arrow
Return

LoRa Resource Allocation Algorithm for Higher Data Rates

delete2025-01-17
delete0
delete
OA
AI
H
Hossein Keshmiri *
G
Gazi M. E. Rahman
K
Khan A. Wahid
DOI:10.3390/s25020518delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
LoRa modulation is a widely used technology known for its long-range transmission capabilities, making it ideal for applications with low data rate requirements, such as IoT-enabled sensor networks. However, its inherent low data rate poses a challenge for applications that require higher throughput, such as video surveillance and disaster monitoring, where large image files must be transmitted over long distances in areas with limited communication infrastructure. In this paper, we introduce the LoRa Resource Allocation (LRA) algorithm, designed to address these limitations by enabling parallel transmissions, thereby reducing the total transmission time (Ttx) and increasing the bit rate (BR). The LRA algorithm leverages the quasi-orthogonality of LoRa's Spreading Factors (SFs) and employs specially designed end devices equipped with dual LoRa transceivers, each operating on a distinct SF. For experimental analysis we choose an image transmission application and investigate various parameter combinations affecting Ttx to optimize interference, BR, and image quality. Experimental results show that our proposed algorithm reduces Ttx by 42.36% and 19.98% for SF combinations of seven and eight, and eight and nine, respectively. In terms of BR, we observe improvements of 73.5% and 24.97% for these same combinations. Furthermore, BER analysis confirms that the LRA algorithm delivers high-quality images at SNR levels above -5 dB in line-of-sight communication scenarios.
Keywords:
image transmission
LoRa PHY
LoRa MAC layer
LoRa modulation
SF selection

Journal

Sensors cover
Sensors
IF:
3.5
Papers:
7.2W
Citations:
20.9W

Organization

U
University of Saskatchewan
Scholars:
1.5W
Papers: 1.4W
Citations: 1.7W
Cited Papers

Cited Papers

Cantor: Improving Goodput in LoRa Concurrent Transmission
err2021-02-01
err3
PREAI
errXu, Dan; Chen, Xiaojiang; Zhang, Nannan; Ding, Nana; Zhang, Jing; Fang, Dingyi; Gu, Tao
errShare
errSave
LoRa-MAB: Toward an Intelligent Resource Allocation Approach for LoRaWAN
err2019-12-01
err21
errOAAI
errTa, Duc-Tuyen; Khawam, Kinda; Lahoud, Samer; Adjih, Cedric; Martin, Steven
errShare
errSave
Accelerated atherogenesis in autoimmune rheumatic diseases
err2002-12-01
err0
PREAI
errP.A Bacon; R.J Stevens; D.M Carruthers; S.P Young; G.D Kitas
errShare
errSave
EWS: Exponential Windowing Scheme to Improve LoRa Scalability
err2022-01-01
err6
PREAI
errSaluja, Deepak; Singh, Rohit; Gautam, Sukriti; Kumar, Suman
errShare
errSave
Improving the Capacity of a Mesh LoRa Network by Spreading-Factor-Based Network Clustering
err2019-01-01
err69
errOAAI
errZhu, Guibing; Liao, Chun-Hao; Sakdejayont, Theerat; Lai, I-Wei; Narusue, Yoshiaki; Morikawa, Hiroyuki
errShare
errSave
Internal Cardioversion of Chronic Atrial Fibrillation in Patients
err2006-06-30
err0
PREAI
errROBERTO NERI; PIERO PALERMO; ANTONIO SILVIO CESARIO; DANIELA BARAGLI; ELISABETTA AMICI; GIANCARLO GAMBELLI
errShare
errSave
researcher View more