This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Anushka Jadhav,
Sneha Tirth,
- U.G Student, Department of Computer Engineering, Trinity College of Engineering and Research, Pune, Maharashtra, India
- Assistant Professor, Department of Computer Engineering, Trinity College of Engineering and Research, Pune, Maharashtra, India
Abstract
Municipal and institutional waste management is becoming increasingly data-driven as cities, campuses, industries and commercial facilities seek to reduce overflowing bins, unnecessary collection trips, poor segregation and avoidable disposal. This review presents a practical Internet of Things and artificial intelligence framework for smart waste management with direct relevance to electrical and electronics engineering as well as AI/ML. The proposed approach connects smart bins equipped with fill-level, weight, moisture, gas and optional camera sensors to embedded controllers and low-power wireless communication. Cloud or edge services receive the resulting telemetry, while AI models support waste recognition, fill-level prediction, priority scoring and collection-route planning. The system is intentionally presented at an accessible engineering level: AI is used to improve decisions rather than replace basic sensing, reliable communication, safe electrical design or human oversight. The paper explains a complete flow from waste disposal and sensing to automated segregation, dynamic collection, recycling and operational feedback. It also discusses practical choices such as ultrasonic versus weight sensing, edge versus cloud processing, LoRaWAN, NB-IoT and Wi-Fi connectivity, confidence-based waste classification, battery and solar power management, sensor maintenance, data quality and cybersecurity. Seven original diagrams and implementation- oriented tables are used to show the architecture and deployment sequence. The resulting framework can support student prototypes, campus pilots and smart-city demonstrations while remaining scalable to larger waste networks. It emphasizes modular design, measurable service improvements, maintainability and circular use of recovered materials rather than excessive algorithmic complexity.
Keywords: Internet of Things, artificial intelligence, machine learning, smart waste management, smart bin, waste segregation, route optimization, embedded systems, computer vision, smart city
References
1. Cook E, Ionkova K, Bhada-Tata P, Yadav S, van Woerden F. What a Waste 3.0: Global Snapshot of Solid Waste Management Toward Circularity until 2050. Urban Development Series. Washington (DC): World Bank; 2026. doi: 10.1596/978-1-4648-2309-1.
2. Sosunova I, Porras J. IoT-Enabled Smart Waste Management Systems for Smart Cities: A Systematic Review. IEEE Access. 2022;10:73326-73363. doi:10.1109/ACCESS.2022.3188308.
3. Arthur MP, Shoba S, Pandey A. A survey of smart dustbin systems using the IoT and deep learning. Artificial Intelligence Review. 2024;57:56. doi:10.1007/s10462-023-10646-6.
4. Olawade DB, Fapohunda O, Wada OZ, Usman SO, Ige AO, Ajisafe O, et al. Smart waste management: A paradigm shift enabled by artificial intelligence. Waste Management Bulletin. 2024;2(2):244-263. doi:10.1016/j.wmb.2024.05.001.
5. Henaien A, Ben Elhadj H, Chaari Fourati L. A sustainable smart IoT-based solid waste management system. Future Generation Computer Systems. 2024;157:587-602. doi:10.1016/j.future.2024.03.056.
6. Khan S, Ali B, Alharbi AAK, Alotaibi S, Alkhathami M. Efficient IoT-Assisted Waste Collection for Urban Smart Cities. Sensors. 2024;24(10):3167. doi:10.3390/s24103167.
7. Roy A, Manna A, Kim J, Moon I. IoT-based smart bin allocation and vehicle routing in solid waste management: A case study in South Korea. Computers & Industrial Engineering. 2022;171:108457. doi:10.1016/j.cie.2022.108457.
8. Ferrer J, Alba E. BIN-CT: Urban waste collection based on predicting the container fill level. Biosystems. 2019;186:103962. doi:10.1016/j.biosystems.2019.04.006.
9. Ramos TRP, de Morais CS, Barbosa-Povoa AP. The smart waste collection routing problem: Alternative operational management approaches. Expert Systems with Applications. 2018;103:146-158. doi:10.1016/j.eswa.2018.03.001.
10. Chhabra M, Sharan B, Elbarachi M, Kumar M, et al. Intelligent waste classification approach based on improved multi-layered convolutional neural network. Multimedia Tools and Applications. 2024;83:84095-84120. doi:10.1007/s11042-024-18939-w.
11. Wang J. Application research of image classification algorithm based on deep learning in household garbage sorting. Heliyon. 2024;10(9):e29966. doi:10.1016/j.heliyon.2024.e29966.
12. Islam MSB, Sumon MSI, Majid ME, Kashem SBA, Nashbat M, Ashraf A, et al. ECCDN-Net: A deep learning-based technique for efficient organic and recyclable waste classification. Waste Management. 2025;193:363-375. doi:10.1016/j.wasman.2024.12.023.
13. Nahiduzzaman M, Ahamed MF, Naznine M, Karim MJ, Kibria HB, Ayari MA, et al. An automated waste classification system using deep learning techniques: Toward efficient waste recycling and environmental sustainability. Knowledge-Based Systems. 2025;310:113028. doi:10.1016/j.knosys.2025.113028.
14. Zoumpoulis P, Konstantinidis FK, Tsimiklis G, Amditis A. Smart bins for enhanced resource recovery and sustainable urban waste practices in smart cities: A systematic literature review. Cities. 2024;152:105150. doi:10.1016/j.cities.2024.105150.
15. Zhang Q, Zhang X, Mu X, Wang Z, Tian R, Wang X, Liu X. Recyclable waste image recognition based on deep learning. Resources, Conservation and Recycling. 2021;171:105636. doi:10.1016/j.resconrec.2021.105636.
16. Adedeji O, Wang Z. Intelligent Waste Classification System Using Deep Learning Convolutional Neural Network. Procedia Manufacturing. 2019;35:607-612. doi:10.1016/j.promfg.2019.05.086.
17. Ahmed K, Dubey MK, Kumar A, Dubey S. Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review. Measurement: Sensors. 2024;36:101395. doi:10.1016/j.measen.2024.101395.
Journal of Microcontroller Engineering and Applications
| Volume | 13 | |
| 03 | ||
| Received | 10/10/2026 | |
| Accepted | 02/11/2026 | |
| Published | 03/11/2026 | |
| Publication Time | 24 Days |