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.
Highlights
Low-power wide-area networks (LPWANs) are a growing technology that target static and mobile Internet of Things (IoT) applications requiring long-range, energy-efficient, and low data rate communication [1]
The typical adaptive data rate (ADR) suffers from a massive packet loss caused by interference and packets arriving under the pre-defined sensitivity at the GW
We present a comprehensive performance assessment of the proposed schemes, which are examined in comparison with typical ADR and ADR+ [20] in terms of the convergence period, packet success ratio (PSR), and energy consumption in both static and mobility scenarios in a confirmed mode
Summary
Low-power wide-area networks (LPWANs) are a growing technology that target static and mobile Internet of Things (IoT) applications requiring long-range, energy-efficient, and low data rate communication [1]. The typical ADR suffers from a high convergence period, owing to the time-consuming process of both ED- and NS-managed ADRs. The typical ADR scheme requires several hours to days to converge into a stable and energy-efficient communication state when the NS starts monitoring the M packets. In order to reduce the convergence period and improve the PSR (for both static and mobile IoT EDs) in a confirmed mode, we propose two NS-managed ADRs and claim the following contributions. By employing a Gaussian filter, G-ADR can optimally find both SF and TP parameters, which results in a reduced convergence period and improved PSR. We propose another NS-managed ADR based on the exponential moving average (EMA-ADR).
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