A Novel AI IoT-Based Approach for Autonomous Waste Segregation and Management in Sustainable Smart Urban Environments
Keywords:
AI-based waste management, Internet of Things (IoT), Smart waste segregation, Overflowing bin detection, Real-Time waste monitoring, Urban waste managementAbstract
Rapid urbanization has resulted in a considerable rise in municipal solid waste, creating significant environmental and public health issues in contemporary cities. Conventional waste management systems frequently prove to be inefficient, labor-intensive, and deficient in real-time monitoring capabilities, leading to improper waste segregation and resource wastage. To overcome these challenges, this paper introduces an innovative AI-IoT (Artificial Intelligence of Things) strategy for autonomous waste segregation and management in sustainable smart urban settings. The suggested system combines Internet of Things (IoT) sensors with Artificial Intelligence (AI) algorithms to facilitate real-time detection, classification, and sorting of waste into categories such as biodegradable, recyclable, and hazardous materials. Sophisticated machine learning models, including computer vision techniques, are utilized to accurately identify different types of waste, while embedded sensors keep track of bin levels, environmental conditions, and collection schedules. Furthermore, the system integrates cloud-based data analytics to optimize waste collection routes, lower operational costs, and improve decision-making for urban authorities. Additionally, the framework fosters sustainability by enhancing recycling efficiency, decreasing reliance on landfills, and minimizing environmental pollution. The autonomous characteristics of the system lessen the need for human intervention, ensuring safer and more hygienic waste handling processes. Experimental analyses and simulations indicate that the proposed AIoT-based solution markedly enhances segregation accuracy, collection efficiency, and overall waste management performance when compared to traditional methods. This research aids in the advancement of intelligent, scalable, and eco-friendly waste management systems, aligning with the vision of sustainable smart cities.
References
S. M. Maturi, S. R. Dhanikonda, and S. Giddaluru, “Smart Dustbins: Real-Time Monitoring and Optimization for Waste Management in Smart Cities through IoT Devices,” Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19246–19252, Feb. 2025.
A. Alourani, M. U. Ashraf, and M. Aloraini, “Smart waste management and classification system using advanced IoT and AI technologies,” PeerJ Computer Science, vol. 11, Art. no. e2777, Apr. 2025.
S. Patel, H. Oza, Y. Sharma, T. Patel, S. Agrawal, and P. Oza, “IoT-Enabled Smart Waste Management using Renewable Energy,” Procedia Computer Science, vol. 259, pp. 1973–1982, Jan. 2025.
R. Alnanih, L. Elrefaei, and A. Al-Ahwal, “Advancing Sustainability Through an IoT-Driven Smart Waste Management System with Software Engineering Integration,” Sustainability, vol. 17, no. 21, Art. no. 9803, Nov. 2025.
M. W. Rahman, R. Islam, A. Hasan, N. I. Bithi, M. M. Hasan, and M. M. Rahman, “Intelligent waste management system using deep learning with IoT,” Journal of King Saud University – Computer and Information Sciences, vol. 34, no. 5, pp. 2072–2087, 2022.
A. Henaien, H. Ben Elhadj, and L. Chaari Fourati, “A sustainable smart IoT-based solid waste management system,” Future Generation Computer Systems, vol. 157, pp. 587–602, 2024.
C. Wang, J. Qin, C. Qu, X. Ran, C. Liu, and B. Chen, “A smart municipal waste management system based on deep-learning and internet of things,” Waste Management, vol. 135, pp. 20–29, 2021.
I. Hussain, A. Elomri, L. Kerbache, and A. E. Omri, “Smart city solutions: Comparative analysis of waste management models in IoT-enabled environments using multiagent simulation,” Sustainable Cities and Society, vol. 103, Art. no. 105247, 2024.
M. Arebey, M. A. Hannan, H. Basri, and H. Abdullah, “Solid waste monitoring and management using RFID, GIS and GSM,” IEEE Student Conf. Research and Development (SCORED), pp. 37–40, 2009.
H. L. Yadav, T. Elango, L. Banakar, S. N. A. Chakravarthy, K. Vijayakanthan, and N. Sudha, “Smart waste management systems: IoT and AI approaches to sustainable urban sanitation,” International Journal of Environmental Sciences, pp. 224–234, 2025.
S. Dubey, P. Singh, P. Yadav, and K. K. Singh, “Household waste management system using IoT and machine learning,” Procedia Computer Science, vol. 167, pp. 1950–1959, 2020.
M. M. Ahmed, E. Hassanien, and A. E. Hassanien, “IoT-based intelligent waste management system,” Neural Computing and Applications, vol. 35, no. 32, pp. 23551–23579, 2023.
S. R. Krishna, B. Kranthikumar, S. Vodithala, M. S. Waseem, and K. Muppavaram, “A machine learning framework for predictive waste management optimization in smart cities,” International Journal of Environmental Sciences, vol. 11, no: 12s, pp. 328–334, 2025.
M. Sayeekumar, S. P. Dhanasri, A. Krishna Priya, and S. Devadharshini, “Machine learning based waste sorting for a sustainable environment,” International Journal of Advanced Research in Science, Communication and Technology, vol. 5, no. 4, pp. 80–86, 2025.
M. Bhuvaneswari, K. Tansin, S. T. Ahamed, N. T. Sri Ram, and S. V. Prasath, “Internet of things based intelligent waste segregation and management system for smart home application,” In Proceedings of the 7th International Conference on Communication and Electronics Systems, 2022, pp. 1737–1743.
