IoT-EnabledAI Solutions for Efficient Smart City Waste Management

Authors

https://doi.org/10.48314/apem.v1i1.20

Abstract

The rise in urbanization has resulted in considerable challenges regarding waste management in smart cities, including inefficient collection techniques, overfilled trash bins, and increasing operational expenses. Conventional waste management approaches lack the necessary intelligence and adaptability to handle the constantly changing dynamics of urban waste generation. This article outlines a comprehensive approach that leverages Artificial Intelligence (AI) and the Internet of Things (IoTs) to enhance waste management in intelligent urban environments. Merging AI-driven data analytics with IoTs-connected sensors and devices allows for real-time monitoring of waste levels and the development of predictive models for collection schedules, ultimately leading to more efficient and sustainable waste management practices. The suggested strategy employs a network of smart trash bins fitted with IoTs sensors to track elements like fill levels, weight, and temperature. The information gathered is transmitted via Low-Power Wide-Area Networks (LPWANs) to a cloud-based platform for immediate analysis. AI techniques, including machine learning models for predictive maintenance and pattern recognition, are utilized to optimize collection routes and schedules based on anticipated waste generation trends. Moreover, computer vision technologies are implemented to automate the sorting of waste and enhance recycling efforts. The introduction of the AI-driven IoTs system has considerably boosted the efficiency of waste management. Case studies indicate that the frequency of waste collection has been reduced by as much as 40%, operational costs have dropped by 30%, and recycling rates have improved by 25%. Additionally, the system has effectively minimized overflowing garbage bins, thereby enhancing public health and environmental responsibility. These findings underscore the potential of AI and IoTs technologies to transform waste management in smart cities, fostering greater adaptability, cost-effectiveness, and environmental sustainability. The method proposed serves as a foundation for future developments in smart city infrastructure and supports more sustainable urban expansion.

Keywords:

Artificial intelligence, Internet of things, Smart city, Waste management, Data analytics, Predictive maintenance, Environmental sustainability

References

  1. [1] Brancoli, P., Bolton, K., & Eriksson, M. (2020). Environmental impacts of waste management and valorisation pathways for surplus bread in Sweden. Waste management, 117, 136–145. https://doi.org/10.1016/j.wasman.2020.07.043
  2. [2] Likotiko, E. D., Nyambo, D., & Mwangoka, J. (2017). Multi-agent based IoT smart waste monitoring and collection architecture. ArXiv Preprint ArXiv:1711.03966. https://doi.org/10.48550/arXiv.1711.03966
  3. [3] Esmaeilian, B., Wang, B., Lewis, K., Duarte, F., Ratti, C., & Behdad, S. (2018). The future of waste management in smart and sustainable cities: A review and concept paper. Waste management, 81, 177–195. https://doi.org/10.1016/j.wasman.2018.09.047
  4. [4] Pardini, K., Rodrigues, J. J. P. C., Kozlov, S. A., Kumar, N., & Furtado, V. (2019). IoT-based solid waste management solutions: A survey. Journal of sensor and actuator networks, 8(1), 1–25. https://doi.org/10.3390/jsan8010005
  5. [5] Hong, I., Park, S., Lee, B., Lee, J., Jeong, D., & Park, S. (2014). IoT‐based smart garbage system for efficient food waste management. The scientific world journal, 2014(1), 646953. https://doi.org/10.1155/2014/646953
  6. [6] Bueno-Delgado, M. V., Romero-Gázquez, J. L., Jiménez, P., & Pavón-Mariño, P. (2019). Optimal path planning for selective waste collection in smart cities. Sensors, 19(9), 1973. https://doi.org/10.3390/s19091973
  7. [7] Glouche, Y., & Couderc, P. (2013). A smart waste management with self-describing objects. The second international conference on smart systems, devices and technologies (smart’13). IARIA .https://inria.hal.science/hal-00924270/
  8. [8] Oralhan, Z., Oralhan, B., & Yigit, Y. (2017). Smart city application: internet of things (IoT) technologies based smart waste collection using data mining approach and ant colony optimization. The international arab journal of information technology, 14(4), 423–427. https://iajit.org/PDF/ Vol 14, No. 4/14969.pdf
  9. [9] Popa, C. L., Carutasu, G., Cotet, C. E., Carutasu, N. L., & Dobrescu, T. (2017). Smart city platform development for an automated waste collection system. Sustainability, 9(11), 1–15. https://doi.org/10.3390/su9112064
  10. [10] Miezah, K., Obiri-Danso, K., Kádár, Z., Fei-Baffoe, B., & Mensah, M. Y. (2015). Municipal solid waste characterization and quantification as a measure towards effective waste management in Ghana. Waste management, 46(1), 15–27. https://doi.org/10.1016/j.wasman.2015.09.009
  11. [11] Gade, D. (2019). Introduction to smart cities and selected literature review. International journal of advance and innovative research, 6(2), 7–15. https://www.researchgate.net/publication/354969041
  12. [12] Pardini, K., Rodrigues, J. J. P. C., Diallo, O., Das, A. K., de Albuquerque, V. H. C., & Kozlov, S. A. (2020). A smart waste management solution geared towards citizens. Sensors, 20(8), 1–15. https://doi.org/10.3390/s20082380

Downloads

Published

2024-12-28

Issue

Vol. 1 No. 1 (2024): Annals of Process Engineering and Management

Section

Articles

How to Cite

IoT-EnabledAI Solutions for Efficient Smart City Waste Management. (2024). Annals of Process Engineering and Management, 1(1), 26-32. https://doi.org/10.48314/apem.v1i1.20