Title:	An IoT solution for load monitoring and tracking of garbage-truck fleets
Authors:	Maia, Jorge Silva, Jones Yudi Mori Alves da
Assunto::	Internet das Coisas (IoT) Azure Hub IoT Telemetria veicular Edge computing Computação em nuvem
Issue Date:	2020
Publisher:	IEEE
Citation:	MAIA, Jorge; YUDI, Jones. An IoT solution for load monitoring and tracking of garbage-truck fleets. In: 2020 IEEE CONFERENCE ON INDUSTRIAL CYBERPHYSICAL SYSTEMS (ICPS), 2020, Tampere. p. 406-410. DOI: 10.1109/ICPS48405.2020.9274699. Disponível: https://ieeexplore.ieee.org/document/9274699.
Abstract:	One of the most significant problems in the modern world is the amount of waste generated by the growing urban population. The destination of this garbage can be recycling, burning or disposal in landfills. A family of four people can produce, on average, 3 kilograms of garbage per day. The collection of this waste is mostly done by trucks that go through the streets of the cities collecting the garbage from door to door, following predetermined routes. When a truck reaches its maximum load, it goes to the unloading site, returning to the route at the same exit point in order to finish that route. The logistical problems involved are several: fines for overloading the truck, late collection times, labor lawsuits, among others. This project proposes a solution based on Internet Things to monitor the location of the truck and its cargo generating maps in near real-time, to allow the dynamic optimization of the routes followed by the various paths of the fleet. Our system consists of an on-board system that measures the truckload and determines its position, sending this data to a cloud computing solution. Several tests were carried out in the field, with pathways from a real fleet, operating on an ordinary working day. The results show that the system developed is viable, successfully meeting the requirements of the application.
metadata.dc.description.unidade:	Faculdade de Tecnologia (FT) Departamento de Engenharia Mecânica (FT ENM)
DOI:	10.1109/ICPS48405.2020.9274699
metadata.dc.relation.publisherversion:	https://ieeexplore.ieee.org/document/9274699
Appears in Collections:	Trabalhos apresentados em evento

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