The relation of LoRaWAN efficiency with energy consumption of sensor node

Pukrongta, Nisit; Kumkhet, Boonyarit

doi:10.1109/icpei47862.2019.8945016

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…The fundamental element of LoRaWAN communication is the spreading factor (SF), which uses an encoding technique to divide a bit into several chips. Each of the SF values, which range from 7 to 12, depends on the quantity of chips [ 39 ]. The percentages of received traffic allocated through the spreading factors (SFs) were displayed in various areas with and without water leakage.…”

Section: Results and Discussionmentioning

confidence: 99%

LoRaWAN Performance Analysis for a Water Monitoring and Leakage Detection System in a Housing Complex

Ghamdi

Khairullah

Mojamed

2022

Sensors

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The automation of water leakage detection and monitoring systems has recently been made possible by the Internet of Things (IoT). However, the high cost is an obstacle when applying a network over a large area. The Low-Power Wide-Area Network (LPWAN) was created specifically to address long-range IoT applications. The Long-Range Wide-Area Network (LoRaWAN) is one of the most common LPWANs. In this study, a method for monitoring and detecting water leakage in a housing complex was tested using LoRaWAN. Water leakage was detected using a low-pressure system model comprising a water meter, presser sensor, and smart valve within a LoRa node. This study investigates the use of LoRaWAN for water monitoring and leakage detection by implementing a comprehensive case study to identify LoRaWAN’s feasibility, reliability, and scalability for water monitoring and leakage detection in simulated scenarios. The housing complex varied in size and number of nodes. The LoRaWAN was evaluated by the FloRa simulator package through the Objective Modular Network Testbed (OMNeT++) platform. The results indicated that it was an efficient means of water monitoring and leakage detection in housing complexes.

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Section: Results and Discussionmentioning

confidence: 99%

LoRaWAN Performance Analysis for a Water Monitoring and Leakage Detection System in a Housing Complex

Ghamdi

Khairullah

Mojamed

2022

Sensors

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“…Our research utilizes these IoT-based ground measurements [30] to gain insights into the interplay between soil properties, environmental variables, and plant growth dynamics. Specifically, we develop IoT devices tailored for measuring soil parameters, including pH, NPK levels, EC, moisture, and location with low-power transmission LoRaWAN technique [31].…”

Section: Optimal Iot Design and Ground Measurementsmentioning

confidence: 99%

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Pukrongta,

Taparugssanagorn,

Sangpradit

2024

Preprint

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This paper presents the PEnsemble 4 model, a sophisticated machine learning framework that integrates IoT-based environmental data to accurately forecast maize yield. With the projected significant growth in global maize demand over the next decade, the inherent risks posed by the crop’s dependence on weather conditions necessitate improved prediction capabilities. The PEnsemble 4 model, developed with high accuracy, incorporates comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the PEnsemble 4 model effectively analyzes temporal patterns in vegetation indices, specifically CIre and NDRE, which serve as reliable indicators of canopy density and plant height. In addition, this research significantly contributes to precision agriculture by offering an efficient and sustainable alternative to conventional farming practices through precise yield predictions. Notably, the PEnsemble 4 model enables earlier estimation, advancing the timeline for yield prediction from the conventional day 100 in the R6 stage to day 79 in the R2 stage. This improvement enhances decision-making processes in farming operations. The remarkable accuracy rate of 91% underscores the importance of adopting a multifaceted data approach that harnesses IoT-derived environmental insights. Additionally, the PEnsemble 4 model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. Ultimately, the PEnsemble 4 model establishes a new standard in maize yield prediction, revolutionizing crop management and protection through the synergistic utilization of IoT and machine learning technologies.

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“…Several papers have modeled the energy performance of LoRaWAN by means of mathematical analysis [6]- [10], [12]- [20], whereas a few others have studied it by using simulation [11], [18]. Impact of packet size on energy performance has only been considered in some of the analytical studies [10], [12]- [14], [17], [19]. The latter works are briefly reviewed next.…”

Section: Related Workmentioning

confidence: 99%

“…EPB has been analytically modeled in the literature, for different frequency bands, and for different ranges of LoRaWAN packet sizes: Between 10 and 35 bytes [12], between 10 and 40 bytes [14], and between 1 and 51 bytes [13]. According to these works, EPB decreases monotonically with packet size, for different SFs and CRs.…”

Section: Related Workmentioning

confidence: 99%

Understanding the impact of packet size on the energy efficiency of LoRaWAN

Casals,

Gomez,

Vidal

2023

J. Commun. Netw.

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LoRaWAN has become a flagship LPWAN technology, and one of the main connectivity alternatives for IoT devices. Since LoRaWAN was designed for low energy consumption, it is fundamental to understand its energy performance. In this paper, we study the impact of packet size on LoRaWAN device energy consumption per delivered data bit (EPB). By means of extensive simulations, we show that, when network performance is very high or very low, EPB decreases steadily with packet size; otherwise, EPB may show an "asymmetric U" shape as a function of packet size, with a minimum EPB value that is achieved for a medium packet size. We also provide detailed insights on the reasons that produce the observed behaviors.

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The relation of LoRaWAN efficiency with energy consumption of sensor node

Cited by 10 publications

References 6 publications

LoRaWAN Performance Analysis for a Water Monitoring and Leakage Detection System in a Housing Complex

LoRaWAN Performance Analysis for a Water Monitoring and Leakage Detection System in a Housing Complex

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Understanding the impact of packet size on the energy efficiency of LoRaWAN

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