Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices

Sensors
Arshad FarhadJae-Young Pyun

Abstract

A long-range wide area network (LoRaWAN) is one of the leading communication technologies for Internet of Things (IoT) applications. In order to fulfill the IoT-enabled application requirements, LoRaWAN employs an adaptive data rate (ADR) mechanism at both the end device (ED) and the network server (NS). NS-managed ADR aims to offer a reliable and battery-efficient resource to EDs by managing the spreading factor (SF) and transmit power (TP). However, such management is severely affected by the lack of agility in adapting to the variable channel conditions. Thus, several hours or even days may be required to converge at a level of stable and energy-efficient communication. Therefore, we propose two NS-managed ADRs, a Gaussian filter-based ADR (G-ADR) and an exponential moving average-based ADR (EMA-ADR). Both of the proposed schemes operate as a low-pass filter to resist rapid changes in the signal-to-noise ratio of received packets at the NS. The proposed methods aim to allocate the best SF and TP to both static and mobile EDs by seeking to reduce the convergence period in the confirmed mode of LoRaWAN. Based on the simulation results, we show that the G-ADR and EMA-ADR schemes reduce the convergence period in a static scenari...Continue Reading

References

Nov 21, 2018·Sensors·Jetmir HaxhibeqiriJeroen Hoebeke
May 10, 2020·Sensors·Arshad FarhadJae-Young Pyun

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Software Mentioned

ADR
SFA
EMA
LoRaWAN
NS
FUOTA
STM

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