Route planning using wireless sensor network for garbage collection in COVID-19 pandemic

Garbage collection is a responsibility faced by all cities and, if not properly carried out, can generate greater costs or sanitary problems. Considering the sanitary situation due to the COVID-19 pandemic, it is necessary to take sanitary safety measures to prevent its spread. The challenge of the present work is to provide an efficient and effective solution that guarantees a garbage collection that optimizes the use of resources and prioritizes the attention to garbage containers located in or near contagion risk zones. To this end, this research proposes the integration of a basic garbage monitoring system, consisting of a wireless sensor network, and a route planning system that implements the decomposition of the Vehicle Routing problem into the subproblems of clustering and sequencing of containers using the K-Means and Ant Colony algorithms. For the monitoring of garbage, a significant reduction in the measurement error of waste level in the containers was achieved compared to other authors. About route planning, adequate error ranges were obtained in the calculation of the optimal values of distance traveled and travel time indicators with respect to an exhaustive enumeration of routes.

Name thesis:degree_name

Ingeniero de sistemas

Level thesis:degree_level

Título Profesional

Discipline thesis:degree_discipline

Ingeniería de sistemas

Grantor dc:publisher

Universidad de Lima

Year dc:date.issued

2022

Authors dc:creator

• Ramirez Alarcon, Javier Enrique
• Santiago Silva, Caleb Misael

Advisor dc:contributor.advisor

• Kamiyama Murakami, Angélica

• Gestión de residuos
• Detectores
• Recogida de residuos
• Refuse and refuse disposal
• Detectors
• Refuse collection
• COVID-19

Statement dc:rights

• info:eu-repo/semantics/openAccess

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-sa/4.0/

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/20.500.12724/16461

OAI identifier oai:identifier

oai:repositorio.ulima.edu.pe:20.500.12724/16461