A Survey of LoRaWAN Adaptive Data Rate Algorithms for Possible Optimization

Lehong, Charles; Isong, Bassey; Lugayizi, Francis; Abu‐Mahfouz, Adnan M.

doi:10.1109/imitec50163.2020.9334144

Cited by 9 publications

(13 citation statements)

References 12 publications

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“…ADR is an algorithm employed in the LoRaWAN network to optimize the transmission parameters for stationery LoRa end devices [4]. The LoRaWAN provides the transmission parameters spreading factor (SF), bandwidth (BW), transmission power (TP), and coding rate (CR), that the ADR can manipulate to optimize the end devices' data rates, energy consumptions, airtimes and the capacity of the network [4]. The specifications of the ADR are provided in detail in [10].…”

Section: Adaptive Date Rate Overviewmentioning

confidence: 99%

“…Despite presenting itself as one of the most desirable network technologies in the LPWAN space, one of LoRaWAN's biggest limitations is that it operates in the freely available Industrial, Scientific and Medical (ISM) bands [4], which it has to share with other technologies while at the same time adhering to duty cycle regulations in those bands. Furthermore, LoRaWAN networks are often deployed in very challenging networking conditions.…”

Section: Introductionmentioning

confidence: 99%

“…These findings have received significant attention from academia: numerous studies and proposals have been produced as potential solutions to this problem. These solutions have been critically studied and summarized by the authors in [4]. The authors showed that these solutions have constantly brought improvements to the ADR.…”

Section: Introductionmentioning

confidence: 99%

“…However, the authors also have argued that these improvements are still not adequate and that arriving at an ideal implementation of the ADR remains an open research area. Therefore, in this paper, the researchers used the knowledge gained from studying the findings in [4] to design an enhanced version of the ADR that uses congestion statuses of spreading factors to determine optimized data rates for end devices. The proposed algorithm was then evaluated in a series of simulated experiments and its performance was compared against four other algorithms with the same characteristics from the papers [7][8][9] in terms of data extraction rate (DER) and energy consumption (EC) [4].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in this paper, the researchers used the knowledge gained from studying the findings in [4] to design an enhanced version of the ADR that uses congestion statuses of spreading factors to determine optimized data rates for end devices. The proposed algorithm was then evaluated in a series of simulated experiments and its performance was compared against four other algorithms with the same characteristics from the papers [7][8][9] in terms of data extraction rate (DER) and energy consumption (EC) [4]. The evaluation results show that the proposed algorithm achieves at least 40 per cent better DER performance and at least 100 per cent better EC performance over the other four algorithms.…”

Section: Introductionmentioning

confidence: 99%

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A Spreading Factor Congestion Status-Aware Adaptive Data Rate Algorithm

Lehong

Isong

Lugayizi

et al. 2021

JSAN

Self Cite

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LoRaWAN has established itself as one of the leading MAC layer protocols in the field of LPWAN. Although the technology itself is quite mature, its resource allocation mechanism, the Adaptive Data Rate (ADR) algorithm is still quite new, unspecified and its functionalities still limited. Various studies have shown that the performance of the ADR algorithm gradually suffers in dense networks. Recent studies and proposals have been made as attempts to improve the algorithm. In this paper, the authors proposed a spreading factor congestion status aware ADR version and compared its performance against that of four other related algorithms to study the performance improvements the algorithm brings to LoRaWAN in terms of DER and EC. LoRaSim was used to evaluate the algorithms’ performances in a simple sensing application that involved end devices transmitting data to the gateway every hour. The performances were measured based on how they affected DER as the network size increases. The results obtained show that the proposed algorithm outperforms the currently existing implementations of the ADR in terms of both DER and EC. However, the proposed algorithm is slightly outperformed by the native ADR in terms of EC. This was expected as the algorithm was mainly built to improve DER. The proposed algorithm builds on the existing algorithms and the ADR and significantly improves them in terms of DER and EC (excluding the native ADR), which is a significant step towards an ideal implementation of LoRaWAN’s ADR.

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Section: Adaptive Date Rate Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Spreading Factor Congestion Status-Aware Adaptive Data Rate Algorithm

Lehong

Isong

Lugayizi

et al. 2021

JSAN

Self Cite

View full text Add to dashboard Cite

show abstract

The error rate analyze and parameter measurement on LoRa communication for health monitoring

Adi

Wahyu

2023

Microprocessors and Microsystems

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Intelligent Resource Allocation in LoRaWAN Using Machine Learning Techniques

et al. 2023

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With the ubiquitous growth of Internet-of-things (IoT) devices, current low-power widearea network (LPWAN) technologies will inevitably face performance degradation due to congestion and interference. The rule-based approaches to assign and adapt the device parameters are insufficient in dynamic massive IoT scenarios. For example, the adaptive data rate (ADR) algorithm in LoRaWAN has been proven inefficient and outdated for large-scale IoT networks. Meanwhile, new solutions involving machine learning (ML) and reinforcement learning (RL) techniques are shown to be very effective in solving resource allocation in dense IoT networks. In this article, we propose a new concept of using two independent learning approaches for allocating spreading factor (SF) and transmission power to the devices using a combination of a decentralized and centralized approach. SF is allocated to the devices using RL for contextual bandit problem, while transmission power is assigned centrally by treating it as a supervised ML problem. We compare our approach with existing state-of-the-art algorithms, showing a significant improvement in both network level goodput and energy consumption, especially for large and highly congested networks.

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A Survey of LoRaWAN Adaptive Data Rate Algorithms for Possible Optimization

Cited by 9 publications

References 12 publications

A Spreading Factor Congestion Status-Aware Adaptive Data Rate Algorithm

A Spreading Factor Congestion Status-Aware Adaptive Data Rate Algorithm

The error rate analyze and parameter measurement on LoRa communication for health monitoring

Intelligent Resource Allocation in LoRaWAN Using Machine Learning Techniques

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