Conference Agenda

This study presents an Internet of Things (IoT)-based solution to optimize waste collection management in a sector of the Comas district, Lima. The proposal integrates real-time geolocation of the collection truck, proximity-based mobile notifications, and a household alert activated by an ESP32 module, with the aim of synchronizing waste disposal with the effective passage of the truck. To evaluate its effectiveness, a quasi-experimental design was applied, combining field tests and structured surveys before and after the intervention.

The results demonstrated significant improvements in three dimensions. Operationally, the time that waste accumulated in public spaces was reduced by approximately 90% (from 12.5 min to 1.2 min). Informationally, knowledge of the truck's schedule increased from 30% to 95%, showing that timely delivered signals reduce uncertainty and improve neighborhood coordination. Experientially, usability, satisfaction, utility, and notification clarity scales showed consistent increases (d ≥ 0.8), with adequate levels of internal reliability (α ≥ 0.78).

The technical architecture—composed of a mobile application, Firebase, Google Maps, and a household IoT module—proved to be viable and replicable in similar urban contexts. Overall, the findings confirm that IoT can optimize waste collection by aligning citizen decisions with the actual service operation, reducing waste accumulation and improving user experience. The research provides evidence and a practical framework for future large-scale implementations.