Analysis and Enhancement of the LoRaWAN Adaptive Data Rate Scheme

Finnegan, Joseph; Farrell, Ronan; Brown, Stephen

doi:10.1109/jiot.2020.2982745

Cited by 58 publications

(62 citation statements)

References 20 publications

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“…In this paper, we performed a comprehensive analysis of solutions that have been developed to optimize ADR algorithms. The proposed optimization techniques address specific challenges such as scalability [ 28 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ], throughput [ 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ] and energy efficiency [ 20 , 46 , 47 , 48 , 49 , 50 , 51 ]. Our analysis distinguished the approaches used and highlighted the challenges and performance in the studies considered.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…The algorithms used to improve scalability in LoRaWAN were discussed in [ 28 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. The strength of the approach in [ 28 ] is that it considers the level of congestion in the network unlike the legacy ADR scheme.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Finnegan et al [ 34 ] presented significant improvements aimed at the end node and gateway that decrease the convergence time for LoRa nodes to attain optimum data rate. They extended the LoRaWAN component in NS-3 by including the ADR, thus allowing the simulation of lifelike LoRaWAN networks by implementing novel improvements in this module.…”

Section: Review Of Adaptive Data Rate Algorithmsmentioning

confidence: 99%

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A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges

Kufakunesu

Hancke

Abu‐Mahfouz

2020

Sensors

136

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Long-Range Wide Area Network (LoRaWAN) is a fast-growing communication system for Low Power Wide Area Networks (LPWAN) in the Internet of Things (IoTs) deployments. LoRaWAN is built to optimize LPWANs for battery lifetime, capacity, range, and cost. LoRaWAN employs an Adaptive Data Rate (ADR) scheme that dynamically optimizes data rate, airtime, and energy consumption. The major challenge in LoRaWAN is that the LoRa specification does not state how the network server must command end nodes pertaining rate adaptation. As a result, numerous ADR schemes have been proposed to cater for the many applications of IoT technology, the quality of service requirements, different metrics, and radio frequency (RF) conditions. This offers a challenge for the reliability and suitability of these schemes. This paper presents a comprehensive review of the research on ADR algorithms for LoRaWAN technology. First, we provide an overview of LoRaWAN network performance that has been explored and documented in the literature and then focus on recent solutions for ADR as an optimization approach to improve throughput, energy efficiency and scalability. We then distinguish the approaches used, highlight their strengths and drawbacks, and provide a comparison of these approaches. Finally, we identify some research gaps and future directions.

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Section: Comparison and Discussionmentioning

confidence: 99%

Section: Comparison and Discussionmentioning

confidence: 99%

Section: Review Of Adaptive Data Rate Algorithmsmentioning

confidence: 99%

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A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges

Kufakunesu

Hancke

Abu‐Mahfouz

2020

Sensors

136

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“…In a class A network, an ED can transmit a UL packet either as confirmed or unconfirmed, using a series of spreading factors (SFs) (7)(8)(9)(10)(11)(12) as the ALOHA channel access mechanism. A UL packet sent as confirmed is required to receive an acknowledgment (ACK) from the NS in a receive window (RX1) with an SF identical to that used in the UL.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the mobility of EDs causes frequent alterations in the topology, which influences the signal strength between EDs and a GW. It was recently realized that the ADR scheme of a LoRaWAN is unsuitable and inadequate when the EDs are mobile because it requires hours to converge to a stable and energy-efficient communication state by adapting an appropriate SF [8]. In addition, the parameters newly modified by NS (i.e., both SF and TP) do not guarantee efficient communication between mobile ED and GW.…”

Section: Introductionmentioning

confidence: 99%

Mobility-Aware Resource Assignment to IoT Applications in Long-Range Wide Area Networks

Farhad

Kim

et al. 2020

IEEE Access

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The long-range wide area network (LoRaWAN) has recently emerged as one of the most potential technologies for the mobile Internet of Things (IoT) applications. In a LoRaWAN, the networkmanaged adaptive data rate (ADR) method is responsible for managing resource allocation (i.e., spreading factor (SF) and transmit power (TP)) to end-devices (EDs) through ADR commands. When an ED is mobile and receives a new configuration (i.e., SF and TP) from the network server, the propagation scenario could have been dramatically changed. Hence, the SF and the link budget will no longer be valid, which results in packet loss and massive retransmissions. Therefore, we propose a mobility-aware resource (SF) assignment scheme (M-ASFA), which aims to allocate the best SF to IoT-enabled mobile EDs at each uplink transmission by considering the strength of the signals that a gateway receives from the EDs. The simulation results demonstrate that, in our proposed M-ASFA solution, the SF is assigned to mobile EDs by proactively responding to the mobility of the EDs. This proactive behavior of the proposed scheme enhances the packet success ratio by significantly reducing the impact of interference, retransmissions, and packet loss when compared with the LoRaWAN-based ADR.

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An Overview on LoRaWAN Technology Simulation Tools

Almuhaya

Jabbar

Sulaiman

et al. 2022

Advances on Intelligent Informatics and Computing

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Low-Power Wide Area Networks (LPWAN) technologies are playing a pivotal role in the IoT applications owing to their capability to meet the keys IoT requirements, i.e., long-range, low cost, small data volumes, massive devices number, and low energy consumption. The creation of new public and private LoRaWAN networks necessitates the use of avoiding node limits and collision prevention measures. Designers of IoT systems confront difficulty in determining the scalability of a given technology, with an emphasis on unlicensed frequency bandwidth (ISM) transmission in densely populated locations. However, picking the best simulation software might be a challenge. To provide a conceptual overview of seven LoRaWAN simulation tools, this paper outlines their key characteristics and the sorts of experiments they support. LoRaWAN simulators, resource utilization, and performance evaluation are all covered in-depth in this report. Furthermore, we classify and compare the most important simulation tools for investigating and analyzing LoRa/LoRaWAN network emulators that have been developed recently. This article will be used to help other researchers decide whether LoRaWAN simulation tool is best for their specific requirements.

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Analysis and Enhancement of the LoRaWAN Adaptive Data Rate Scheme

Cited by 58 publications

References 20 publications

A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges

A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges

Mobility-Aware Resource Assignment to IoT Applications in Long-Range Wide Area Networks

An Overview on LoRaWAN Technology Simulation Tools

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