EcoPull: Sustainable IoT Image Retrieval Empowered by TinyML Models
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911861133279232 |
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| author | Thorsager, Mathias Croisfelt, Victor Shiraishi, Junya Popovski, Petar |
| author_facet | Thorsager, Mathias Croisfelt, Victor Shiraishi, Junya Popovski, Petar |
| contents | This paper introduces EcoPull, a sustainable Internet of Things (IoT) framework empowered by tiny machine learning (TinyML) models for fetching images from wireless visual sensor networks. Two types of learnable TinyML models are installed in the IoT devices: i) a behavior model and ii) an image compressor model. The first filters out irrelevant images for the current task, reducing unnecessary transmission and resource competition among the devices. The second allows IoT devices to communicate with the receiver via latent representations of images, reducing communication bandwidth usage. However, integrating learnable modules into IoT devices comes at the cost of increased energy consumption due to inference. The numerical results show that the proposed framework can save > 70% energy compared to the baseline while maintaining the quality of the retrieved images at the ES. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14236 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EcoPull: Sustainable IoT Image Retrieval Empowered by TinyML Models Thorsager, Mathias Croisfelt, Victor Shiraishi, Junya Popovski, Petar Networking and Internet Architecture This paper introduces EcoPull, a sustainable Internet of Things (IoT) framework empowered by tiny machine learning (TinyML) models for fetching images from wireless visual sensor networks. Two types of learnable TinyML models are installed in the IoT devices: i) a behavior model and ii) an image compressor model. The first filters out irrelevant images for the current task, reducing unnecessary transmission and resource competition among the devices. The second allows IoT devices to communicate with the receiver via latent representations of images, reducing communication bandwidth usage. However, integrating learnable modules into IoT devices comes at the cost of increased energy consumption due to inference. The numerical results show that the proposed framework can save > 70% energy compared to the baseline while maintaining the quality of the retrieved images at the ES. |
| title | EcoPull: Sustainable IoT Image Retrieval Empowered by TinyML Models |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2404.14236 |