TITLE:
Simulation-Based Comparison of Urban Waste Collection in Resource-Constrained Settings: Traditional, Imported IoT, and Contextualized IoT
AUTHORS:
Didi Tshimpanga, Alex Lina, Olivier Baraka Mushage, Audace Manirabona, Jérémie Ndikumagenge
KEYWORDS:
Urban Waste Management, Contextualized IoT, Resource-Constrained Settings
JOURNAL NAME:
Advances in Internet of Things,
Vol.16 No.2,
April
23,
2026
ABSTRACT: 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