Version 1
: Received: 24 July 2024 / Approved: 25 July 2024 / Online: 25 July 2024 (12:00:01 CEST)
How to cite:
Tempiem, P.; Silapunt, R. Enhancing Spreading Factor Assignment in LoRaWAN with a Geometric Distribution Approach for Practical Node Distributions. Preprints2024, 2024072033. https://doi.org/10.20944/preprints202407.2033.v1
Tempiem, P.; Silapunt, R. Enhancing Spreading Factor Assignment in LoRaWAN with a Geometric Distribution Approach for Practical Node Distributions. Preprints 2024, 2024072033. https://doi.org/10.20944/preprints202407.2033.v1
Tempiem, P.; Silapunt, R. Enhancing Spreading Factor Assignment in LoRaWAN with a Geometric Distribution Approach for Practical Node Distributions. Preprints2024, 2024072033. https://doi.org/10.20944/preprints202407.2033.v1
APA Style
Tempiem, P., & Silapunt, R. (2024). Enhancing Spreading Factor Assignment in LoRaWAN with a Geometric Distribution Approach for Practical Node Distributions. Preprints. https://doi.org/10.20944/preprints202407.2033.v1
Chicago/Turabian Style
Tempiem, P. and Rardchawadee Silapunt. 2024 "Enhancing Spreading Factor Assignment in LoRaWAN with a Geometric Distribution Approach for Practical Node Distributions" Preprints. https://doi.org/10.20944/preprints202407.2033.v1
Abstract
This paper proposes the GD (Geometric Distribution) algorithm, a novel approach to enhance the default Adaptive Data Rate (ADR) mechanism in the Long Range Wide Area Network (LoRaWAN). By leveraging the Probability Mass Function (PMF) of the GD model, the GD algorithm effectively addresses massive device distribution challenges in real-life scenarios. To evaluate the algorithm's performance, the LoRaWAN simulations were conducted using the fixed node pattern derived from actual locations of dairy farms in Ratchaburi province, Thailand. The research established scenarios for assessing the network's performance namely, DER (Data Extraction Rate) and SF (Spreading Factor) assignment. Comparative analyses were performed against the uniform random node pattern and established algorithms, including the default ADR scheme, EXPLoRa, Quantile Classification of Variance from the Mean (QCVM), and Standard Deviation (SD). The GD algorithm demonstrated significant improvements over existing methodologies for both fixed and uniform random patterns. The fixed pattern exhibited an enhancement of 14.3%, while the uniform random pattern showed 4.8% enhancement over the default ADR scheme. Further assessments covered the coverage area, payload size, and energy consumption. The GD algorithm consistently achieved the optimal DER values with a coverage area and payload size, albeit often at the expense of increased energy consumption.
Keywords
LoRaWAN; Internet of Things; Adaptive Data Rate; Smart dairy farm
Subject
Engineering, Telecommunications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
