Evidence map›Paper›PMID 40596451›Full record

ArticleScientific reports2025

Repurposing of TV boxes for a circular economy in smart cities applications.

Gustavo P C P da Luz, Gabriel Massuyoshi Sato, Luis Fernando Gomez Gonzalez, Juliana Freitag Borin

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Gustavo P C P da LuzInstitute of Computing, University of Campinas, Campinas, 13083-852, Brazil.
Gabriel Massuyoshi SatoInstitute of Computing, University of Campinas, Campinas, 13083-852, Brazil.
Luis Fernando Gomez GonzalezInstitute of Computing, University of Campinas, Campinas, 13083-852, Brazil.
Juliana Freitag BorinInstitute of Computing, University of Campinas, Campinas, 13083-852, Brazil. juliana@ic.unicamp.br.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 131653/2023-7Conselho Nacional de Desenvolvimento Científico e Tecnológico 308840/2020-8Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 88887.999360/2024-00Fundação de Amparo à Pesquisa do Estado de São Paulo 2023/00811-0Ministry of Science, Technology, and Innovations 126/2022
6 · The paper itself

Abstract

In recent years, a large number of illegal TV box devices have been confiscated in Brazil. According to a news report released in March 2024, an estimated 2.5 million TV boxes were stored in the warehouses of the Federal Revenue Service. Typically, these devices are destroyed, which not only incurs significant costs for the government but also generates substantial e-waste. Meanwhile, the advancement of smart city applications based on the Internet of Things (IoT) and machine learning has driven research in edge computing using hardware-constrained devices. This paper explores the feasibility of repurposing TV boxes for edge computing in applications involving people counting in images collected by cameras. We developed a testbed consisting of 20 TV boxes to conduct a thorough evaluation of their resilience and carbon footprint compared to commonly used edge computing equipment. Our findings demonstrate that these repurposed devices can outperform commercially available devices in terms of carbon footprint when using the Brazilian energy matrix, a conclusion drawn after performing over 16 million inferences during a stress test. Specially, the most modern TV box with the lightest model version was the best option in terms of average inferences per day, reliability, and carbon footprint. This study underscores the innovative potential and environmental benefits of repurposing TV boxes for smart city applications, especially when utilizing lightweight machine learning models.

Identifiers

PMID40596451
PMCPMC12215630

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.