PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

 

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ISSN 1511-3701

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ESS-IoT: The Smart Waste Management System for General Household

Shen Yuong Wong, Huashuo Han, Kin Meng Cheng, Ah Choo Koo and Salman Yussof

Pertanika Journal of Tropical Agricultural Science, Volume 31, Issue 1, January 2023

DOI: https://doi.org/10.47836/pjst.31.1.19

Keywords: IoT, machine learning, overflow mechanism, waste collection, waste classification, waste management

Published on: 3 January 2023

With the urban population’s growth, unethical and unmanaged waste disposal may negatively impact the environment. In many cities, a massive flow of people in municipal buildings or offices has generated vast amounts of waste daily, which correlates to the enormous expenses of waste management. The critical issue for better waste management is waste collection and sorting. In this study, the Electronic Smart Sorting- Internet of Things (ESS-IoT) is proposed to assist people in better waste management. The ESS-IoT system uses Raspberry Pi 4b as the microcontroller with three modules, and it is designed with two main functions: waste collection and waste classification. The two main functions have been deployed separately in the literature, while this study has combined both functions to achieve a more comprehensive smart bin waste disposal solution. Waste collection is triggered by the overflow alarm mechanism that employs ultrasonic and tracker sensors. On the other hand, the waste classification is implemented using two classification algorithms: Random Forest (RF) prediction model and Convolutional Neural Network (CNN) prediction model. An experiment is conducted to evaluate the accuracy of the two classification algorithms in classifying various types of waste. The waste materials under investigation can be classified into four categories: kitchen waste, recyclables, hazardous waste, and other waste. The results show that CNN is the better classification algorithm between the two. Future work proposes the research extension by introducing an incentive mechanism to motivate the household communities using a cloud-based competition platform incorporated with the ESS-IoT system.

  • Aja, O. C., & Al-Kayiem, H. H. (2014). Review of municipal solid waste management options in Malaysia, with an emphasis on sustainable waste-to-energy options. Journal of Material Cycles and Waste Management, 16, 693-710. https://doi.org/10.1007/s10163-013-0220-z

  • Al-Masri, E., Diabate, I., Jain, R., Lam, M. H., & Reddy, N. S. (2019). Recycle.io: An IoT-enabled framework for urban waste management. In Proceedings. 2018 IEEE International Conference on Big Data, Big Data (pp. 5285-5287). IEEE Publishing. https://doi.org/10.1109/BigData.2018.8622117

  • Anagnostopoulos, T., Zaslavsky, A., Ntalianis, K., Anagnostopoulos, C., Ramson, S., Shah, P., Behdad, S., & Salmon, I. (2020). IoT-enabled tip and swap waste management models for smart cities. International Journal of Environment and Waste Management, 28(4), 521-539. https://doi.org/10.1504/IJEWM.2021.10042472

  • Akshaya, B., & Kala, M. T. (2020). Convolutional neural network based image classification and new class detection. In Proceedings of 2020 IEEE International Conference on Power, Instrumentation, Control and Computing (pp. 1-6) IEEE Publishing. https://doi.org/10.1109/PICC51425.2020.9362375

  • Bansode, P., Rasal, P. V., Waste, S., & City, S. (2021). Smart waste management for smart city using IoT. Journal of Science and Technology, 6(1), 53-58.

  • Chandra, R. P., & Tawami, T. (2020). Design of smart trash bin. In IOP Conference Series: Materials Science and Engineering: Material Science Engineering (Vol. 879, p. 012155). IOP Publishing Limited. https://doi.org/10.1088/1757-899X/879/1/012155

  • Department of Statistics Malaysia. (2020, November 27). Compendium of Environment Statistics, Malaysia 2020 (Press Release). Department of Statistics Malaysia. https://www.dosm.gov.my/v1/index.php?r=column/pdfPrev&id=TjM1ZlFxb3VOakdmMnozVms5dUlKZz09

  • Dugdhe. S., Shelar, P., Jire, S., & Apte, A. (2016). Efficient waste collection system. In Proceedings of the International Conference on Internet of Things and Applications (IOTA) (pp. 143-147) IEEE Publishing. https://doi.org/10.1109/IOTA.2016.7562711

  • Fachmin, F., Low Y. S., & Yeow W. L. (2015). Smartbin: Smart waste management system. In IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) (pp. 1-2). IEEE Publishing. https://doi.org/10.1109/ISSNIP.2015.7106974

  • Ferronato, N., & Torretta, V. (2019). Waste mismanagement in developing countries: A review of global issues. International Journal of Environmental Research and Public Health, 16(6), Article 1060. https://doi.org/10.3390/ijerph16061060.

  • Hanbal, I. F., Jeffrey, S. I., Neal, A. O., & Hu, Y. (2020). Classifying wastes using random forests, gaussian naïve bayes, support vector machine and multilayer perceptron. International Conference Series: Material Science Engineering, 803, Article 012017. https://doi.org/10.1088/1757-899X/803/1/012017.

  • Ivan, L. (April 07, 2021). Council to give out free rubbish bins to residents The Star. https://www.thestar.com.my/metro/metro-news/2021/04/07/council-to-give-out-free-rubbish-bins-to-residents

  • Kumar, N. S., Vuayalakshmi, B., Prarthana, R. J., & Shankar, A. (2016). IoT based smart garbage alert system using Arduino UNO. In Proceedings of 2016 IEEE Region 10 Conference (TENCON) (pp. 1028-1034). IEEE Publishing. https://doi.org/10.1109/TENCON.2016.7848162

  • Lim, M. G., & Chuah, J. H. (2018). Durian types recognition using deep learning techniques. In Proceedings of the 9th IEEE Control and System Graduate Research Colloquium (ICSGRC) (pp. 183-187). IEEE Publishing. https://doi.org/10.1109/ICSGRC.2018.8657535

  • Low S. T., Tee S. Y., & Choong W. W. (2016). Preferred attributes of waste separation behaviour: An empirical study. Procedia Engineering, 145, 738-745. https://doi.org/10.1016/j.proeng.2016.04.094

  • Mirchandani, S., Wadhwa, S., Wadhwa, P., & Joseph, R. (2018). IoT enabled dustbins. In Proceedings of the 2017 International Conference on Big Data, IoT and Data Science (pp. 73-76). IEEE Publishing. https://doi.org/10.1109/BID.2017.8336576

  • Nanda, S., & Berruti, F. (2021). Municipal solid waste management and landfilling technologies: A review. Environmental Chemistry Letters, 19(2), 1433-1456. https://doi.org/10.1007/s10311-020-01100-y

  • Pelonero, L., Fornaia, A., & Tramontana, E., (2020). From smart city to smart citizen: Rewarding waste recycle by designing a data-centric IoT-based garbage collection service. In Proceedings of 2020 IEEE International Conference on Smart Computing (SMARTCOMP) (pp. 380-385). IEEE Publishing. https://doi.org/10.1109/SMARTCOMP50058.2020.00081

  • Rana, R., Ganguly. R., Ashok, K. G., (2015). An Asssessment of solid waste management in Chandigarh City, India. Electronic Journal of Geotechnical Engineering, 20, 1547-1572.

  • Ruiz, V., Sanchez, A., Velez, J. F., & Raducanu, B. (2019). Automatic image-based waste. In J. M. F. Vicente, J. R. A. Sanchez, F. D. L. P. Lopez, J. T. Moreo & H. Adeli (Eds), From Bioinspired Systems and Biomedical Applications to Machine Learning (pp. 422-431). Springer Nature. https://doi.org/10.1007/978-3-030-19651-6

  • Shaily, T., & Kala, S. (2020). Bacterial image classification using convolutional neural networks. In Proceedings of the IEEE 17th India Council International Conference (INDICON) (pp. 1-6). IEEE Publishing. https://doi.org/10.1109/INDICON49873.2020.9342356

  • Sushmitha, N., Veena, R. C., Ajay, K., Ajith, D., & Yasaswi, K. (2018). An IoT based waste management system for smart cities. International Journal of Emerging Technologies and Innovative Research, 5(12), 202-205.

  • The World Bank. (2018, September 20). Global waste to grow by 70 percent by 2050 unless urgent action is taken: World Bank report. (Press Release). https://www.worldbank.org/en/news/press-release/2018/09/20/global-waste-to-grow-by-70-percent-by-2050-unless-urgent-action-is-taken-world-bank-report

  • Wahab, M. H. A., Kadir, A. A., Tomari, M. R., & Jabbar, M. H. (2014). Smart recycle bin: A conceptual approach of smart waste management with integrated web based system. In Proceedings of 2014 International Conference on IT Convergence and Security (ICITCS) (pp. 1-4 ). IEEE Publishing. https://doi.org/10.1109/ICITCS.2014.7021812

  • Wang, S., Chen, W., Xie, S. M., Azzari, G., & Lobell, D. B. (2020). Weakly supervised deep learning for segmentation of remote sensing imagery. Remote Sensing, 12(2), Article 207. https://doi.org/10.3390/rs12020207

  • Wu, M., Lu, Y., Yang, W., & Wong, S. Y. (2021). A study on arrhythmia via ECG signal classification using the convolutional neural network. Frontiers in Computational Neuroscience, 14, 1-10. https://doi.org/10.3389/fncom.2020.564015

  • Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In D. Fleet, T. Pajdla, B. Schiele & T. Tuytelaars (Eds.), Computer Vision-ECCV 2014, Lecture Notes in Computer Science, (Vol. 8689). Springer. https://doi.org/10.1007/978-3-319-10590-1_53

ISSN 1511-3701

e-ISSN 2231-8542

Article ID

JST-3331-2021

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