[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125816-en":3,"doc-seo-125816-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125816,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Explainable machine learning for LoRaWAN link budget analysis and modeling","This article investigates how explainable artificial intelligence can improve precise planning of LoRa networks by pairing machine learning methods with empirically collected data. A propagation model for LoRaWAN is developed using a decoupled approach for feature extraction and regression, reducing training data burden while enabling interpretability. Comparative results show decision-tree-based gradient boosting delivering the lowest root-mean-squared error of 5.53 dBm. The analysis links signal strength to variables including spreading factor and clutter, supporting more accurate link budget estimation for dense, large-scale IoT deployments.","Explainable machine learning for LoRaWAN link budget analysis and modeling  \nHosseinzadeh, Salaheddin; Ashawa, Moses; Owoh, Nsikak; Larijani, Hadi; Curtis, Krystyna  \nPublished in: Sensors  \nDOI:  \n10.3390/s24030860  \nPublication date:  \n2024  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in ResearchOnline  \nCitation for published version (Harvard):  \nHosseinzadeh, S, Ashawa, M, Owoh, N, Larijani, H & Curtis, K 2024, 'Explainable machine learning for LoRaWAN link budget analysis and modeling', Sensors, vol. 24, no. 3, 860. [https://doi.org/10.3390/s24030860](https://doi.org/10.3390/s24030860)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please view our takedown policy at [https://edshare.gcu.ac.uk/id/eprint/5179 for details](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[ ](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[of how to contact us.](of how to contact us.)  \nDownload date: 08. May. 2024  \n sensors   \nArticle  \nExplainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling  \nSalaheddin Hosseinzadeh *, Moses Ashawa , Nsikak Owoh, Hadi Larijani  and Krystyna Curtis  \nCitation: Hosseinzadeh, S.; Ashawa, M.; Owoh, N.; Larijani, H.; Curtis, K. Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling. Sensors 2024, 24, 860 . [https://doi.org/10.3390/s24030860](https://doi.org/10.3390/s24030860)  \nAcademic Editors: Jiliang Wang, Yuanqing Zheng and Wan Du  \nReceived: 28 December 2023  \nRevised: 19 January 2024  \nAccepted: 25 January 2024  \nPublished: 29 January 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nDepartment of Cybersecurity and Networks, Glasgow Caledonian University, Glasgow G4 0BA, UK;  \n[moses.ashawa@gcu.ac.uk](moses.ashawa@gcu.ac.uk) (M.A.); [krystyna.curtis@gcu.ac.uk](krystyna.curtis@gcu.ac.uk) (K.C.)  \n* Correspondence: [salaheddin.hosseinzadeh@gcu.ac.uk](salaheddin.hosseinzadeh@gcu.ac.uk); Tel.: +44-(0)141-331-8160  \nAbstract: This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN. We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR’s spreading factor changed from 7 to 12 . The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective. The outcome of this work leads toa more accurate estimation and a better understanding of the LoRa’s propagation. Consequently, mitigating the challenges associated with large-scale and dense LoRaWAN deployments, enabling improved link budget analysis, interference management, quality of service, scalability, and energy efficiency of Internet of Thing","cbCairTXMuUHDFkC","https://ap.wps.com/l/cbCairTXMuUHDFkC","pdf",2787964,1,15,"English","en",105,"# Introduction\n## Machine learning for IoT and LoRa networks\n## Planning challenges in dense deployments\n# Proposed approach and modeling\n## Feature extraction and regression decoupling\n## Interpretability and mechanism observation\n# Results and analysis\n## Performance comparison and error metrics\n## Sensitivity to spreading factor\n## Non-linear impact of clutter\n# Implications for link budget and deployment management","[{\"question\":\"What problem does the paper address in LoRaWAN planning?\",\"answer\":\"The work targets precise planning of LoRa networks by improving propagation and link budget estimation using machine learning with collected empirical data.\"},{\"question\":\"How does the proposed modeling approach work?\",\"answer\":\"It decouples feature extraction from regression analysis to facilitate training and improve usability of the resulting propagation model.\"},{\"question\":\"What modeling performance and key findings are reported?\",\"answer\":\"Decision-tree-based gradient boosting achieves the lowest root-mean-squared error (5.53 dBm). The study also reports a 1.5 dBm sensitivity improvement when the spreading factor changes from 7 to 12 and shows clutter effects that are highly non-linear.\"}]","Explainable machine learning for LoRaWAN link budget analysis and modeling | PDF",1785901374,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"explainable-machine-learning-for-lorawan-link-budget-analysis-and-modeling","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/explainable-machine-learning-for-lorawan-link-budget-analysis-and-modeling/125816/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in LoRaWAN planning?","Question",{"text":75,"@type":76},"The work targets precise planning of LoRa networks by improving propagation and link budget estimation using machine learning with collected empirical data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed modeling approach work?",{"text":80,"@type":76},"It decouples feature extraction from regression analysis to facilitate training and improve usability of the resulting propagation model.",{"name":82,"@type":73,"acceptedAnswer":83},"What modeling performance and key findings are reported?",{"text":84,"@type":76},"Decision-tree-based gradient boosting achieves the lowest root-mean-squared error (5.53 dBm). 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