Adaptive Data Rate for Multiple Gateways LoRaWAN Networks

Coutaud, Ulysse; Heusse, Martin; Tourancheau, Bernard

doi:10.1109/wimob50308.2020.9253425

Cited by 11 publications

(11 citation statements)

References 12 publications

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“…In order to evaluate the performance of the considered RFTs in the presence of errors, two different error distributions are supported in Sim-RFT: a uniform error distribution [28]- [57], and a burst error distribution [34]- [50]. These error distributions cover a comprehensive set of characteristics of LoRaWAN networks such as frame loss over distance [31], [33], [34], [37], [41], [51]- [53], different uses cases [31], [35], [54]- [57], mobile or stationary devices [33], [46], [49], [50], network capacity [54], and collisions [33], [53]. Frame loss burstiness may be due to channel effects [34], [36], mobility [34], [35], limited coverage [38], [40], [41], [44], or opportunistic coverage [42]- [44].…”

Section: Error Patterns and Ratesmentioning

confidence: 99%

“…These error distributions cover a comprehensive set of characteristics of LoRaWAN networks such as frame loss over distance [31], [33], [34], [37], [41], [51]- [53], different uses cases [31], [35], [54]- [57], mobile or stationary devices [33], [46], [49], [50], network capacity [54], and collisions [33], [53]. Frame loss burstiness may be due to channel effects [34], [36], mobility [34], [35], limited coverage [38], [40], [41], [44], or opportunistic coverage [42]- [44]. Event-driven communication may also lead to burst errors, as many devices may try to communicate at the same time during a relatively long time interval [45]- [48].…”

Section: Error Patterns and Ratesmentioning

confidence: 99%

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Evaluation of Receiver-Feedback Techniques for Fragmentation Over LPWANs

Aguilar

Vidal

Gómez

2022

IEEE Internet Things J.

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The Internet Engineering Task Force (IETF) has standardized a new framework for IPv6 support over Low Power Wide Area Networks (LPWANs), called Static Context Header Compression and Fragmentation (SCHC). SCHC includes acknowledgment (ACK)-based mechanisms for reliable fragmented packet transmission. For the latter, SCHC defines a Receiver-Feedback Technique (RFT), called Compressed Bitmap (CB), by which a receiver reports to the sender whether the fragments carrying a packet have been received or not. Such information is carried as ACK payload. Considering the extraordinary frame size and message rate constraints of LPWANs, ACK payload size becomes crucial. In this paper, we compare the performance of CB with that of several alternative RFTs, namely List of Lost Fragments (LLF), List of Deltas (LoD), and Uncompressed Bitmap (UB), where the latter is used as a benchmark. We evaluate the considered RFTs in terms of ACK size, number of Layer 2 (L2) frames needed to carry an ACK, and ACK Time on Air. Our analysis shows that the use of RFTs different from CB offers significant performance improvement in many scenarios. Furthermore, we provide guidance on which RFT should be used for different packet sizes, error rates and error patterns.

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Section: Error Patterns and Ratesmentioning

confidence: 99%

Section: Error Patterns and Ratesmentioning

confidence: 99%

Evaluation of Receiver-Feedback Techniques for Fragmentation Over LPWANs

Aguilar

Vidal

Gómez

2022

IEEE Internet Things J.

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“…Marini et al [51] proposed a collision-aware adaptive data rate algorithm for LoRaWAN, which aims at minimizing the collision probability when assigning data rates while keeping the link-level performance under control. Coutaud et al [52] proposed an ADR proposal for multi-gateway LoRaWAN Networks. The work relies on the accurate estimation of the effective channel, dynamically adapts to the number of gateways and exploits the macro-diversity.…”

Section: Related Workmentioning

confidence: 99%

Towards Energy-Fairness in LoRa Networks

Gao

Zhao

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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LoRa has become one of the most promising networking technologies for Internet-of-Things applications. Distant end devices have to use a low data rate to reach a LoRa gateway, causing long in-the-air transmission time and high energy consumption. Compared with the end devices using high data rates, they will drain the batteries much earlier and the network may be broken early. Such an energy unfairness can be mitigated by deploying more gateways. However, with more gateways, more end devices may choose small spreading factors to reach closer gateways, increasing the collision probability. In this paper, we propose a networking solution for LoRa networks, namely EF-LoRa, that can achieve energy fairness among end devices by carefully allocating network resources, including frequency channels, spreading factors and transmission power. We develop a LoRa network model to study the energy consumption of the end devices, considering the unique features of LoRa networks such as the LoRaWAN MAC protocol and capacity limitation of a gateway. We formulate the energy fairness allocation as an optimization problem and propose a greedy allocation algorithm to achieve max-min fairness of energy efficiency. Extensive simulation results show that EF-LoRa can improve the energy fairness by 177.8%, compared to the stateof-the-art solutions.

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“…ADR TTN relies on the EDs loss of connectivity denoted by the lack of downlinks (96 by default) to increase the SF and thus regain connectivity. More details of the ADR TTN algorithm can be found in previous works [12,13].…”

mentioning

confidence: 99%

LoRa Channel Characterization for Flexible and High Reliability Adaptive Data Rate in Multiple Gateways Networks

Coutaud¹,

Heusse²,

Tourancheau³

2021

Computers

Self Cite

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We characterize the LoRa channel in terms of multi-path fading, loss burstiness, and assess the benefits of Forward Error Correction as well as the influence of frame length. We make these observations by synthesizing extensive experimental measurements realized with The Things Network in a medium size city. We then propose to optimize the LoRaWAN Adaptive Data Rate algorithm based on this refined LoRa channel characterization and taking into account the LoRaWAN inherent macro-diversity from multi-gateway reception. Firstly, we propose ADRopt, which adjusts Spreading Factor and frame repetition number to maintain the communication below a target Packet Error Rate ceiling with optimized Time-On-Air. Secondly, we propose ADRIFECC, an extension of ADRopt in case an Inter-Frame Erasure Correction Code is available. The resulting protocol provides very high reliability even over low quality channels, with comparable Time on Air and similar downlink usage as the currently deployed mechanism. Simulations corroborate the analysis, both over a synthetic random wireless link and over replayed real-world packet transmission traces.

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Adaptive Data Rate for Multiple Gateways LoRaWAN Networks

Cited by 11 publications

References 12 publications

Evaluation of Receiver-Feedback Techniques for Fragmentation Over LPWANs

Evaluation of Receiver-Feedback Techniques for Fragmentation Over LPWANs

Towards Energy-Fairness in LoRa Networks

LoRa Channel Characterization for Flexible and High Reliability Adaptive Data Rate in Multiple Gateways Networks

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