2026-10-29T22:39:12Zhttps://bayburt-rdlopen.rawdatalibrary.net/oai/request

oai:bayburt-rdlopen.rawdatalibrary.net:bayburt/138822026-10-29T19:52:18Zopenairedrivercom_bayburt_10000col_bayburt_10001com_bayburt_10010col_bayburt_10224

Use of Q-learning approaches for practical medium access control in wireless sensor networksKosunalp, SelahattinChu, YiMitchell, Paul D.Grace, DavidClarke, TimALOHAMedium access controlQ-LearningWireless sensor networksThis 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.Elsevier Ltd2016Articlehttps://rdlopen.rawdatalibrary.net/r/bayburt/13882https://doi.org/10.1016/j.engappai.2016.06.0120952-1976enEngineering Applications of Artificial Intelligenceinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://bayburt-rdlopen.rawdatalibrary.net/tr/items/bayburt/13882