Cantor: improving goodput in LoRa concurrent transmission

Dan Xu, Xiaojiang Chen, Nannan Zhang, Nana Ding, Jing Zhang, Dingyi Fang, Tao Gu

Research output: Contribution to journalArticlepeer-review

Abstract

Long range (LoRa) is an attractive low-power wide-area networks (LPWANs) technology for its features of low power, long range, and support for concurrent transmission. Our study reveals LoRa concurrent transmission suffer from the mismatch between the sender's reception (RX) and gateway's transmission (TX) window, which leads to the decline of goodput even the throughput is improved. Our experiment shows that goodput only accounts for two-fifths of the throughput in concurrent transmissions with 48 nodes at a duty cycle of 20%. This article presents a window match scheme named Cantor which improves the goodput of LoRa concurrent transmission by controlling the RX window size. Cantor does not require the frequent exchange of controlling information. Instead, it introduces a novel concurrent transmission model to estimate the downlink packet reception rate (PRR) with different network parameters, and a regression model is used to make the result more realistic. Then, we propose a simple optimization algorithm to select optimal RX window sizes in which nodes are able to receive acknowledgments. We implement and evaluate Cantor with commodity LoRa gateway and nodes, and conduct experiments in different scenarios. The experimental results show that Cantor increases the goodput by 70% and reduces energy consumption by 30% in LoRa concurrent transmissions with 48 nodes operate at a duty cycle of 20%.
Original languageEnglish
Pages (from-to)1519-1532
Number of pages14
JournalIEEE Internet of Things Journal
Volume8
Issue number3
Early online date31 Jul 2020
DOIs
Publication statusPublished - 1 Feb 2021
Externally publishedYes

Keywords

  • Concurrent transmission
  • goodput
  • long range (LoRa)
  • window mismatch

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