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1 →- MakaleAçık Erişim2016Scopus SJRQ1Use of Q-learning approaches for practical medium access control in wireless sensor networks
Kosunalp, Selahattin; Chu, Yi; Mitchell, Paul D.; Grace, David; Clarke, Tim
This paper studies the potential of a novel approach to ensure more efficient and intelligent assignment of capacity through medium access control (MAC) in practical wireless sensor networks. Q-Learning is employed as an intelligent transmission strategy. We review the existing MAC protocols in the context of Q-learning. A recently-proposed, ALOHA and Q-Learning based MAC scheme, ALOHA-Q, is considered which improves the channel performance significantly with a key benefit of simplicity. Practical implementation issues of ALOHA-Q are studied. We demonstrate the performance of the ALOHA-Q through extensive simulations and evaluations in various testbeds. A new exploration/exploitation method is proposed to strengthen the merits of the ALOHA-Q against dynamic the channel and environment conditions.
AtıfScopus41WoS29OpenAlex37İncele
Tam Kayıt
| Alan | Değer | Dil |
|---|---|---|
| creativeworkseries.issn | 0952-1976 | |
| dc.date.accessioned | 2026-10-29T19:11:35.410468081Z | |
| dc.date.available | 2026-10-29T19:11:35.410468081Z | |
| dc.identifier.uri | https://rdlopen.rawdatalibrary.net/r/bayburt/13883 | |
| dc.publisher | Elsevier Ltd | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.title | Engineering Applications of Artificial Intelligence |
