AI Article Synopsis
- Machine Learning techniques enhance energy efficiency in IoT networks by optimizing data transmission, reducing unnecessary data sent over the network.
- A smart gateway combining low-power processing with NB-IoT radio uses supervised and unsupervised ML algorithms to improve visual data size and quality before sending.
- Field results show impressive outcomes: 93% reduction in NB-IoT radio transmissions, 90.5% decrease in energy consumption, and 90% less data transmission time.
Article Abstract
Machine Learning (ML) techniques can play a pivotal role in energy efficient IoT networks by reducing the unnecessary data from transmission. With such an aim, this work combines a low-power, yet computationally capable processing unit, with an NB-IoT radio into a smart gateway that can run ML algorithms to smart transmit visual data over the NB-IoT network. The proposed smart gateway utilizes supervised and unsupervised ML algorithms to optimize the visual data in terms of their size and quality before being transmitted over the air. This relaxes the channel occupancy from an individual NB-IoT radio, reduces its energy consumption and also minimizes the transmission time of data. Our on-field results indicate up to 93% reductions in the number of NB-IoT radio transmissions, up to 90.5% reductions in the NB-IoT radio energy consumption and up to 90% reductions in the data transmission time.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8434658 | PMC |
| http://dx.doi.org/10.3390/s21175929 | DOI Listing |
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