Intelligent Waste Management Systems: A Review of IoT, Deep Learning, and Optimization Techniques for Sustainable E-Waste and Solid Waste Handling

Authors

  • Godfrey Perfectson Department of Computing, Wellspring University, Edo State, Nigeria
  • Oyedotun Samuel Abiodun Department of Computing, Wellspring University, Edo State, Nigeria
  • Onwuzo Chioma Julia Department of Computer Science, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria
  • Ejenarhome Otega Prosper Department of Computer Science, Delta State University, Delta State. Nigeria

DOI:

https://doi.org/10.56778/rjslr.v3i2.508

Abstract

This review addresses the urgent environmental and health issues posed by the rapid growth of electronic waste (e-waste) and municipal solid waste (MSW), highlighting the role of emerging technologies in crafting sustainable waste management solutions. It explores the integration of the Internet of Things (IoT), deep learning, and optimization algorithms in enhancing waste classification, recycling, and disposal. Key innovations include IoT-enabled smart bins for real-time monitoring, deep learning models like CNNs achieving up to 97% sorting accuracy, and optimization techniques that improve energy efficiency and scalability. The paper synthesizes findings from over 50 studies and emphasizes both technical advances and implementation challenges, such as data limitations, model interpretability, and the lack of robust policy frameworks. Future research directions include explainable AI, edge computing, and global standardization of e-waste regulations. The review is intended for researchers, developers, and policymakers working toward circular economy principles and sustainable smart city solutions.

 

References

Abina, A., Puc, U., & Zidanšek, A. (2022). Challenges and opportunities of terahertz technology in construction and demolition waste management. Journal of Environmental Management, 315, 115118. https://doi.org/10.1016/j.jenvman.2022.115118

Abu-Qdais, H., Shatnawi, N., & AL-Alamie, E. (2023). Intelligent solid waste classification system using combination of image processing and machine learning models. https://doi.org/10.21203/rs.3.rs-2573812/v1

Ádám, B., Göen, T., Scheepers, P. T. J., Adliene, D., Batinic, B., Budnik, L. T., Duca, R.-C., Ghosh, M., Giurgiu, D. I., Godderis, L., Goksel, O., Hansen, K. K., Kassomenos, P., Milic, N., Orru, H., Paschalidou, A., Petrovic, M., Puiso, J., Radonic, J., … Au, W. W. (2021). From inequitable to sustainable e-waste processing for reduction of impact on human health and the environment. Environmental Research, 194, 110728. https://doi.org/10.1016/j.envres.2021.110728

Ahmed, M. M., Hassanien, E., & Hassanien, A. E. (2023). IoT-based intelligent waste management system. Neural Computing and Applications, 35(32), 23551–23579. https://doi.org/10.1007/s00521-023-08970-7

Ahmed, S., Mubarak, S., Du, J. T., & Wibowo, S. (2022). Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning. International Journal of Environmental Research and Public Health, 19(24), 16798. https://doi.org/10.3390/ijerph192416798

Ali, T., Irfan, M., Alwadie, A. S., & Glowacz, A. (2020). IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System for Smart Cities. Arabian Journal for Science and Engineering, 45(12), 10185–10198. https://doi.org/10.1007/s13369-020-04637-w

Alqahtani, F., Al-Makhadmeh, Z., Tolba, A., & Said, W. (2020). Internet of things-based urban waste management system for smart cities using a Cuckoo Search Algorithm. Cluster Computing, 23(3), 1769–1780. https://doi.org/10.1007/s10586-020-03126-x

Arashpour, M. (2023). AI explainability framework for environmental management research. Journal of Environmental Management, 342, 118149. https://doi.org/10.1016/j.jenvman.2023.118149

Arun, V., Krishna Rao Patro, E., Anusuya Devi, V. S., Nagpal, A., Kumar Chandra, P., & Albawi, A. (2024). AI Based Prediction Algorithms for Enhancing the Waste Management System: A Comparative Analysis. E3S Web of Conferences, 552, 01052. https://doi.org/10.1051/e3sconf/202455201052

Ascher, S., Watson, I., & You, S. (2022). Machine learning methods for modelling the gasification and pyrolysis of biomass and waste. Renewable and Sustainable Energy Reviews, 155, 111902. https://doi.org/10.1016/j.rser.2021.111902

Ba Alawi, A. E., Saeed, A. Y. A., Almashhor, F., Al-Shathely, R., & Hassan, A. N. (2021). Solid Waste Classification Using Deep Learning Techniques. 2021 International Congress of Advanced Technology and Engineering (ICOTEN), 1–5. https://doi.org/10.1109/ICOTEN52080.2021.9493430

Bibri, S. E. (2020). The IoT and Big Data Analytics for Smart Sustainable Cities: Enabling Technologies and Practical Applications. In S. E. Bibri, Advances in the Leading Paradigms of Urbanism and their Amalgamation (pp. 191–226). Springer International Publishing. https://doi.org/10.1007/978-3-030-41746-8_8

Bircanoglu, C., Atay, M., Beser, F., Genc, O., & Kizrak, M. A. (2018). RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks. 2018 Innovations in Intelligent Systems and Applications (INISTA), 1–7. https://doi.org/10.1109/INISTA.2018.8466276

Chhabra, M., Sharan, B., Elbarachi, M., & Kumar, M. (2024). Intelligent waste classification approach based on improved multi-layered convolutional neural network. Multimedia Tools and Applications, 83(36), 84095–84120. https://doi.org/10.1007/s11042-024-18939-w

Fatima Abbas Maikano. (2024). Machine Learning Approaches For Cyber Bullying Detection In Hausa Language Social Media: A Comprehensive Review And Analysis. Fudma Journal of Sciences (FJS), 8(3), pp 344-348.

Hussain, A., Draz, U., Ali, T., Tariq, S., Irfan, M., Glowacz, A., Antonino Daviu, J. A., Yasin, S., & Rahman, S. (2020). Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach. Energies, 13(15), 3930. https://doi.org/10.3390/en13153930

Huynh, M.-H., Pham-Hoai, P.-T., Tran, A.-K., & Nguyen, T.-D. (2020). Automated Waste Sorting Using Convolutional Neural Network. 2020 7th NAFOSTED Conference on Information and Computer Science (NICS), 102–107. https://doi.org/10.1109/NICS51282.2020.9335897

James, G. G., P, O. G., G, C. E., A, M. N., F, E. W., & E, O. P. (2024). Optimizing Business Intelligence System Using Big Data and Machine Learning. Journal of Information Systems and Informatics, 6(2), 1215–1236. https://doi.org/10.51519/journalisi.v6i2.631

Jin, S., Yang, Z., Królczykg, G., Liu, X., Gardoni, P., & Li, Z. (2023). Garbage detection and classification using a new deep learning-based machine vision system as a tool for sustainable waste recycling. Waste Management, 162, 123–130. https://doi.org/10.1016/j.wasman.2023.02.014

Kang, K. D., Kang, H., Ilankoon, I. M. S. K., & Chong, C. Y. (2020). Electronic waste collection systems using Internet of Things (IoT): Household electronic waste management in Malaysia. Journal of Cleaner Production, 252, 119801. https://doi.org/10.1016/j.jclepro.2019.119801

Kaya, M., Ulutürk, S., Çeti̇N Kaya, Y., Altintaş, O., & Turan, B. (2023). Optimization of Several Deep CNN Models for Waste Classification. Sakarya University Journal of Computer and Information Sciences, 6(2), 91–104. https://doi.org/10.35377/saucis...1257100

Khatiwada, B., Jariyaboon, R., & Techato, K. (2023). E-waste management in Nepal: A case study overcoming challenges and opportunities. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 4, 100155. https://doi.org/10.1016/j.prime.2023.100155

Kumar Lilhore, U., Simaiya, S., Dalal, S., Radulescu, M., & Balsalobre-Lorente, D. (2024). Intelligent waste sorting for sustainable environment: A hybrid deep learning and transfer learning model. Gondwana Research, S1342937X24002077. https://doi.org/10.1016/j.gr.2024.07.014

Kunsen Lin, Youcai Zhao, Lina Wang, Wenjie Shi, Feifei Cui, & Tao Zhou. (2023). MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting. Frontiers of Environmental Science & Engineering, 17(77). https://doi.org/10.1007/s11783-023-1677-1

Lakhouit, A., Shaban, M., Alatawi, A., Abbas, S. Y. H., Asiri, E., Al Juhni, T., & Elsawy, M. (2023). Machine-learning approaches in geo-environmental engineering: Exploring smart solid waste management. Journal of Environmental Management, 330, 117174. https://doi.org/10.1016/j.jenvman.2022.117174

Lin, K., Zhou, T., Gao, X., Li, Z., Duan, H., Wu, H., Lu, G., & Zhao, Y. (2022). Deep convolutional neural networks for construction and demolition waste classification: VGGNet structures, cyclical learning rate, and knowledge transfer. Journal of Environmental Management, 318, 115501. https://doi.org/10.1016/j.jenvman.2022.115501

Lin, W. (2021). YOLO-Green: A Real-Time Classification and Object Detection Model Optimized for Waste Management. 2021 IEEE International Conference on Big Data (Big Data), 51–57. https://doi.org/10.1109/BigData52589.2021.9671821

Lu, W., Huo, W., Gulina, H., & Pan, C. (2022). Development of machine learning multi-city model for municipal solid waste generation prediction. Frontiers of Environmental Science & Engineering, 16(9), 119. https://doi.org/10.1007/s11783-022-1551-6

Munir, M. T., Li, B., & Naqvi, M. (2023). Revolutionizing municipal solid waste management (MSWM) with machine learning as a clean resource: Opportunities, challenges and solutions. Fuel, 348, 128548. https://doi.org/10.1016/j.fuel.2023.128548

Nowakowski, P., Szwarc, K., & Boryczka, U. (2020). Combining an artificial intelligence algorithm and a novel vehicle for sustainable e-waste collection. Science of The Total Environment, 730, 138726. https://doi.org/10.1016/j.scitotenv.2020.138726

Oise, G., & Konyeha, S. (2024). E-Waste Management Through Deep Learning: A Sequential Neural Network Approach. Fudma Journal Of Sciences, 8(3), 17–24. https://doi.org/10.33003/fjs-2024-0804-2579

Oise, G. P., & Konyeha, S. (2024a). Deep Learning System for E-Waste Management. The 3rd International Electronic Conference on Processes, 66. https://doi.org/10.3390/engproc2024067066

Oise, G. P., & Konyeha, S. (2024b). Harnessing Deep Learning for Sustainable E-Waste Management and Environmental Health Protection. https://doi.org/10.21203/rs.3.rs-4518352/v1

Oise Godfrey Perfectson. (2024). Environmental and Information Safety of End-of-Life Electronics. International Transactions on Electrical Engineering and Computer Science, 3(2), 86–91. https://doi.org/10.62760/iteecs.3.2.2024.91

Pinhal Luqueci Thomaz, I., Fernando Mahler, C., & Pereira Calôba, L. (2023). Artificial Intelligence (AI) applied to waste management: A contingency measure to fill out the lack of information resulting from restrictions on field sampling. Waste Management Bulletin, 1(3), 11–17. https://doi.org/10.1016/j.wmb.2023.06.002

Qi, C., Wu, M., Liu, H., Liang, Y., Liu, X., & Lin, Z. (2023). Machine learning exploration of the mobility and environmental assessment of toxic elements in mining-associated solid wastes. Journal of Cleaner Production, 401, 136771. https://doi.org/10.1016/j.jclepro.2023.136771

Ramya, P., Ramya, V., & Babu Rao, M. (2024). Optimized Deep Learning-Based E-Waste Management in IoT Application via Energy-Aware Routing. Cybernetics and Systems, 55(8), 2041–2070. https://doi.org/10.1080/01969722.2023.2175119

Ramya, P., V, R., & M, B. R. (2023). E-waste management using hybrid optimization-enabled deep learning in IoT-cloud platform. Advances in Engineering Software, 176, 103353. https://doi.org/10.1016/j.advengsoft.2022.103353

Sayed, G. I., Abd Elfattah, M., Darwish, A., & Hassanien, A. E. (2024). Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning. Environmental Science and Pollution Research, 31(21), 31492–31510. https://doi.org/10.1007/s11356-024-33233-w

Selvakanmani, S., Rajeswari, P., Krishna, B. V., & Manikandan, J. (2024). Optimizing E-waste management: Deep learning classifiers for effective planning. Journal of Cleaner Production, 443, 141021. https://doi.org/10.1016/j.jclepro.2024.141021

Sheng, T. J., Islam, M. S., Misran, N., Baharuddin, M. H., Arshad, H., Islam, Md. R., Chowdhury, M. E. H., Rmili, H., & Islam, M. T. (2020). An Internet of Things Based Smart Waste Management System Using LoRa and Tensorflow Deep Learning Model. IEEE Access, 8, 148793–148811. https://doi.org/10.1109/ACCESS.2020.3016255

Simaei, E., & Rahimifard, S. (2024). AI-based decision support system for enhancing end-of-life value recovery from e-wastes. International Journal of Sustainable Engineering, 17(1), 1–17. https://doi.org/10.1080/19397038.2024.2306293

Sirimewan, D., Bazli, M., Raman, S., Mohandes, S. R., Kineber, A. F., & Arashpour, M. (2024). Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild. Journal of Environmental Management, 351, 119908. https://doi.org/10.1016/j.jenvman.2023.119908

Tasnim, A., Chowdhury, R., Mim, S. J., Ng, K. T. W., & Adu-Darko, H. (2024). Influence of Canadian provincial stewardship model attributes on the cost effectiveness of e-waste management. Journal of Environmental Management, 358, 120945. https://doi.org/10.1016/j.jenvman.2024.120945

Wirtu, Y. D., & Tucho, G. T. (2022). E-waste: Growing environmental and health problems and its management alternatives in developing countries. Environmental Reviews, 30(4), 524–536. https://doi.org/10.1139/er-2021-0120

Wu, T.-W., Zhang, H., Peng, W., Lü, F., & He, P.-J. (2023). Applications of convolutional neural networks for intelligent waste identification and recycling: A review. Resources, Conservation and Recycling, 190, 106813. https://doi.org/10.1016/j.resconrec.2022.106813

Yazdani, M., Kabirifar, K., & Haghani, M. (2024). Optimising post-disaster waste collection by a deep learning-enhanced differential evolution approach. Engineering Applications of Artificial Intelligence, 132, 107932. https://doi.org/10.1016/j.engappai.2024.107932

Yu, D., Guo, J., Meng, J., & Sun, T. (2023). Biofuel production by hydro-thermal liquefaction of municipal solid waste: Process characterization and optimization. Chemosphere, 328, 138606. https://doi.org/10.1016/j.chemosphere.2023.138606

Zhang, H., Cao, H., Zhou, Y., Gu, C., & Li, D. (2023). Hybrid deep learning model for accurate classification of solid waste in the society. Urban Climate, 49, 101485. https://doi.org/10.1016/j.uclim.2023.101485

Zhang, Q., Zhang, X., Mu, X., Wang, Z., Tian, R., Wang, X., & Liu, X. (2021). Recyclable waste image recognition based on deep learning. Resources, Conservation and Recycling, 171, 105636. https://doi.org/10.1016/j.resconrec.2021.105636

Downloads

Published

2025-08-20

How to Cite

Oise, G., Oyedotun Samuel ABIODUN, Onwuzo Chioma JULIA, & Ejenarhome Otega Prosper. (2025). Intelligent Waste Management Systems: A Review of IoT, Deep Learning, and Optimization Techniques for Sustainable E-Waste and Solid Waste Handling. RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW, 3(2), 648–659. https://doi.org/10.56778/rjslr.v3i2.508

Issue

Vol. 3 No. 2 (2025): Radinka Journal of Science and Systematic Literature Review

Section

A Systematic Literature Review

License

Copyright (c) 2025 RADINKA JOURNAL OF SCIENCE AND SYSTEMATIC LITERATURE REVIEW

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.