Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices

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 scenario by 16% and 68%, and in a mobility scenario by 17% and 81%, respectively, as compared to typical ADR. Moreover, we show that the proposed schemes are successful in reducing the energy consumption and enhancing the packet success ratio.

Improving the Convergence Period of Adaptive Data Rate in a Long Range Wide Area Network for the Internet of Things Devices

“…The convergence time issue is highlighted in [3,5,11] and resolved by the authors in [15,16]. Initially, the convergence time issue of the ADR was highlighted in [5] using various channel conditions.…”

“…However, the authors in [15] only considered a static environment. The convergence time issue was resolved in mobility environments in [16]. Therefore, the authors in [16] proposed two NS-side ADRs (exponentiation moving average and Gaussian-filer-based).…”

Experimental Evaluation of the Availability of LoRaWAN Frequency Channels in the Czech Republic

“…However, most studies focus on a targeted verification of technological possibilities, or on improving Quality-Of-Service in LoRaWAN through Optimized Radio Resource Management as settings parameters (ADR, SF, CSS, channels, etc.) [ 10 , 11 , 12 ]. The consequences of such optimization have an impact on energy consumption, which is also addressed by several authors, as exemplified: “Comparison of LoRaWAN Classes and their Power Consumption” [ 13 ].…”

A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies

“…To minimize this problem, a low-pass smoothing filter can be employed. Some of the representative smoothing filters are moving average filter [ 12 , 81 ], Kalman filter [ 110 , 111 , 112 ], Gaussian filter [ 72 , 113 ], and exponential averaging [ 114 ].…”