A Fuzzy-Logic Based Adaptive Data Rate Scheme for Energy-Efficient LoRaWAN Communication

Kufakunesu, Rachel; Hancke, Gerhard P.; Abu‐Mahfouz, Adnan M.

doi:10.3390/jsan11040065

Cited by 7 publications

(5 citation statements)

References 42 publications

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“…The research was focused on using the Xingyun satellite services using patented satellite transceiver names such as LEOBIT. Meanwhile, Rachel et al [11] suggested an ADR algorithm based on fuzzy logic for LoRaWAN gateway systems. Even though that research demonstrated superior performance in terms of energy consumption and transmission power compared to the Semtech ADR scheme, it showed that the Semtech ADR scheme is ideal in terms of energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…In Figure 3, the spherical triangle is redrawn to compute central angle γ subtended by circle arc ES, and the law of cosines is applied to the sides as shown in Equations ( 10) and (11).…”

Section: 𝑡𝑎𝑛𝜃 = 𝑧 𝑥 + 𝑦 (8)mentioning

confidence: 99%

“…In Figure 3, the spherical triangle is redrawn to compute central angle γ subtended by circle arc ES, and the law of cosines is applied to the sides as shown in Equations ( 10) and (11). cosγ = cos(90 − δ) cos(90 − Φg) + sin(90 − δ) sin(90 − Φg) cos∆λ = sinΦ g sinδ + cosΦ g cosδ cos∆λ (10) Slant range can be determined by applying cosine laws to the plane triangle using Equation ( 11), as shown in Figure 4.…”

Section: 𝑡𝑎𝑛𝜃 = 𝑧 𝑥 + 𝑦 (8)mentioning

confidence: 99%

See 2 more Smart Citations

LoRaWAN CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin

Mehdi¹,

Ali²,

Zulkifli³

2023

Aerospace

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One of the challenges of the Internet of Things (IoT) is to provide connectivity to devices around the globe. Long Range (LoRa) is one of the most practical technologies due to its low-power and long-range capabilities to be used by the Low Earth Orbit (LEO) CubeSat. This study aims to evaluate the performance of the Long Range Wide Area Network (LoRaWAN) in receiving uplink messages from ground sensor nodes at an altitude of 550 km with a maximum elevation angle of 0°. An Adaptive Data Rate (ADR) is applied for the dynamic signal reception with respect to receiving signal strength. In this study, the path loss is simulated using attenuation levels from 30–150 dB to ensure that the signal connectivity success rate is at a minimum elevation angle and to perform the link analysis under various conditions. The results show that the LoRaWAN signals are successfully received with an SNR of −21 at the 150 dB attenuation from the end nodes because of its spread-spectrum technique which allows the system to detect signals under the noise floor.

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Section: Related Workmentioning

confidence: 99%

“…In Figure 3, the spherical triangle is redrawn to compute central angle γ subtended by circle arc ES, and the law of cosines is applied to the sides as shown in Equations ( 10) and (11).…”

Section: 𝑡𝑎𝑛𝜃 = 𝑧 𝑥 + 𝑦 (8)mentioning

confidence: 99%

Section: 𝑡𝑎𝑛𝜃 = 𝑧 𝑥 + 𝑦 (8)mentioning

confidence: 99%

See 1 more Smart Citation

LoRaWAN CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin

Mehdi¹,

Ali²,

Zulkifli³

2023

Aerospace

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“…Kufakunesu et al [23] designed a TPC based on a Mandami Fuzzy Interference System that runs in the NS and infers the transmission parameters based on the historical data of the SNR. They reduced the size of the historical SNR data from 20 samples (traditional ADR) to four samples, improving the convergence time.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Assisted Transmission Power Control for LoRaWAN Considering Environments With High Signal- to -Noise Variation

González-Palacio,

Tobón-Vallejo,

Sepúlveda-Cano

et al. 2024

IEEE Access

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To achieve an adequate tradeoff between range and energy efficiency, LoRaWAN End Nodes (ENs) choose their transmission parameters using an Adaptive Data Rate (ADR) scheme based on the maximum value of previous Signal-to-Noise (SNR) values. However, the ADR only performs well in favorable channel conditions. In fact, if the SNR exhibits high variability, these parameters could be inefficiently set and may negatively affect the Packet Delivery Rate (PDR). Therefore, a link margin could be overestimated to improve the PDR by the ADR algorithm, which may, however, waste the EN's energy. This paper proposes a novel ADR that does not rely on the past SNR values. Still, our proposed design directly predicts the current SNR and transmission parameters using Machine Learning. Specifically, the underlying Machine Learning models were trained using in-field measurements for six months in Medellín, Colombia, including different environmental variables and their effects on the SNR. Our ADR scheme improved energy consumption by 47.1% with a PDR of 99% and reduced collisions in dense networks up to 9.5% compared with the ADR scheme. Furthermore, we show that our proposed design outperforms some enhanced versions of the ADR scheme proposed in the literature in both energy consumption and collision rate. Finally, our proposed framework enables simple implementation since it runs directly in the ENs, improving the response time compared with the traditional ADR scheme.

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“…Finally, recently in [ 12 ], the authors extend their previous work [ 8 ] applying a variable-order hidden Markov model to predict the ED mobility pattern in the case of “unknown or undefined trajectories”. Eventually, the recent paper [ 13 ] in Table 1 presents a survey of different ADR algorithms that appeared in the literature.…”

Section: Introductionmentioning

confidence: 99%

Mobility Classification of LoRaWAN Nodes Using Machine Learning at Network Level

Vangelista

Calabrese²,

Cattapan³

2023

Sensors

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LoRaWAN networks rely heavily on the adaptive data rate algorithm to achieve good link reliability and to support the required density of end devices. However, to be effective the adaptive data rate algorithm needs to be tuned according to the level of mobility of each end device. For that purpose, different adaptive data rate algorithms have been developed for the different levels of mobility of end devices, e.g., for static or mobile end devices. In this paper, we describe and evaluate a new and effective method for determining the level of mobility of end devices based on machine learning techniques and specifically on the support vector machine supervised learning method. The proposed method does not rely on the location capability of LoRaWAN networks; instead, it relies only on data always available at the LoRaWAN network server. Moreover, the performance of this method in a real LoRaWAN network is assessed; the results give clear evidence of the effectiveness and reliability of the proposed machine learning approach.

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A Fuzzy-Logic Based Adaptive Data Rate Scheme for Energy-Efficient LoRaWAN Communication

Cited by 7 publications

References 42 publications

LoRaWAN CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin

LoRaWAN CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin

Machine-Learning-Assisted Transmission Power Control for LoRaWAN Considering Environments With High Signal- to -Noise Variation

Mobility Classification of LoRaWAN Nodes Using Machine Learning at Network Level

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