Image Transmission Using LoRa Technology with Various Spreading Factors

Wei, Ching-Chung; Chen, Shu-Ting; Su, Pei-Yi

doi:10.1109/wsce49000.2019.9041044

Cited by 14 publications

(1 citation statement)

References 9 publications

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“…They showed that this approach reserves a lot of link budget during the surveillance of static agriculture sites and provides better performance. Wei et al [ 6 ] proposed a methodology for transmission of JPEG compressed image data in a multiplexing mode with different spreading factors to reduce the transmission time of image data by keeping the quality of the images at the receiver side to high PSNR values. Other similar works that use LoRA for image transmission include [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

An NB-IoT-Based Edge-of-Things Framework for Energy-Efficient Image Transfer

Khan

Moullec

Alam

2021

Sensors

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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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