Skip to main content
SpringerLink
Log in

Varıous Frameworks for IoT-Enabled Intellıgent Waste Management System Usıng ML for Smart Cıtıes

  • Conference paper
  • First Online:
Mobile Computing and Sustainable Informatics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 126))

Abstract

Urbanization reached severe dimensions as a result of rising population density and rural flight to cities, posing a massive waste generating problem in cities. Increased garbage creation has long been seen as a serious burden for large metropolitan centers across the world, and it is a vital issue for nations experiencing rapid urbanization. The Internet of Things (IoT) and machine learning (ML) offer an automotive possibility through cyber-bodily systems, which revolutionized solid waste management. This study conducts a comprehensive analysis of different framework for waste management models available in the existing works based on infrastructure of IoT for effective treatment of trash generated in municipal situations, taking into account IoT needs. The relationship between concessionaires and trash generators (citizens) is being studied in order to minimize collection time and costs while also promoting citizenship. The objective of this paper is to emphasize about the most important methods and categorize outstanding research challenges on the topic by describing an IoT-based reference model and analyzing the available solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kellow, P., Rodrigues, J.J.P.C., Kozlov, S.A., Kumar, N., Furtado, V.: IoT-based solid waste management solutions: a survey. J. Sens. Actuator Netw. 8(1), 5 (2019). https://doi.org/10.3390/jsan8010005

  2. Nižetić, S., Šolić, P., González-de-Artaza, D.L.I., Patrono, L.: Internet of Things (IoT): opportunities, issues and challenges towards a smart and sustainable future. J. Cleaner Prod. 274, 122877 (2020). ISSN 0959-6526. https://doi.org/10.1016/j.jclepro.2020.122877

  3. Sungheetha, A., Sharma, R.: Fuzzy Chaos Whale optimization and BAT integrated algorithm for parameter estimation in sewage treatment. J. Soft Comput. Paradigm (JSCP) 3(01), 10–18 (2021)

    Article  Google Scholar 

  4. Sungheetha, A., Sharma, R.: Real time monitoring and fire detection using Internet of Things and cloud based drones. J. Soft Comput. Paradigm (JSCP) 2(03), 168–174 (2020)

    Article  Google Scholar 

  5. Han, J., Song, W., Gozho, A., Sung, Y., Ji, S., Song, L., Wen, L., Zhang, Q.: LoRa-based smart IoT application for smart city: an example of human posture detection. Wireless Commun. Mobile Comput. Article ID 8822555, 15 pages (2020). https://doi.org/10.1155/2020/8822555

  6. Cuomo, F., Garlisi, D., Martino, A., Martino, A.: Predicting LoRaWAN behavior: how machine learning can help. Computers 9(3), 60 (2020). https://doi.org/10.3390/computers9030060

  7. Khalaf, O.I., Ogudo, K.A., Singh, M.: A fuzzy-based optimization technique for the energy and spectrum efficiencies trade-off in cognitive radio-enabled 5G network. Symmetry,13(1), 47 (2021)

    Google Scholar 

  8. Chen, T., Eager, D., Makaroff, D.: Efficient ımage transmission using LoRa technology ın agricultural monitoring IoT systems. In: 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 937–944 (2019). https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00166

  9. Akram, S.V., Singh, R., AlZain, M.A., Gehlot, A., Rashid, M., Faragallah, O.S., El-Shafai, W., Prashar, D.: Performance analysis of IoT and long-range radio-based sensor node and gateway architecture for solid waste management. Sensors 21(8), 2774 (2021). https://doi.org/10.3390/s21082774

  10. Bircanoğlu, C., Atay, M., Beşer, F., Genç, Ö., Kızrak, M.A.: RecycleNet: intelligent waste sorting using deep neural networks. Innov. Intell. Syst. Appl. (INISTA) 2018, 1–7 (2018). https://doi.org/10.1109/INISTA.2018.8466276

    Article  Google Scholar 

  11. Rokade, A., Singh, M.: Analysis of precise green house management system using machine learning based Internet of Things (IoT) for smart farming. In: 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC) (2021)

    Google Scholar 

  12. Bakhshi, T., Ahmed, M.: IoT-Enabled Smart City Waste Management using machine learning analytics. In: 2018 2nd International Conference on Energy Conservation and Efficiency (ICECE), pp. 66–71 (2018). https://doi.org/10.1109/ECE.2018.8554985

  13. Shamin, N., Fathimal, P.M., Raghavendran, R., Prakash, K.: Smart garbage segregation & management system using Internet of Things(IoT) & machine learning (ML). In: 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), pp. 1–6 (2019). https://doi.org/10.1109/ICIICT1.2019.8741443

  14. Dubey, S., Singh, M.K., Singh, P., Aggarwal, S.: Waste management of residential society using machine learning and IoT approach. In: 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 293–297 (2020). https://doi.org/10.1109/ESCI48226.2020.9167526

  15. Shaikh, F., Kazi, N., Khan, F., Thakur, Z.: Waste profiling and analysis using machine learning. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 488–492 (2020). https://doi.org/10.1109/ICIRCA48905.2020.9183035

  16. Rutqvist, D., Kleyko, D., Blomstedt, F.: An automated machine learning approach for smart waste management systems. IEEE Trans. Industr. Inf. 16(1), 384–392 (2020). https://doi.org/10.1109/TII.2019.2915572

    Article  Google Scholar 

  17. Sheng, T.J., et al.: An Internet of Things based smart waste management system using LoRa and Tensorflow deep learning model. IEEE Access 8, 148793–148811 (2020). https://doi.org/10.1109/ACCESS.2020.3016255

    Article  Google Scholar 

  18. Jadli, A., Hain, M.: Toward a deep smart waste management system based on pattern recognition and transfer learning. In: 2020 3rd International Conference on Advanced Communication Technologies and Networking (CommNet), pp. 1–5 (2020). https://doi.org/10.1109/CommNet49926.2020.9199615

  19. Zhao, Z.Q., Zheng, P., Xu, S.T., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learning Syst. 30(11), 3212–3232 (2019)

    Article  Google Scholar 

  20. Bharadwaj, S., Rego, R., Chowdhury, A.: IoT based solid waste management system: A conceptual approach with an architectural solution as a smart city application. In: Proceedings of 2016 IEEE Annual India Conference (INDICON), December, 2016. pp. 1–6

    Google Scholar 

  21. Vafeiadis, T., et al.: Data analytics platform for the optimization of waste management procedures. In: 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 333–338 (2019). https://doi.org/10.1109/DCOSS.2019.00074

  22. Azhaguramyaa, V.R., Janet, J., Lakshmi Narayanan, V.V., Sabari, R.S., Santhosh, K.K.: An ıntelligent system for waste materials segregation using IoT and deep learning. Journal of Physics: Conference Series, Volume 1916, 2021 International Conference on Computing, Communication, Electrical and Biomedical Systems (ICCCEBS) 2021 25–26 March 2021, Coimbatore, India

    Google Scholar 

  23. Jardosh, P.M., Shah, S.S., Bide, P.J.: SEGRO: key towards modern waste management. In: 2020 International Conference for Emerging Technology (INCET), pp. 1–5 (2020). https://doi.org/10.1109/INCET49848.2020.9154113

  24. Kambam, L.R., Aarthi, R.: Classification of plastic bottles based on visual and physical features for waste management. In: 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), pp. 1–6 (2019). https://doi.org/10.1109/ICECCT.2019.8869191

  25. Pamintuan, M., Mantiquilla, S.M., Reyes, H., Samonte, M.J.: i-BIN: an ıntelligent trash bin for automatic waste segregation and monitoring system. In: 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), pp. 1–5 (2019). https://doi.org/10.1109/HNICEM48295.2019.9072787

  26. Savla, D.V., Parab, A.N., Kekre, K.Y., Gala, J.P., Narvekar, M.: IoT and ML based smart system for efficient garbage monitoring: real time AQI monitoring and fire detection for dump yards and garbage management system. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 315–321 (2020). https://doi.org/10.1109/ICSSIT48917.2020.9214202

  27. Hulyalkar, K.S., Deshpande, R., Makode, K.: Implementation of Smartbin using convolutional neural networks. Int. Res. J. Eng. Technol. 5(4), 1–7 (2018)

    Google Scholar 

  28. Liang, S., Gu, Y.: A deep convolutional neural network to simultaneously localize and recognize waste types in images. Waste Manag. 126, 247–257 (2021)

    Google Scholar 

  29. Parilla, R.A.G., Leorna, O.J.C., Attos, R.D.P., Palconit, M.G.B., Obiso, J.J.A.: Low-Cost Garbage Level Monitoring System in Drainages Using Internet of Things in the Philippines, vol. 18, pp. 164–186 (2020)

    Google Scholar 

  30. Soliman, A., Zaher Akkad, M., Alloush, R.: Smart bın monıtorıng system for smart waste management. Multidiszciplináris tudományok, 10. Kötet, 402–412 (2020). https://doi.org/10.35925/j.multi.2020.2.45

  31. Khoa, T.A., Phuc, C.H., Lam, P.D., Nhu, L.M.B., Trong, N.M., Phuong, N.T.H., Van Dung, N., Nguyen Tan Y., Nguyen, H.N., Duc, D.N.M.: Waste management system using IoT-based machine learning in University. Wireless Commun. Mobile Comput. Article ID 6138637, 13 pages (2020). https://doi.org/10.1155/2020/6138637

  32. Graus, M., Niemietz, P., Rahman, M.T., Hiller, M., Pahlenkemper, M.: Machine learning approach to integrate waste management companies in micro grids. In: 2018 19th International Scientific Conference on Electric Power Engineering (EPE), pp. 1–6 (2018). https://doi.org/10.1109/EPE.2018.8396029

  33. Hussain, A., Draz, U., Ali, T., Tariq, S., Irfan, M., Glowacz, A., Antonino Daviu, J.A., Yasin, S., Rahman, S.: Waste management and prediction of air pollutants using IoT and machine learning approach. Energies 13(15), 3930 (2020). https://doi.org/10.3390/en13153930

  34. Ikram, B.A.Q., Abdelhakim, B.A., Abdelali, A, Zafar, B., Mohammed, B.: Deep Learning architecture for temperature forecasting in an IoT LoRa based system, pp. 1–6 (2019). https://doi.org/10.1145/3320326.3320375

  35. Nandhini, S., Mrinal, S.S., Balachandran, N., Suryanarayana, K., Ram, D.S.H.: Electronically assisted automatic waste segregation. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 846–850 (2019). https://doi.org/10.1109/ICOEI.2019.8862666

  36. Naveen Ananda Kumar, J., Chimmani, S.: Proposal of smart home resource management for waste reduction and sustainability using AI and ML. In: 2019 International Conference on Communication and Electronics Systems (ICCES), pp. 992–998 (2019). https://doi.org/10.1109/ICCES45898.2019.9002031

  37. Gomez, C.A., Shami, A., Wang, X.: Machine learning aided scheme for load balancing in dense IoT networks. Sensors 18(11), 3779 (2018). https://doi.org/10.3390/s18113779

  38. Suresh, V.M., Sidhu, R., Karkare, P., Patil, A., Lei, Z., Basu, A.: Powering the IoT through embedded machine learning and LoRa. In: 2018 IEEE 4th world forum on Internet of Things (WF-IoT), pp. 349–354 (2018). https://doi.org/10.1109/WF-IoT.2018.8355177

Download references

Author information

Author notes

  1. Karan S. Belsare

    Present address: School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India

Authors and Affiliations

  1. School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India

    Manwinder Singh

Authors
  1. Karan S. Belsare
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manwinder Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manwinder Singh .

Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, Nepal

    Subarna Shakya

  2. University of Applied Sciences, Athens, Greece

    Klimis Ntalianis

  3. Department of Computer Science, Texas Southern University, Houston, TX, USA

    Khaled A. Kamel

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Belsare, K.S., Singh, M. (2022). Varıous Frameworks for IoT-Enabled Intellıgent Waste Management System Usıng ML for Smart Cıtıes. In: Shakya, S., Ntalianis, K., Kamel, K.A. (eds) Mobile Computing and Sustainable Informatics. Lecture Notes on Data Engineering and Communications Technologies, vol 126. Springer, Singapore. https://doi.org/10.1007/978-981-19-2069-1_55

Download citation

Publish with us

Policies and ethics

Search

Navigation