ResearchSpace

Empirical analysis of LoRaWAN-based adaptive data rate algorithms

Show simple item record

dc.contributor.author Charles, L
dc.contributor.author Isong, B
dc.contributor.author Lugayizi, F
dc.contributor.author Abu-Mahfouz, Adnan MI
dc.date.accessioned 2022-01-24T06:56:47Z
dc.date.available 2022-01-24T06:56:47Z
dc.date.issued 2021-10
dc.identifier.citation Charles, L., Isong, B., Lugayizi, F. & Abu-Mahfouz, A.M. 2021. Empirical analysis of LoRaWAN-based adaptive data rate algorithms. http://hdl.handle.net/10204/12230 . en_ZA
dc.identifier.isbn 978-1-6654-3554-3
dc.identifier.isbn 978-1-6654-0256-9
dc.identifier.uri DOI: 10.1109/IECON48115.2021.9589038
dc.identifier.uri http://hdl.handle.net/10204/12230
dc.description.abstract Long Range Wide Area Networking (LoRaWAN) has established itself as one of the leading Media Access Control (MAC) layer protocols in the realm of Low Power Wide Area Networks (LPWAN). Although the technology itself is quite mature, the resource allocation mechanism, the Adaptive Data Rate (ADR) algorithm it uses is still quite new, unspecified and its functionalities still limited. Various studies have shown that the performance of the ADR algorithm gradually suffers in dense networks. As such, studies and proposals have been made as attempts to improve the algorithm. In this paper, the authors chose four proposed algorithms that focused on improving the ADR in terms of data extraction rate (DER) and evaluated them to study and critically analyze their performances. LoRaSim was used and the algorithms were employed in a simple sensing application that involved end devices transmitting data to the gateway every hour. The performances were measured based on how they affected DER as the network size increases. The results obtained show that the implemented algorithms outperformed the ADR algorithm. However, as network size increases, these superior performances are not adequate for a reliable and energy-efficient LoRaWAN network. Though attempts have been made to improve the ADR algorithm, arriving at its ideal implementation is still an open research area and therefore, we recommend more improvement should be proposed. en_US
dc.format Abstract en_US
dc.language.iso en en_US
dc.relation.uri https://ieeexplore.ieee.org/abstract/document/9589038 en_US
dc.source IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, Toronto, ON, Canada, 13-16 October 2021 en_US
dc.subject Adaptive data rate en_US
dc.subject ADR en_US
dc.subject Low Power Wide Area Networks en_US
dc.subject LPWAN en_US
dc.subject Long Range Wide Area Networking en_US
dc.subject LoRaWAN en_US
dc.subject Spreading factors en_US
dc.subject Transmission power en_US
dc.title Empirical analysis of LoRaWAN-based adaptive data rate algorithms en_US
dc.type Conference Presentation en_US
dc.description.pages 7 en_US
dc.description.note Copyright: 2021 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website. en_US
dc.description.cluster Next Generation Enterprises & Institutions en_US
dc.description.impactarea EDTRC Management en_US
dc.identifier.apacitation Charles, L., Isong, B., Lugayizi, F., & Abu-Mahfouz, A. M. (2021). Empirical analysis of LoRaWAN-based adaptive data rate algorithms. http://hdl.handle.net/10204/12230 en_ZA
dc.identifier.chicagocitation Charles, L, B Isong, F Lugayizi, and Adnan MI Abu-Mahfouz. "Empirical analysis of LoRaWAN-based adaptive data rate algorithms." <i>IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, Toronto, ON, Canada, 13-16 October 2021</i> (2021): http://hdl.handle.net/10204/12230 en_ZA
dc.identifier.vancouvercitation Charles L, Isong B, Lugayizi F, Abu-Mahfouz AM, Empirical analysis of LoRaWAN-based adaptive data rate algorithms; 2021. http://hdl.handle.net/10204/12230 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Charles, L AU - Isong, B AU - Lugayizi, F AU - Abu-Mahfouz, Adnan MI AB - Long Range Wide Area Networking (LoRaWAN) has established itself as one of the leading Media Access Control (MAC) layer protocols in the realm of Low Power Wide Area Networks (LPWAN). Although the technology itself is quite mature, the resource allocation mechanism, the Adaptive Data Rate (ADR) algorithm it uses is still quite new, unspecified and its functionalities still limited. Various studies have shown that the performance of the ADR algorithm gradually suffers in dense networks. As such, studies and proposals have been made as attempts to improve the algorithm. In this paper, the authors chose four proposed algorithms that focused on improving the ADR in terms of data extraction rate (DER) and evaluated them to study and critically analyze their performances. LoRaSim was used and the algorithms were employed in a simple sensing application that involved end devices transmitting data to the gateway every hour. The performances were measured based on how they affected DER as the network size increases. The results obtained show that the implemented algorithms outperformed the ADR algorithm. However, as network size increases, these superior performances are not adequate for a reliable and energy-efficient LoRaWAN network. Though attempts have been made to improve the ADR algorithm, arriving at its ideal implementation is still an open research area and therefore, we recommend more improvement should be proposed. DA - 2021-10 DB - ResearchSpace DP - CSIR J1 - IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, Toronto, ON, Canada, 13-16 October 2021 KW - Adaptive data rate KW - ADR KW - Low Power Wide Area Networks KW - LPWAN KW - Long Range Wide Area Networking KW - LoRaWAN KW - Spreading factors KW - Transmission power LK - https://researchspace.csir.co.za PY - 2021 SM - 978-1-6654-3554-3 SM - 978-1-6654-0256-9 T1 - Empirical analysis of LoRaWAN-based adaptive data rate algorithms TI - Empirical analysis of LoRaWAN-based adaptive data rate algorithms UR - http://hdl.handle.net/10204/12230 ER - en_ZA
dc.identifier.worklist 25217 en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record