{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T02:05:07Z","timestamp":1780625107215,"version":"3.54.1"},"reference-count":23,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Long-Range Wide Area Network (LoRaWAN) is an open-source protocol for the standard Internet of Things (IoT) Low Power Wide Area Network (LPWAN). This work\u2019s focal point is the LoRa Multi-Armed Bandit decentralized decision-making solution. The contribution of this paper is to study the effect of the re-learning EXP3 Multi-Armed Bandit (MAB) algorithm with previous experts\u2019 advice on the LoRaWAN network performance. LoRa smart node has a self-managed EXP3 algorithm for choosing and updating the transmission parameters based on its observation. The best parameter choice needs previously associated distribution advice (expert) before updating different choices for confidence. The paper proposes a new approach to study the effects of combined expert distribution for each transmission parameter on the LoRaWAN network performance. The successful transmission of the packet with optimized power consumption is the pivot of this paper. The validation of the simulation result has proven that combined expert distribution improves LoRaWAN network\u2019s performance in terms of data throughput and power consumption.<\/jats:p>","DOI":"10.3390\/s22041603","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:34:47Z","timestamp":1645432487000},"page":"1603","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Re-Learning EXP3 Multi-Armed Bandit Algorithm for Enhancing the Massive IoT-LoRaWAN Network Performance"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0572-0608","authenticated-orcid":false,"given":"Samar Adel","family":"Almarzoqi","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Yahya","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zaki","family":"Matar","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1221-1318","authenticated-orcid":false,"given":"Ibrahim","family":"Gomaa","sequence":"additional","affiliation":[{"name":"Computers & Systems Department, National Telecommunication Institute (NTI-Egypt), Ministry of Communications and Information Technology, Cairo 11112, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gaitan, N.C. 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