LoRa Resource Allocation Algorithm for Higher Data Rates
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...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/518 |
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| author | Hossein Keshmiri Gazi M. E. Rahman Khan A. Wahid |
| author_facet | Hossein Keshmiri Gazi M. E. Rahman Khan A. Wahid |
| author_sort | Hossein Keshmiri |
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| description | 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 (<i>T<sub>tx</sub></i>) 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 <i>T<sub>tx</sub></i> to optimize interference, BR, and image quality. Experimental results show that our proposed algorithm reduces <i>T<sub>tx</sub></i> 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. |
| format | Article |
| id | doaj-art-d125165d51a94312a2b76aec0b81d753 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-d125165d51a94312a2b76aec0b81d7532025-01-24T13:49:12ZengMDPI AGSensors1424-82202025-01-0125251810.3390/s25020518LoRa Resource Allocation Algorithm for Higher Data RatesHossein Keshmiri0Gazi M. E. Rahman1Khan A. Wahid2Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A2, CanadaDepartment of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A2, CanadaDepartment of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A2, CanadaLoRa 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 (<i>T<sub>tx</sub></i>) 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 <i>T<sub>tx</sub></i> to optimize interference, BR, and image quality. Experimental results show that our proposed algorithm reduces <i>T<sub>tx</sub></i> 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.https://www.mdpi.com/1424-8220/25/2/518image transmissionLoRa PHYLoRa MAC layerLoRa modulationSF selection |
| spellingShingle | Hossein Keshmiri Gazi M. E. Rahman Khan A. Wahid LoRa Resource Allocation Algorithm for Higher Data Rates Sensors image transmission LoRa PHY LoRa MAC layer LoRa modulation SF selection |
| title | LoRa Resource Allocation Algorithm for Higher Data Rates |
| title_full | LoRa Resource Allocation Algorithm for Higher Data Rates |
| title_fullStr | LoRa Resource Allocation Algorithm for Higher Data Rates |
| title_full_unstemmed | LoRa Resource Allocation Algorithm for Higher Data Rates |
| title_short | LoRa Resource Allocation Algorithm for Higher Data Rates |
| title_sort | lora resource allocation algorithm for higher data rates |
| topic | image transmission LoRa PHY LoRa MAC layer LoRa modulation SF selection |
| url | https://www.mdpi.com/1424-8220/25/2/518 |
| work_keys_str_mv | AT hosseinkeshmiri loraresourceallocationalgorithmforhigherdatarates AT gazimerahman loraresourceallocationalgorithmforhigherdatarates AT khanawahid loraresourceallocationalgorithmforhigherdatarates |