Abstract
Along with the development of the modern technologies IoT plays a major role in these days. The implementation of the new things based on the requirement for the need of the society is very easy to design based on the IoT and machine learning techniques. Now a days waste management is appeared as a major issue. Waste management is a daily task in the areas either villages, towns, cities we should maintain that with the large no of labours government should pay huge amount to those workers with or without doing any work. So to avoid all these issues we are proposing a new method called optimal waste management by using IoT and machine learning techniques. By using this technology we will save our time, and no need of large no of workers for the social aspects. To optimize waste management several approaches have been proposed, such as colony optimization, the nearest neighbour search, genetic algorithm and particle swarm optimization techniques. However, the results are still too hazy to be useful in real-world situations, such as universities or cities. Combining effective waste management tactics with low-cost IoT technologies has been popular recently. So to avoid all these issues we are proposing a new method called optimal waste management by using IoT and machine learning techniques. A novel method by forecasting the likelihood of the garbage level in trash bins, waste management is achieved quickly and effectively is proposed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Silva, B. N., Khan, M., & Han, K. (2018). Towards sustainable smart cities: A review of trends, architectures, components, and open challenges in smart cities. Sustainable Cities and Society, 38, 697–713.
Shaik, F., Giri Prasad, M. N., Rao, J., Abdul Rahim, B., & Soma Sekhar, A. (2010). Medical image analysis of electron micrographs in diabetic patients using contrast enhancement. In 2010 International Conference on Mechanical and Electrical Technology, Singapore (pp. 482–485). https://doi.org/10.1109/ICMET.2010.5598408.
Davanam, G., Kallam, S., Singh, N., Gunjan, V. K., Roy, S., Rahebi, J., Farzamnia, A., & Saad, I. (2022). Multi-Controller model for improving the performance of IoT networks. Energies, 15, 8738. https://doi.org/10.3390/en15228738
Vinagre, E., De Paz, F., Pinto, T., Vale, Z., Corchado, M., & Garcia, O. (2016). Intelligent energy forecasting based on the correlation between solar radiation and consumption patterns. In Proceedings of the 2016 IEEE Symposium Series on Computational Intelligence (SSCI). Athens, Greece: IEEE, December 2016.
Siddiquee, K. N.-e-A., Islam, M. S., Singh, N., Gunjan, V. K., Yong, W. H., Huda, M. N., & Bhupal Naik, D. S. (2022). Development of algorithms for an IoT-Based smart agriculture monitoring system. Wireless Communications and Mobile Computing, 2022, Article ID 7372053, 16 p. https://doi.org/10.1155/2022/7372053.
Gunjan, V. K., Shaik, F., & Kashyap, A. (2021). Detection and analysis of pulmonary TB using bounding box and K-means algorithm. In A. Kumar & S. Mozar (Eds.), ICCCE 2020. Lecture Notes in Electrical Engineering (Vol. 698). Singapore: Springer. https://doi.org/10.1007/978-981-15-7961-5_142.
Sukjaimuk, R., Nguyen, Q. N., & Sato, T. (2018). A smart congestion control mechanism for the green IoT sensor-enabled information-centric networking. Sensors, 18(9), 2889.
Yellamma, Pachipala, P. G. Sandeep, R. Revanth Sai, S. Rohith Reddy, and D. Mahesh. “Automatic Vehicle Alert and Accident Detection System Based on Cloud Using IoT.“ In Embracing Machines and Humanity Through Cognitive Computing and IoT, pp. 77–85. Singapore: Springer Nature Singapore, 2023.
Ahmed, M., Ansari, M. D., Singh, N., Gunjan, V. K., BV, S. K., & Khan, M. (2022). Rating-based recommender system based on textual reviews using iot smart devices. Mobile Information Systems, 2022.
Pardhasaradhi, P., Pavan Kumar, K. V. K. V. L., Yathiraju, R., Sumanth, Y. S. S., Nishith, S., & Reddy, T. V. V. (2023). Stuck-At Fault Detection in Ripple Carry Adders with FPGA. In Embracing Machines and Humanity Through Cognitive Computing and IoT (pp. 23–31). Singapore: Springer Nature Singapore.
Bharadwaj, A. S., Rego, R., & Chowdhury, A. (2016). IoT based solid waste management system: A conceptual approach with an architectural solution as a smart city application. In 2016 IEEE Annual India Conference (INDICON), Bangalore, India (pp. 1–6). https://doi.org/10.1109/INDICON.2016.7839147.
Karimullah, S., Vishnu Vardhan, D., & Basha, S. J. (2020). Floorplanning for placement of modules in VLSI physical design using harmony search technique. In ICDSMLA 2019, Lecture Notes in Electrical Engineering (Vol. 601). Springer Nature Singapore Pte Ltd.
Kavitha, A., et al. (2022). Security in IoT mesh networks based on trust similarity. IEEE Access, 10, 121712–121724. https://doi.org/10.1109/ACCESS.2022.3220678
Sai Surya Teja, T., Venkata Hari Prasad, G., Meghana, I., & Manikanta, T. (2023). Publishing temperature and humidity sensor data to ThingSpeak. In M. Usman & X. Z. Gao (Eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT. Advanced Technologies and Societal Change. Singapore: Springer. https://doi.org/10.1007/978-981-19-4522-9_1.
Saha, H. N., Auddy, S., Pal, S., Kumar, S., Pandey, S., Singh, R., Singh, S. K., Banerjee, S., Ghosh, D., & Saha, S. (2017). Waste management using the Internet of Things (IoT). In Proceedings of the 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON), Bangkok, Thailand, August 16–18, 2017 (pp. 359–363).
Siddaiah, N., Pardhasaradhi, P., Phanigopi, M., Vasanthi, Y., & Deepika, Y. (2023). Assembly Line Implementation for IOT Applications. In M. Usman & X. Z. Gao (Eds.), Embracing Machines and Humanity Through Cognitive Computing and IoT. Advanced Technologies and Societal Change. Singapore: Springer. https://doi.org/10.1007/978-981-19-4522-9_12.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Sivayamini, L., Venkatesh, C., Shaik, F. (2024). Design of Optimal Waste Management System Using IOT and Machine Learning Technique in Educational Institutions. In: Gunjan, V.K., Zurada, J.M., Singh, N. (eds) Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough. Studies in Computational Intelligence, vol 1117. Springer, Cham. https://doi.org/10.1007/978-3-031-43009-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-43009-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43008-4
Online ISBN: 978-3-031-43009-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)