Performance of LoRaWAN for Handling Telemetry and Alarm Messages in Industrial Applications

Filho, Francisco Helder C. dos S.; Dester, Plínio S.; Stancanelli, Elvis M. G.; Cardieri, Paulo; Nardelli, Pedro H. J.; Carrillo, Dick; Alves, Hirley

doi:10.3390/s20113061

Cited by 15 publications

(12 citation statements)

References 23 publications

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“…Then the weaker the signal produced, accordingly, the minimum RSSI is -120 dBm, so the greater the Spreading Factor (Fig. 2) [19], the weaker the signal produced.…”

Section: Resultsmentioning

confidence: 97%

Performance Evaluation of LoRa ES920LR 920 MHz on the Development Board

Adi¹,

Kitagawa²

2020

IJACSA

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This study contains the LoRa ES920LR test on obstruction or resistance conditions and its comparison with Free Space Path Loss (FSPL) using Drone means without obstacles. The ES920LR has 920 MHz frequency channel settings, 125 kHz Bandwidth, and SF 7-12, with 13 dBm Output Power. Comes with Sleep mode operation with Command Prompt based settings. The development board used is a leafony board, Leafony board is a board with a small size that is compatible with Arduino IDE, using a micro ATmega M328P microcontroller, this board is mounted tiled with complete facilities, e.g., a power supply board, four different sensor boards, MCU boards, communication boards e.g., WiFi and Bluetooth, specifically in this article using the LoRa ES920LR on the Leafony board, using LoRa because it requires a long range (km) and low power, and expansion boards that can be developed, Expansion to Leafony boards is expected to reduce the power consumption of the sensor node, lifetime, lif, small size, and lightweight. Furthermore, this algorithm is used to optimize LoRa Coverage and LoRa Lifetime. The results of receiving the FSPL LoRa 920LR have a Power Receiver (Pr) of 30 dB at a distance of 1 meter or A, at 500 meters 85 dB, 1500 meters 95 dB, 2.5km 100 dB. Attenuation is caused by distance, although not significant, other factors are obstacles or obstacles, bad weather (rain, snow).

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“…Then the weaker the signal produced, accordingly, the minimum RSSI is -120 dBm, so the greater the Spreading Factor (Fig. 2) [19], the weaker the signal produced.…”

Section: Resultsmentioning

confidence: 97%

Performance Evaluation of LoRa ES920LR 920 MHz on the Development Board

Adi¹,

Kitagawa²

2020

IJACSA

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“…In particular, it has been deployed in waste management in North Korea, solar power plant management in the United States of America, power usage monitoring in France, smart meters in Germany, a smart golf course in Canada and smart islands in Spain [6]. However, as observed by Filho et al [2] and Buurman et al [8], it has yet to be deployed in mining, deep underground, deep underwater and in space. Also noteworthy is what the investigation by Sundaram et al [6] brings out, which is that the technology still has challenges in four areas; namely, link coordination, resource allocation, reliable transmission and security.…”

Section: Background 21 Reliability Of Lorawan Communication In Mining...mentioning

confidence: 99%

“…Mining companies require data harvesting technologies that are more effective, work within the field and are faster and more robust. This includes wireless communication solutions that are not disruptive to mining operations and are less costly, as well as novel wireless underground communication techniques for the IoT to support real-time collection of data for decision making [2].…”

Section: Introductionmentioning

confidence: 99%

“…The former are regular, non-emergency traffic generated from constant monitoring and measurements, tracking miners and equipment, while the latter are emergency traffic that occur occasionally as a result of sudden and drastic changes in mine conditions or a safety alarm sent by one of the miners. Regular, non-emergency traffic is not delay-sensitive or reliability-constrained, while emergency traffic requires high reliability, low latency and high throughput [2,4].…”

Section: Introductionmentioning

confidence: 99%

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Reliability of LoRaWAN Communications in Mining Environments: A Survey on Challenges and Design Requirements

Musonda,

Ndiaye,

Libati

et al. 2024

JSAN

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While a robust and reliable communication network for monitoring the mining environment in a timely manner to take care of people, the planet Earth and profits is key, the mining environment is very challenging in terms of achieving reliable wireless transmission. This survey therefore investigates the reliability of LoRaWAN communication in the mining environment, identifying the challenges and design requirements. Bearing in mind that LoRaWAN is an IoT communication technology that has not yet been fully deployed in mining, the survey incorporates an investigation of LoRaWAN and other mining IoT communication technologies to determine their records of reliability, strengths and weaknesses and applications in mining. This aspect of the survey gives insight into the requirements of future mining IoT communication technologies and where LoRaWAN can be deployed in both underground and surface mining. Specific questions that the survey addresses are: (1) What is the record of reliability of LoRaWAN in mining environments? (2) What contributions have been made with regard to LoRa/LoRaWAN communication in general towards improving reliability? (3) What are the challenges and design requirements of LoRaWAN reliability in mining environments? (4) What research opportunities exist for achieving LoRaWAN communication in mining environments? In addition to recommending open research opportunities, the lessons learnt from the survey are also outlined.

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“…Many protocols for LPWAN exist including long-range wide area network (LoRaWAN) [2], SigFox [3], and NB-IoT. LoRaWAN is one of the most promising candidates for IoT, attracting more attention and can support many applications such as electricity metering, localization, and industrial applications [4][5].…”

Section: Introductionmentioning

confidence: 99%

MIX-MAB: Reinforcement Learning-based Resource Allocation Algorithm for LoRaWAN

Azizi¹,

Teymuri²,

Aslani³

et al. 2022

Preprint

Self Cite

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This paper focuses on improving the resource allocation algorithm in terms of packet delivery ratio (PDR), i.e., the number of successfully received packets sent by end devices (EDs) in a long-range wide-area network (LoRaWAN). Setting the transmission parameters significantly affects the PDR. Employing reinforcement learning (RL), we propose a resource allocation algorithm that enables the EDs to configure their transmission parameters in a distributed manner. We model the resource allocation problem as a multi-armed bandit (MAB) and then address it by proposing a two-phase algorithm named MIX-MAB, which consists of the exponential weights for exploration and exploitation (EXP3) and successive elimination (SE) algorithms. We evaluate the MIX-MAB performance through simulation results and compare it with other existing approaches. Numerical results show that the proposed solution performs better than the existing schemes in terms of convergence time and PDR.

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Performance of LoRaWAN for Handling Telemetry and Alarm Messages in Industrial Applications

Cited by 15 publications

References 23 publications

Performance Evaluation of LoRa ES920LR 920 MHz on the Development Board

Performance Evaluation of LoRa ES920LR 920 MHz on the Development Board

Reliability of LoRaWAN Communications in Mining Environments: A Survey on Challenges and Design Requirements

MIX-MAB: Reinforcement Learning-based Resource Allocation Algorithm for LoRaWAN

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