Optimization of Waste Collection in Smart Cities (Case Study: Tehran City)
Subject Areas : Urban and Regional Planning Studies
Ali Asghari
1
,
Hosssein Azgomi
2
,
Aliabbass Zoraghchian
3
1 -
2 - Department of Engineering, Faculty of Computer Engineering, Islamic Azad University, Rasht, Iran
3 - Department of Engineering, Faculty of Computer Engineering, Sanati Ghaem Institute of Higher Education, Ghaemshahr, Iran
Keywords: Smart city, waste collection management, Internet of Things (IoT), environmental sensors, Coral Reef Optimization (CRO) algorithm,
Abstract :
|
Introduction: Waste accumulation in metropolises such as Tehran has challenged urban management, as traditional waste collection methods are inefficient in densely populated areas. Smart cities improve the quality of urban life by providing technological solutions, and smart waste collection systems reduce service times, pollution, and costs. Objective: This study focuses on garbage collection in Tehran, seeking to increase sustainability and efficiency using the Internet of Things and optimization algorithms. Given the global increase in garbage expected to reach 3.4 billion tons by 2050, this study targets scheduling inefficiencies, energy consumption, pollution, and costs with an intelligent model. Research Methodology: This applied study optimizes garbage collection in Tehran with the Coral Reef Optimization Algorithm. The multi-stage approach of the proposed algorithm not only controls the filling level of the bins and traffic but also considers the type of garbage to perform more appropriate separation. In the proposed model, sensors attached to the garbage bins collect information related to data, such as the filling level of the bin and the type of garbage, and the proposed algorithm optimizes the routes and schedules. The efficiency of the proposed method is measured in comparison with genetic algorithms and ant colony optimization. The experiment results show the efficiency of the proposed method over compared algorithms in all objectives. Findings: The proposed intelligent algorithm using sensors and in the IoT platform reduces costs, distance traveled, emissions, and emptying time compared to traditional methods in Tehran. This method provides a scalable IoT-based solution for waste management, traffic, and high-volume waste. Conclusion: The IoT platform and related sensors combined with the coral reef algorithm improve waste management by optimizing routes and reducing the costs and time of collecting the whole city's waste. Successive iterations provide an efficient model that outperforms classical methods in all objectives. |
Al Nuaimi, E., & Al Neyadi, H. (2022). Smart city solutions: Comparative analysis of waste management models in IoT-enabled environments using multiagent simulation. Sustainable Cities and Society, 85, 104089. https://doi.org/10.1016/j.scs.2022.104089.
Awan, M. U., & Khan, M. A. (2023). Waste management 2.0 leveraging Internet of Things for an efficient and eco-friendly smart city solution. PLOS ONE, 18(5), e0285678. https://doi.org/10.1371/journal.pone.0285678
Chen, Y., & Li, X. (2023). Artificial intelligence for waste management in smart cities: A review. Environmental Chemistry Letters, 21(3), 1235–1258. https://doi.org/10.1007/s10311-023-01567-8
Damghani, A. M., Savarypour, G., Zand, E., & Deihimfard, R. (2008). Municipal solid waste management in Tehran: Current practices, opportunities and challenges. Waste Management, 28(5), 929–934. https://doi.org/10.1016/j.wasman.2007.06.010
Esmaeilian, B., & Sarkis, J. (2022). The future of waste management in smart and sustainable cities: A review and concept paper. Waste Management, 133, 45–60. https://doi.org/10.1016/j.wasman.2022.07.015
Hannan, M. A., & Arebey, M. (2022). Challenges, recent development, and opportunities of smart waste collection: A review. Renewable and Sustainable Energy Reviews, 157, 112031. https://doi.org/10.1016/j.rser.2021.112031
Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F. (2018). What a waste 2.0: A global snapshot of solid waste management to 2050. World Bank Publications. https://doi.org/10.1596/978-1-4648-1329-0
Kurniawan, T. A., & Othman, M. (2024). Unlocking synergies between waste management and climate change mitigation through circular-economy digitalization. Sustainable Production and Consumption, 46, 522–535. https://doi.org/10.1016/j.spc.2024.01.012
Moirogiorgou, K., & Zervakis, M. (2022). Intelligent robotic system for urban waste recycling. IEEE Transactions on Automation Science and Engineering, 19(3), 1–6. https://doi.org/10.1109/TASE.2022.3156789
Montecinos, J., & Orellana, R. (2021). Forecasting multiple waste collecting sites for the agro-food industry. Journal of Cleaner Production, 187, 932–941. https://doi.org/10.1016/j.jclepro.2021.03.045
Nowakowski, P., & Król, A. (2023). Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection. Waste Management, 141, 377–392. https://doi.org/10.1016/j.wasman.2023.02.013
Nirde, K., & Mulay, P. (2021). IoT based solid waste management system for smart city. Procedia Computer Science, 187, 666–669. https://doi.org/10.1016/j.procs.2021.04.112
Norouzian-Maleki, S., Mohammadi, M., & Karimi, A. (2023). Urban waste management in developing cities: Challenges and opportunities in Tehran. Journal of Urban Management, 12(2), 145–158. https://doi.org/10.1016/j.jum.2023.01.003
Rahman, M. M., & Islam, M. S. (2022). Intelligent waste management system using deep learning with IoT. Journal of King Saud University - Computer and Information Sciences, 34(5), 2072–2085. https://doi.org/10.1016/j.jksuci.2020.08.016
Rupani, P. F., Delarestaghi, R. M., Abbaspour, M., & Rupani, M. M. (2019). A review of waste management in Iran: Current practices and future directions. International Journal of Environmental Research and Public Health, 16(6), 979. https://doi.org/10.3390/ijerph16060979
Shukla, S., & Hait, S. (2022). Smart waste management practices in smart cities: Current trends and
