Development of AMPIBI: A solar-powered smart waste monitoring and sorting system with cloud integration for environmental preservation and energy conservation
DOI:
https://doi.org/10.61511/whem.v2i2.2025.2348Keywords:
AMPIBI, IoT, energy conservation, waste managementAbstract
Background: Waste management in campus areas remains a significant issue, with trash bins often overflowing due to irregular monitoring and limited awareness among users. This problem is exacerbated by inefficient and energy-consuming traditional waste collection methods, alongside a common failure among students to properly separate waste at the source. The accumulation of unsorted waste not only degrades the campus environment but also represents a missed opportunity for effective recycling and resource recovery. Existing smart bin solutions often focus on a single aspect, such as capacity monitoring or basic sorting, but rarely integrate a comprehensive, energy-independent system tailored for developing-world contexts. To address this multifaceted challenge, a new generation of smart and automated waste management systems is needed. This study introduces a novel solution designed to tackle these issues simultaneously. Methods: This study developed the Automatic Monitoring and Sorting Waste Bin (AMPIBI), an Internet of Things (IoT)-based device designed to automatically sort waste by category and monitor bin capacity in real time. The system integrates cloud-based applications, solar power, and multiple sensors, including moisture, metal, and ultrasonic sensors. The research followed a Research and Development (R&D) approach consisting of problem analysis, design, prototyping, and performance testing. Findings: Experimental results demonstrated that AMPIBI successfully classified waste into three categories: organic, non-metallic inorganic, and metal with an accuracy of 96.67%. The moisture sensor effectively distinguished organic from inorganic waste, the inductive sensor identified metals, and the ultrasonic sensor measured bin capacity. The monitoring system displayed real-time waste status via a cloud platform accessible through mobile devices. Conclusion: AMPIBI improves campus waste management by promoting proper waste disposal, reducing the need for manual intervention, and supporting environmentally friendly practices. Powered by solar energy, the system proved efficient and sustainable, making it a viable solution for cleaner and more energy-conserving campus environments. Novelty/Originality of this article: The novelty of this study lies in the integration of IoT technology, automated waste sorting, and renewable energy into a single system tailored for campus waste management. Unlike conventional bins, AMPIBI provides real-time monitoring, accurate waste classification, and independent solar-powered operation, offering an innovative model for sustainable waste management.
References
Abdelmoneim, A. A., Al Kalaany, C. M., Khadra, R., Derardja, B., & Dragonetti, G. (2025). Calibration of low-cost capacitive soil moisture sensors for irrigation management applications. Sensors, 25(2), 343. https://doi.org/10.3390/s25020343
Abdul-Qawy, A. S., Pramod, P. J., Magesh, E., & Srinivasulu, T. (2015). The internet of things (IoT): An overview. International Journal of Engineering Research and Applications, 5(12), 71–82. https://www.ijera.com/
Abishek, K. S., Deepak, D., Babu, R. D., & Keerthana, S. (2025). An IoT-based smart waste disposal and Credibin rewards system for sustainable campus waste management. In 2025 International Conference on Electronics and Renewable Systems (ICEARS) (pp. 674–679). IEEE. https://ieeexplore.ieee.org
Addas, A., Khan, M. N., & Naseer, F. (2024). Waste management 2.0 leveraging internet of things for an efficient and eco-friendly smart city solution. PLOS One, 19(7), e0307608. https://doi.org/10.1371/journal.pone.0307608
Ahmad, G., Khan, M., & Farooq, S. (2025). Intelligent waste sorting for urban sustainability using deep learning and IoT. Scientific Reports, 15, 8461. https://doi.org/10.1038/s41598-025-08461-w
Ahmad, Y. A., Gunawan, T. S., Mansor, H., Hamida, B. A., Hishamudin, A. F., & Arifin, F. (2021). On the evaluation of DHT22 temperature sensor for IoT application. In 2021 8th International Conference on Computer and Communication Engineering (ICCCE) (pp. 131–134). IEEE. https://doi.org/10.1109/ICCCE50029.2021.9467206
Ahmed, I., Zhang, Y., Jeon, G., Lin, W., Khosravi, M. R., & Qi, L. (2022). A blockchain- and artificial intelligence-enabled smart IoT framework for sustainable city. International Journal of Intelligent Systems, 37(9), 6493–6507. https://doi.org/10.1002/int.22833
Aladiyan, A. (2024). Efficient data structures and algorithms for cloud computing platforms. In 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 1717–1721). IEEE. https://doi.org/10.1109/ICACITE59712.2024.10467288
Alourani, A., Ashraf, M. U., & Aloraini, M. (2025). Smart waste management and classification system using advanced IoT and AI technologies. PeerJ Computer Science, 11, e2777. https://doi.org/10.7717/peerj-cs.2777
Ameen, A., Ahmad, J., & Raza, S. (2016). Effect of pH and moisture content on composting of municipal solid waste. International Journal of Scientific and Research Publications, 6(5), 35–37.https://www.ijsrp.org/research-paper-0516.php?rp=P5351
Arbeláez-Estrada, J. C., Vallejo, P., Aguilar, J., Tabares-Betancur, M. S., Ríos-Zapata, D., Ruiz-Arenas, S., & Rendón-Vélez, E. (2023). A systematic literature review of waste identification in automatic separation systems. Recycling, 8(6), 86. https://doi.org/10.3390/recycling8060086
Arduino. (2025). UNO R3 – Tech specs. https://docs.arduino.cc/hardware/uno-rev3
Bahrami, M. (2015). Cloud computing for emerging mobile cloud apps. In 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (pp. 4–5). IEEE. https://doi.org/10.1109/MobileCloud.2015.15
Balamurugan, R., Kumar, V. S., Pasupuleti, T., & Kumar, S. D. (2023). Design and investigation of automatic trash collecting machine for industry. SAE Technical Paper No. 2023-28-0179. https://doi.org/10.4271/2023-28-0179
Baldo, D., Mecocci, A., Parrino, S., Peruzzi, G., & Pozzebon, A. (2021). A multi-layer LoRaWAN infrastructure for smart waste management. Sensors, 21(8), 2600. https://doi.org/10.3390/s21082600
Berte, D. R. (2018). Defining the IoT. In Proceedings of the International Conference on Business Excellence (Vol. 12, No. 1, pp. 118–128). https://doi.org/10.2478/picbe-2018-0011
Blynk. (2025). Blynk.Cloud – Blynk documentation. https://docs.blynk.io/en
Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and internet of things: A survey. Future Generation Computer Systems, 56, 684–700. https://doi.org/10.1016/j.future.2015.09.021
Chhabra, M., Kumar, S., & Singh, G. (2024). Intelligent waste classification approach based on convolutional neural networks and feature engineering. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-18939-w
Cruz, N., Cota, N., & Tremoceiro, J. (2021). LoRaWAN and urban waste management—A trial. Sensors, 21(6), 2142. https://doi.org/10.3390/s21062142
Dawodu, A., Dai, H., Zou, T., Zhou, H., Lian, W., Oladejo, J., & Osebor, F. (2022). Campus sustainability research: Indicators and dimensions to consider for the design and assessment of a sustainable campus. Heliyon, 8(12), e11864. https://doi.org/10.1016/j.heliyon.2022.e11864
Espressif Systems. (2023). ESP8266EX datasheet. Espressif Systems. https://www.espressif.com/en/products/socs/esp8266
Fatimah, Y. A., Govindan, K., Murniningsih, R., & Setiawan, A. (2020). Industry 4.0 based sustainable circular economy approach for smart waste management system to achieve sustainable development goals: A case study of Indonesia. Journal of Cleaner Production, 269, Article 122263. https://doi.org/10.1016/j.jclepro.2020.122263
Gadde, M. N., Oli, P., & Somineni, M. (2023). Automatic trash monitoring system. In 2023 8th International Conference on Communication and Electronics Systems (ICCES) (pp. 1662–1667). IEEE. https://ieeexplore.ieee.org
Haji, L. M., Ahmad, O. M., Zeebaree, S. R., Dino, H. I., Zebari, R. R., & Shukur, H. M. (2020). Impact of cloud computing and internet of things on the future internet. Technology Reports of Kansai University, 62(5), 2179–2190. https://www.kansaiuniversityreports.com/
Kunduru, A. R. (2023). Artificial intelligence usage in cloud application performance improvement. Central Asian Journal of Mathematical Theory and Computer Sciences, 4(8), 42–47. https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/491
Kuntoğlu, M., Salur, E., Gupta, M. K., Sarıkaya, M., & Pimenov, D. Y. (2021). A state of the art review on sensors and signal processing systems in mechanical machining processes. International Journal of Advanced Manufacturing Technology, 116(9–10), 2711–2735. https://doi.org/10.1007/s00170-021-07425-4
Kurniawan, T. A., Gamaralalage, P. J. D., Othman, M. H. D., & Avtar, R. (2020). Progress, challenges, and opportunities for solid waste management in Indonesia. Journal of Material Cycles and Waste Management, 22, 214–230. https://doi.org/10.1007/s10163-019-00990-3
Machado, M. A. (2024). Eddy currents probe design for NDT applications: A review. Sensors (Basel, Switzerland), 24(17), 5819. https://doi.org/10.3390/s24175819
Madakam, S., Ramaswamy, R., & Tripathi, S. (2015). Internet of Things (IoT): A literature review. Journal of Computer and Communications, 3(5), 164–173. http://dx.doi.org/10.4236/jcc.2015.35021
Maus, T. R., Zengeler, N., & Sänger, D. (2024). Volume determination challenges in waste sorting facilities: Observations and strategies. Sensors, 24(7), 2114. https://www.mdpi.com/1424-8220/24/7/2114
Mishra, S., & Tyagi, A. K. (2022). The role of machine learning techniques in internet of things based cloud applications. In Artificial intelligence based internet of things systems (pp. 105–135). Springer International Publishing.
Pamintuan, M., Mantiquilla, S. M., Reyes, H., & Samonte, M. J. (2019). i-BIN: An intelligent 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). IEEE. https://ieeexplore.ieee.org
Proença, P. F., & Simões, P. (2020). TACO: Trash annotations in context for litter detection. arXiv. https://doi.org/10.48550/arXiv.2003.06975
Purwoto, B. H., Jatmiko, J., Fadilah, M. A., & Huda, I. F. (2018). Efisiensi penggunaan panel surya sebagai sumber energi alternatif. Emitor: Jurnal Teknik Elektro, 18(1), 10–14. https://ejournal.unisri.ac.id/index.php/emitor/article/view/45
Qu, D. (2021). Application of artificial intelligence in waste classification management at university. In International Conference on Intelligent Vision and Computing (pp. 330–343). Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-030-78016-2_26
Republic of Indonesia. (2008). Law No. 18 of 2008 regarding Waste Management. Directorate General of Legislation. https://peraturan.go.id/id/uu-no-18-tahun-2008
Republic of Indonesia. (2017). Presidential Regulation No. 97 of 2017 concerning National Policy and Strategy on Management of Household Waste and Household-like Waste (Jakstranas). Directorate General of Legislation. https://peraturan.go.id/perpres/no-97-tahun-2017
Rodríguez-Guerreiro, M. J., Torrijos, V., & Soto, M. (2024). A review of waste management in higher education institutions: The road to zero waste and sustainability. Environments, 11(12), 293. https://doi.org/10.3390/environments11120293
Rusimamto, P. W., Endryansyah, L. A., Harimurti, R., & Anistyasari, Y. (2021). Implementation of Arduino Pro Mini and ESP32 Cam for temperature monitoring on automatic thermogun IoT-based. Indonesian Journal of Electrical Engineering and Computer Science, 23(3), 1366–1375. https://doi.org/10.11591/ijeecs.v23.i3.pp1366-1375
Singh, S., Singh, V. P., Dohare, R. K., Singh, S. P., & Jain, S. K. (2017). Optimal PID controller design for level control of three tank system. International Journal of Advanced Technology and Engineering Exploration, 4(26), 1. https://doi.org/10.19101/IJATEE.2017.426001
Siva Priya, R., Shunmughavel, V., Praveen Kumar, B., & Aruna, E. R. (2023). Data security for Internet of Things (IoT) using lightweight cryptography (LWC) method. In International Conference on Intelligent Computing for Sustainable Development (pp. 135–144). Springer Nature Switzerland. https://link.springer.com/chapter/10.1007/978-3-031-40350-6_12
Soumyalatha, S. G. H. (2016). Study of IoT: Understanding IoT architecture, applications, issues and challenges. In 1st International Conference on Innovations in Computing & Networking (ICICN16), CSE, RRCE (Vol. 478, pp. 477–482). International Journal of Advanced Networking & Applications. https://papers.ijana.in/ICICN16/478-477-482
Stylianou, E., Pateraki, C., Ladakis, D., Cruz Fernández, M., Latorre Sánchez, M., Coll, C., & Koutinas, A. (2020). Evaluation of organic fractions of municipal solid waste as renewable feedstock for succinic acid production. Biotechnology for Biofuels, 13, 72. https://doi.org/10.1186/s13068-020-01708-w
Suthar, S. B., Chithra, R., Abhinaya, G. K., & Harikumar, H. (2023). Smart waste segregation and collection system with IoT-enabled monitoring and analytics. In 2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI) (pp. 1–6). IEEE. https://ieeexplore.ieee.org
United Nations Environment Programme (UNEP). (2024). Global waste management outlook 2024. UNEP. https://www.unep.org/resources/global-waste-management-outlook-2024
United Nations. (n.d.-a). SDG 7 – Affordable and clean energy. https://sdgs.un.org/goals/goal7
United Nations. (n.d.-b). SDG 12 – Responsible consumption and production. https://sdgs.un.org/goals/goal12
Ustun, T. S., & Abdolrasol, M. G. (2025). Techno-economic analysis of off-grid PV systems and batteries vs diesel generators: Life span and cost-effectiveness. CRC Press. https://doi.org/10.1201/9781003663348
Wang, L., Ranjan, R., Chen, J., & Benatallah, B. (Eds.). (2017). Cloud computing: Methodology, systems, and applications. CRC Press. https://www.crcpress.com/Cloud-Computing-Methodology-Systems-and-Applications/Wang-Ranjan-Chen-Benatallah/p/book/9781498712645)
Williams, K. C., O’Toole, M. D., Mallaburn, M. J., & Peyton, A. J. (2023). A review of the classification of non-ferrous metals using magnetic induction for recycling. Insight: Non-Destructive Testing and Condition Monitoring, 65(7), 384–388. https://doi.org/10.1784/insi.2023.65.7.384
World Bank, & ESMAP. (2025). Global solar atlas – Indonesia (PV power potential). https://globalsolaratlas.info/map
Wu, Z., Kai, Q., & Jianguo, Z. (2020). A smart microcontroller architecture for the Internet of Things. Sensors, 20(7), 1821. https://doi.org/10.3390/s20071821
Wu, L., Leng, J., & Ju, B. (2021). Digital twins-based smart design and control of ultra-precision machining: A review. Symmetry, 13(9), 1717. https://doi.org/10.3390/sym13091717
Zhang, W., & Jian, J. (2024). Research hotspots, research frontiers, and management significance: A bibliometric analysis and review of global food waste of students research based on CiteSpace. Sustainability, 16(8), 3145. https://doi.org/10.3390/su16083145
Zhao, Y., & Li, J. (2022). Sensor-based technologies in effective solid waste sorting: Successful applications, sensor combination, and future directions. Environmental Science & Technology, 56(24), 17531–17544. https://doi.org/10.1021/acs.est.2c04514
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