Bukavu (DRC) experiences significant waste management challenges, exacerbated by rapid urbanization. The city produces approximately 898 tons of waste daily. Yet only 7% is collected through an inefficient system, increasing sanitation and environmental risks. This study designs and validates a comprehensive IoT network architecture. The system enables real-time monitoring of waste bin fill levels and optimizes collection routes. The methodology involved a 12 week pilot deployment of seven prototypes across three neighborhood types: residential, dense, and peri-urban. The system uses ultrasonic sensors with a two point calibration method to accurately estimate bin fill levels. A set of seven 120 L smart bins was deployed. The results provide evidence for the system’s robustness. Sensor energy autonomy remained above 90% after 11 months for most units, despite network challenges where 57% of sensors had Received-Signal-Strength-Indicator less than or equal to -107 decibel-milliwatt and 43% exhibited negative Signal-to-Noise Ratio values down to −10.25 decibel. It also enabled waste quantification, with an estimated 641 kg calculated from sensor fill levels over 14 days in one pilot area (seven households). The 12 week pilot across three neighborhood types validates the technical framework for a city-wide deployment. A theoretical economic analysis projects a 99% reduction in cost per ton, a 4 month payback period, and annual savings, enabling an increase in the collection rate from 7% to 30%. The architecture’s scalability is shown to be technically feasible for city-wide deployment based on pilot data and network capacity modelling, and a preliminary security risk assessment is presented. All waste mass, volume, and route time values reported are estimates or calculations derived from sensor data, not direct measurements.

Waste management in developing countries faces several challenges, including optimizing basic services and integrating sustainable innovations. However, local empirical studies that could guide this transition are often lacking. This study analyses residents’ perceptions of household waste management in the city of Bukavu, comparing traditional methods and automated systems. A probabilistic survey of 442 households across the city was conducted using mobile data collection (KoboCollect). Data were analyzed quantitatively using the Python programming language in the Google Colab environment. While a structured municipal service exists (used by 82.8% of households), satisfaction is only relative (57.2%). Critical health concerns are odors (86.9%) and disease proliferation (84.8%). Residents prioritize increasing collection frequency (78.5%) and providing public bins (76%). Although 53.6% believe their neighborhood is ready for automation, major concerns regarding job losses (66.1%) and cost (59%) persist. The study provides a three-tiered empirical roadmap: it diagnoses the need to optimize an existing service, delivers a citizen-centered action plan for immediate improvements, and defines the social and informational prerequisites for a sustainable automation transition. Success requires improving basic services while managing the perceived socioeconomic risks of innovation.