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 Computer Engineering, Ra.C., Islamic Azad University, Rasht, Iran
3 - Department of Engineering, Faculty of Computer Engineering, Sanati Ghaem Institute of Higher Education, Ghaemshahr, Iran

Received: 2025-04-07 Accepted : 2025-05-23 Published : 2026-04-04

Keywords: Smart City, Waste Collection Management, Internet of Things (IoT), Environmental Sensors, Coral Reef Optimization (CRO) Algorithm,

Abstract :

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. 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. 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. 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. 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.

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