Repurposing of TV boxes for a circular economy in smart cities applications
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 onl...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-97379-4 |
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| Summary: | 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. |
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| ISSN: | 2045-2322 |