ArticleSensors (Basel, Switzerland)2020
Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- LSML-SF: a lightweight stacked ML approach for spreading factor allocation in mobile IoT LoRaWAN networks.Frontiers in artificial intelligence · 2026Article
- LoRaWAN Meets ML: A Survey on Enhancing Performance with Machine Learning.Sensors (Basel, Switzerland) · 2023Review
- Adaptive Parameters for LoRa-Based Networks Physical-Layer.Sensors (Basel, Switzerland) · 2023Article
- Resource Management for Massive Internet of Things in IEEE 802.11ah WLAN: Potentials, Current Solutions, and Open Challenges.Sensors (Basel, Switzerland) · 2022Review
- A Fault Tolerant Surveillance System for Fire Detection and Prevention Using LoRaWAN in Smart Buildings.Sensors (Basel, Switzerland) · 2022Article
- RM-ADR: Resource Management Adaptive Data Rate for Mobile Application in LoRaWAN.Sensors (Basel, Switzerland) · 2021Article
- Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2021Article
- Experimental Evaluation of the Availability of LoRaWAN Frequency Channels in the Czech Republic.Sensors (Basel, Switzerland) · 2021Article
- A Survey of Smartphone-Based Indoor Positioning System Using RF-Based Wireless Technologies.Sensors (Basel, Switzerland) · 2020Review
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Authors and funding
4 authors.
Funding
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.
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