Год журнала: 2025, Номер unknown

Опубликована: Апрель 10, 2025

Purpose This study outlines current research trends and patterns in the domain of Internet Things (IoT) within public sector. The purpose this is to guide new researchers with a clear roadmap for understanding IoT’s complexities opportunities domain. Design/methodology/approach conducts bibliometric content analysis 841 IoT articles indexed Web Science database, covering span over 14 years. analysis, using advanced R-software-based tool Biblioshiny, provides insights into key metrics such as popular keywords, publication productivity, leading journals prolific authors In addition, was carried out Atlas.ti software ascertain most prevalent applications, frequently studied areas dominant application technologies Findings reveals influence on sector innovation its increasing adoption. It identifies themes smart cities, artificial intelligence blockchain, alongside emerging like health care, urban development safety. stresses need robust security privacy measures sector’s expanding use. Originality/value offers seminal exploration sector, addressing notable gap business management literature. synthesizes years research, offering foundation future academic practical work. findings act guide, suggesting innovative directions that use transformative potential services.

Язык: Английский

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Municipal Solid Waste Management Using Machine Learning: A Case Study in Sheger City, Koye Sub-city, Ethiopia

European Journal of Theoretical and Applied Sciences, Год журнала: 2025, Номер 3(2), С. 511 - 525

Опубликована: Апрель 11, 2025

Municipal Solid Waste Management is an increasingly critical challenge in urban areas, intensified by rapid urbanization, population growth, and evolving consumption patterns. This study investigates the application of machine learning techniques to predict municipal solid waste generation Sheger City, Koye Sub-city, Ethiopia, using data from 2009 2023. Three models, ARIMA, RF, LSTM, were employed forecast trends for period 2024–2028, considering various socio-economic demographic factors. Among LSTM demonstrated highest accuracy, with MSE 1.62 × 10⁸ tonnes, MAE 9,500 R² 0.93. These results outperformed ARIMA (MSE = 3.84 tonnes², 15,200 0.85) RF 2.91 12,800 0.89). The forecasts 8.5% increase total generation, 3,852,150 tonnes 2023 4,177,500 2028. Notable growth expected high-volume streams, including food (13.5% increase) plastic (8.9% increase). findings highlight urgent need enhanced management strategies, expanded recycling programs policy interventions. provides a robust framework leveraging models guide decisions, contributing more sustainable practices rapidly growing cities.

Язык: Английский

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AI and Machine Learning for Optimizing Waste Management and Reducing Air Pollution

Journal of Hazardous Toxic and Radioactive Waste, Год журнала: 2025, Номер 29(3)

Опубликована: Апрель 21, 2025

Язык: Английский

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