Urban waste collection in resource-constrained contexts faces significant challenges due to variability in waste generation and limited operational capacity. This study presents a simulation-based comparison of three collection strategies: traditional fixed-route collection, intelligent collection with imported IoT, and intelligent collection based on contextualized IoT. A discrete-event simulation model is developed to evaluate system performance across operational, environmental, and economic dimensions within a common experimental framework. The contextualized IoT approach integrates a Contextual Prioritization Index (CPI), which enables adaptive decision-making based on multiple criteria beyond simple fill-level thresholds. Results from 30 independent simulation runs show that traditional collection systems exhibit low operational activity but result in significant under-service, leading to high levels of uncollected waste and associated nuisance costs. The imported IoT approach improves collection efficiency but increases travel distance, emissions, and operational costs due to reactive routing behavior. In contrast, the contextualized IoT approach maintains a comparable level of service while reducing operational intensity, emissions, and total cost. Statistical analysis using the Friedman test and post-hoc Wilcoxon signed-rank tests confirms that the observed differences between scenarios are highly significant (p < 0.001). The integration of nuisance cost into the economic evaluation provides a more comprehensive assessment of system performance, particularly in environments where under-service generates substantial indirect impacts. These findings highlight the importance of context-aware decision mechanisms in designing sustainable waste collection systems under resource constraints, where efficiency depends more on adaptive prioritization than on technological sophistication alone.
References
[1]
Addas, A., Khan, M.N. and Naseer, F. (2024) Waste Management 2.0 Leveraging Internet of Things for an Efficient and Eco-Friendly Smart City Solution. PLOSONE, 19, e0307608. https://doi.org/10.1371/journal.pone.0307608
[2]
Enache, B.A., Banica, C.K. and Bogdan, A.G. (2024) Optimising Urban Waste Management through Smart Monitoring Systems: A Case Study in Enhancing Efficiency and Sustainability. TheScientificBulletinofElectricalEngineeringFaculty, 24, 60-63. https://doi.org/10.2478/sbeef-2024-0011
[3]
Guerrero, L.A., Maas, G. and Hogland, W. (2013) Solid Waste Management Challenges for Cities in Developing Countries. Waste Management, 33, 220-232. https://doi.org/10.1016/j.wasman.2012.09.008
[4]
Wilson, B.G. and Vincent, J.K. (2008) Estimating Waste Transfer Station Delays Using GPS. Waste Management, 28, 1742-1750. https://doi.org/10.1016/j.wasman.2007.09.020
[5]
Anand, J., Elangovan, V., Muthukumar, V. and Thivya, C. (2023) Waste Management System Using IoT for Sustainable Cities. 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), Trichy, 23-25 August 2023, 1634-1639. https://doi.org/10.1109/icaiss58487.2023.10250610
[6]
Faccio, M., Persona, A. and Zanin, G. (2011) Waste Collection Multi Objective Model with Real Time Traceability Data. Waste Management, 31, 2391-2405. https://doi.org/10.1016/j.wasman.2011.07.005
[7]
Longhi, S., Marzioni, D., Alidori, E., Di Buo, G., Prist, M., Grisostomi, M., et al. (2012) Solid Waste Management Architecture Using Wireless Sensor Network Technology. 2012 5th International Conference on New Technologies, Mobility and Security (NTMS), Istanbul, 7-10 May 2012, 1-5. https://doi.org/10.1109/ntms.2012.6208764
[8]
Leochico, C.F.D., Espiritu, A.I., Ignacio, S.D. and Mojica, J.A.P. (2020) Challenges to the Emergence of Telerehabilitation in a Developing Country: A Systematic Review. Frontiers in Neurology, 11, Article 1007. https://doi.org/10.3389/fneur.2020.01007
[9]
Sardesai, S. and Klingebiel, K. (2023) Maintaining Viability by Rapid Supply Chain Adaptation Using a Process Capability Index. Omega, 115, Article 102778. https://doi.org/10.1016/j.omega.2022.102778
[10]
Tshimpanga, D., Mushage, O.B., Manirabona, A. and Ndikumagenge, J. (2026) Contextual Selection of Multi-Criteria Decision-Making Methods for Waste Management in Low-Resource Settings: A Conceptual Framework and Comparative Analysis. Advances in Internet of Things, 16, 1-10. https://doi.org/10.4236/ait.2026.161001
[11]
Carvalho, L.I., da Silva, D.M.A. and Sofia, R.C. (2020) Leveraging Context-Awareness to Better Support the IoT Cloud-Edge Continuum. 2020 5th International Conference on Fog and Mobile Edge Computing (FMEC), Paris, 20-23 April 2020, 356-359. https://doi.org/10.1109/fmec49853.2020.9144760
[12]
Machado, R., Almeida, R., Albandes, R., Pernas, A.M. and Yamin, A. (2020) An IoT Architecture to Provide Hybrid Context Reasoning. In: IFIP Advances in Information and Communication Technology, Springer, 86-102. https://doi.org/10.1007/978-3-030-43605-6_6
[13]
Badibanga, D.T., Mushage, O.B., Manirabona, A. and Ndikumagenge, J. (2025) Critical Systematic Review of the Transferability of Iot Solutions for Smart Waste Collection in Low-Resource Settings. AdvancesinInternetofThings, 15, 96-119. https://doi.org/10.4236/ait.2025.154006
[14]
Tshimpanga, D., Lina, A., Mushage, O.B., Manirabona, A. and Ndikumagenge, J. (2026) Validation of a Multi-Agent Simulation Engine for Urban Solid Waste Collection in Resource-Constrained Contexts. Advances in Internet of Things, 16, 28-42. https://doi.org/10.4236/ait.2026.162003
[15]
Din, G.Y. and Cohen, E. (2013) Modeling Municipal Solid Waste Management in Africa: Case Study of Matadi, the Democratic Republic of Congo. Journal of Environmental Protection, 4, 435-445. https://doi.org/10.4236/jep.2013.45052
[16]
Shi, Y., Wang, Y., Yue, Y., Zhao, J., Maraseni, T. and Qian, G. (2021) Unbalanced Status and Multidimensional Influences of Municipal Solid Waste Management in Africa. Chemosphere, 281, Article 130884. https://doi.org/10.1016/j.chemosphere.2021.130884
[17]
KOMWAG (2026) Types of Containers. https://www.komwag.cz/en/types-of-containers
[18]
World Bank (2025) Déchets: Quel gâchis 2.0: Un état des lieux actualisé des enjeux de la gestion des ordures ménagères. World Bank. https://www.banquemondiale.org/fr/news/immersive-story/2018/09/20/what-a-waste-an-updated-look-into-the-future-of-solid-waste-management
[19]
Tshimpanga, D., Lina, A., Mushage, O.B., Manirabona, A. and Ndikumagenge, J. (2026) A Contextual Prioritization Index for Solid Waste Collection in Low-Resource Urban Areas: Kinshasa. Advances in Internet of Things, 16, 11-27. https://doi.org/10.4236/ait.2026.162002
[20]
Almelhem, M., Buics, L., Süle, E. and Simoes, R. (2025) Industry 4.0 for Sustainable Reverse Waste Collection: A Systematic Literature Review. JournalofTransportandSupplyChainManagement, 19, a1179. https://doi.org/10.4102/jtscm.v19i0.1179
[21]
Anitha, R. and Parthiban, A. (2025) AI-IoT-Graph Synergy for Smart Waste Management: A Scalable Framework for Predictive, Resilient, and Sustainable Urban Systems. Frontiers in Sustainability, 6, Article 1675021. https://doi.org/10.3389/frsus.2025.1675021
[22]
Prata, J., Simões, C.L. and Simoes, R. (2025) Improvements to Municipal Solid Waste Collection Systems Using Real-Time Monitoring. Sustainability, 17, Article 1405. https://doi.org/10.3390/su17041405
[23]
Borges Caldas da Silva, P.V. (2026) Middleware Support for Energy Awareness in the Internet of Things (IoT). Theses, Institut Polytechnique de Paris. https://theses.hal.science/tel-03937453
[24]
López, O.L.A., Rosabal, O.M., Ruiz-Guirola, D.E., Raghuwanshi, P., Mikhaylov, K., Lovén, L., et al. (2023) Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions. IEEEOpenJournaloftheCommunicationsSociety, 4, 2609-2666. https://doi.org/10.1109/ojcoms.2023.3323832
[25]
Wadel, F. (2026) Contribution à l’élaboration d’une stratégie de maintenance prévisionnelle basée sur l’IoT : Application à l’industrie biopharmaceutique. Theses, Université de Strasbourg. https://theses.hal.science/tel-05153994
[26]
Wang, Z., Chen, C., Li, X., Zheng, P. and Khoo, L.P. (2021) A Context-Aware Concept Evaluation Approach Based on User Experiences for Smart Product-Service Systems Design Iteration. Advanced Engineering Informatics, 50, Article 101394. https://doi.org/10.1016/j.aei.2021.101394
[27]
Haddoun, N. and Khomsi, D. (2020) Design of a Computer Tool for the Optimization of Solid Waste Collection Circuits. E3S Web of Conferences, 150, Article 02016. https://doi.org/10.1051/e3sconf/202015002016
[28]
Hirbod, F., Karimi, T., Mohammadnazari, Z., Aghsami, A. and Rabbani, M. (2022) Municipal Waste Management Using Multiple Disposal Location Arc Routing and Waste Segregation Approach—A Real Life Case Study in England.
[29]
Li, X., An, X. and Zhang, B. (2024) Minimizing Passenger Waiting Time in the Multi-Route Bus Fleet Allocation Problem through Distributionally Robust Optimization and Reinforcement Learning. Computers & Operations Research, 164, Article 106568. https://doi.org/10.1016/j.cor.2024.106568
