LSML-SF: a lightweight stacked ML approach for spreading factor allocation in mobile IoT LoRaWAN networks.
Saved in:
| Title: | LSML-SF: a lightweight stacked ML approach for spreading factor allocation in mobile IoT LoRaWAN networks. |
|---|---|
| Authors: | Farhad A; Department of Computer Science, Bahria University, Islamabad, Pakistan., Lodhi MA; School of Information Engineering, Yangzhou University, Yangzhou, China., Nisar F; Department of Physical and Numerical Science Qurtaba University & IT, Peshawar, Pakistan., Hadi HJ; Center of Excellence in Cyber Security, Prince Sultan University, Riyadh, Saudi Arabia., Ahmad N; Software Engineering Department, Prince Sultan University, Riyadh, Saudi Arabia., Ladan M; College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia. |
| Source: | Frontiers in artificial intelligence [Front Artif Intell] 2026 Feb 06; Vol. 9, pp. 1704369. Date of Electronic Publication: 2026 Feb 06 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Frontiers Media SA Country of Publication: Switzerland NLM ID: 101770551 Publication Model: eCollection Cited Medium: Internet ISSN: 2624-8212 (Electronic) Linking ISSN: 26248212 NLM ISO Abbreviation: Front Artif Intell Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 2624-8212 |
|---|---|
| DOI: | 10.3389/frai.2026.1704369 |
