Internet of things-based urban waste management system for smart cities using a Cuckoo Search Algorithm (original) (raw)

References

  1. Guillemin, P., Friess, P.: Internet of things strategic research roadmap. The Cluster of European Research Projects, Technical Report, September 2009. https://www.internet-of-things-research.eu. Accessed 23 Dec 2014
  2. Lingling, H., Haifeng, L., Xu, X., Jian, L.: An intelligent vehicle monitoring system, based on internet of things. In: IEEE 7th International Conference on Computational Intelligence and Security (CIS), Hainan, pp. 231–233 (2011)
  3. Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Sensing as a service model for smart cities supported by Internet of Things. Trans. Emerg. Telecommun. Technol. 25(1), 81–93 (2014)
    Article Google Scholar
  4. Sharma, N., Shamkuwar, M., Singh, I.: The history, present and future with IoT. In: Balas, V., Solanki, V., Kumar, R., Khari, M. (eds.) Internet of Things and Big Data Analytics for Smart Generation Intelligent Systems Reference Library, 154th edn. Springer, Cham (2019)
    Google Scholar
  5. Mulligan, C.E.A., Olsson, M.: Architectural implications of smart city business models: an evolutionary perspective. IEEE Commun. Mag. 51(6), 80–85 (2013)
    Article Google Scholar
  6. Saba, D., Sahli, Y., Berbaoui, B., Maouedj, R.: Towards smart cities: challenges, components, and architectures. In: Hassanien, A., Bhatnagar, R., Khalifa, N., Taha, M. (eds.) Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications Studies in Computational Intelligence, 846th edn. Springer, Cham (2020)
    Google Scholar
  7. Silva, B.N., Khan, M., Han, K.: Towards sustainable smart cities: a review of trends, architectures, components, and open challenges in smart cities. Sustain. Cities Soc. (2018). https://doi.org/10.1016/j.scs.2018.01.053
    Article Google Scholar
  8. https://www.gartner.com/en/newsroom/press-releases/2017-02-07-gartner-says-8-billion-connected-things-will-be-in-use-in-2017-up-31-percent-from-2016
  9. Gutierrez, J.M., Jensen, M., Henius, M., Riaz, T.: Smart waste collection system based on location intelligence. Procedia Comput. Sci. (2015). https://doi.org/10.1016/j.procs.2015.09.170
    Article Google Scholar
  10. Hannan, M.A., Arebey, M., Basri, H., Begum, R.A.: Intelligent solid waste bin monitoring and management system. Aust. J. Basic Appl. Sci. (2010). https://doi.org/10.1007/s10661-010-1642-x
    Article Google Scholar
  11. https://sensoneo.com/reference/nitra/
  12. https://www.ecubelabs.com/solar-powered-trash-compactor/
  13. Anagnostopoulos, T. V., Zaslavsky, A., Medvedev A.: Robust waste collection exploiting cost efficiency of IOT potentiality in smart cities. In: International Conference on Recent Advances in Internet of Things (ICRIOT), pp. 7–9 (2015)
  14. Kusum, L., Singh, S.S.K.: IOT based smart waste management system using Wireless Sensor Network and Embedded Linux Board. Int. J. Curr. Trends Eng. Res. (IJCTER) 2(7), 210–214 (2016)
    Google Scholar
  15. https://archive.epa.gov/epawaste/nonhaz/municipal/web/pdf/msw07-fs.pdf
  16. Medvedev, A., Fedchenkov, P., Zaslavsky, A., Anagnostopoulos, T., Khoruzhnikov, S.: Waste management as an IoT-enabled service in smart cities. In: Balandin, S., Andreev, S., Koucheryavy, Y. (eds.) Internet of Things, Smart Spaces, and Next Generation Networks and Systems ruSMART 2015, NEW2AN 2015 Lecture Notes in Computer Science, 9247th edn. Springer, Cham (2015)
    Google Scholar
  17. Bakhshi, T., Ahmed, M.: IoT-enabled smart city waste management using machine learning analytics. In: 2nd International Conference on Energy Conservation and Efficiency (ICECE) (2018)
  18. Abdullah, N., Alwesabi, O.A., Abdullah, R.: IoT-based smart waste management system in a smart city. In: Saeed, F. et al. (Eds.) IRICT 2018, AISC 843, pp. 364–371. Springer, New York (2019). https://doi.org/10.1007/978-3-319-99007-1_35
  19. Gupta, P.K., Shree, V., Hiremath, L., Rajendran, S.: The use of modern technology in smart waste management and recycling: artificial intelligence and machine learning. In: Kumar, R., Wiil, U. (eds.) Recent Advances in Computational Intelligence Studies in Computational Intelligence, 823rd edn. Springer, Cham (2019)
    Google Scholar
  20. Idwan, S., Mahmood, I., Zubairi, J.A., et al.: Optimal management of solid waste in smart cities using internet of things. Wirel. Pers. Commun. 110, 485–501 (2020). https://doi.org/10.1007/s11277-019-06738-8
    Article Google Scholar
  21. Ruiz V., Sánchez Á., Vélez J.F., Raducanu B.: Automatic image-based waste classification. In: Ferrández Vicente J., Álvarez-Sánchez J., de la Paz López F., Toledo Moreo J., Adeli H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science, vol. 11487. Springer, Cham (2019)
  22. Adedeji, O., Wang, Z.: Intelligent waste classification system using deep learning convolutional neural network. Procedia Manuf. 35, 607–612 (2019)
    Article Google Scholar
  23. Lokuliyana, S., Jayakody, A., Rupasinghe, L., Kandawala, S.: IGOE IoT framework for waste collection optimization. In: 6th National Conference on Technology and Management (NCTM) (2016)
  24. https://awesomeopensource.com/project/garythung/trashnet
  25. Aral, R.A., Keskin, S.R., Kaya, M., Hacıömeroğlu, M.: Classification of TrashNet dataset based on deep learning models. In: IEEE International Conference on Big Data (Big Data) (2018)
  26. Pui, S., Minoi, J.L.: Keypoint descriptors in SIFT and SURF for face feature extractions. In: Alfred, R., Iida, H., Ag. Ibrahim, A., Lim, Y. (eds) Computational Science and Technology. ICCST 2017. Lecture Notes in Electrical Engineering, vol. 488. Springer, Singapore (2018)
  27. Shortell, T., Shokoufandeh, A.: Secure feature extraction in computational vision using fully homomorphic encryption. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2018. FTC 2018. Advances in Intelligent Systems and Computing, vol. 881. Springer, Cham (2019)
  28. Bai, Y., Zho, L., Cheng, B., Peng, Y.F.: Surf feature extraction in encrypted domain. In: 2014 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2014)
  29. Sheena, S., Sheena, M.: A comparison of SIFT and SURF algorithm for the recognition of an efficient iris biometric system. Int. J. Adv. Res. Comput. Commun. Eng. 5(1), 37–42 (2016)
    Google Scholar
  30. Zhao, Z., Chen, W., Wu, X., Chen, P.C.Y., Liu, J.: LSTM network: A deep learning approach for Short-term traffic forecast. IET Intel. Transp. Syst. 11(2), 68–75 (2017). https://doi.org/10.1049/iet-its.2016.0208
    Article Google Scholar
  31. Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive Business Process Monitoring with LSTM neural networks. In: Proceedings of the International Conference on Advanced Information Systems Engineering (CAiSE). Lecture Notes in Computer Science. 10253. pp. 477–492. arXiv:1612.02130. doi:10.1007/978-3-319-59536-8_30. ISBN 978-3-319-59535-1 (2017)
  32. Datta, D., Agarwal, S., Kumar, V., Raj, M., Ray, B., Banerjee, A.: Design of current mode sigmoid function and hyperbolic tangent function. In: Sengupta, A., Dasgupta, S., Singh, V., Sharma, R., Kumar Vishvakarma, S. (eds) VLSI Design and Test. VDAT 2019. Communications in Computer and Information Science, vol. 1066. Springer, Singapore (2019)
  33. Naveen Naidu, M., Boindala, P.S., Vasan, A., Varma, M.R.R.: Optimization of water distribution networks using Cuckoo Search Algorithm. In: Venkata Rao, R., Taler, J. (eds) Advanced Engineering Optimization Through Intelligent Techniques. Advances in Intelligent Systems and Computing, vol. 949. Springer, Singapore (2020)
  34. Ali, A.F., Tawhid, M.A.: A hybrid cuckoo search algorithm with Nelder Mead method for solving global optimization problems. SpringerPlus 5, 473 (2016). https://doi.org/10.1186/s40064-016-2064-1
    Article Google Scholar
  35. Misra, D., Das, G., Chakrabortty, T., et al.: An IoT-based waste management system monitored by cloud. J. Mater. Cycles Waste Manag. 20, 1574–1582 (2018). https://doi.org/10.1007/s10163-018-0720-y
    Article Google Scholar
  36. Kansara, R., Bhojani, P., Chauhan, J.: Smart Waste Management for Segregating Different Types of wastes. In: Balas, V., Sharma, N., Chakrabarti, A. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol. 808. Springer, Singapore (2019)
  37. Gokulnath, C.B., Shantharajah, S.P.: An optimized feature selection based on genetic approach and support vector machine for heart disease. Cluster Comput. 22, 14777–14787 (2019). https://doi.org/10.1007/s10586-018-2416-4
    Article Google Scholar
  38. Salama, K.M., Abdelbar, A.M.: Learning neural network structures with ant colony algorithms. Swarm Intell. 9, 229–265 (2015). https://doi.org/10.1007/s11721-015-0112-z
    Article Google Scholar
  39. Yadav, N., Yadav, A., Kumar, M., et al.: An efficient algorithm based on artificial neural networks and particle swarm optimization for solution of nonlinear Troesch’s problem. Neural Comput. Appl. 28, 171–178 (2017). https://doi.org/10.1007/s00521-015-2046-1
    Article Google Scholar

Download references