QuadScatter for Simultaneous Transmissions in a Large-Scale Backscatter Network

Zeba, Ousmane; Saruwatari, Shunsuke; Watanabe, Takashi

doi:10.1109/icc40277.2020.9148907

Cited by 4 publications

(1 citation statement)

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“…The ability to consume the power of a few milliwatts is significant for the upcoming internet of things (IoT) technology. The IoT aims to improve numerous novel industrial or nonindustrial applications, where objects will be able to respond to the presence of people and other objects, anytime and anywhere [1], [2].…”

Section: Introductionmentioning

confidence: 99%

QuadScatter: Computational Efficiency in Simultaneous Transmissions for Large-Scale IoT Backscatter Networks

Zeba

Hayashi

Kizaki

et al. 2021

IEEE Open J. Comput. Soc.

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Backscatter communication has attracted attention owing to its ultra-low-power consumption ability, which is expected to enhance internet of things (IoT) technology that aims to enable many novel applications for object-to-object communication. Such a network with a large and continuously increasing number of connected objects will benefit significantly from resource-saving. This work introduces a system named QuadScatter, which is a set of algorithms that select and associate transmitters, tags and readers to enable simultaneous backscatter transmissions and increase network capacity. Consequently, the energy consumption in the network is considerably lessened. Intensive simulations have been conducted to demonstrate the effectiveness of backscatter simultaneous transmissions. QuadScatter shows promising results compared to the exhaustive search algorithm. The simulation results highlight computational time and simultaneous transmission improvements of at least 250x and 2x, respectively. Furthermore, while the exhaustive search is limited to a few nodes (< 20), our proposal uses numerous nodes. Additionally, an implementation of limited simultaneous backscatter transmissions is conducted to show its feasibility in the real world.

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Section: Introductionmentioning

confidence: 99%