Use of Q-learning approaches for practical medium access control in wireless sensor networks. (October 2016)

• Record Type: Journal Article
• Authors: Kosunalp, Selahattin; Chu, Yi; Mitchell, Paul D.; Grace, David; Clarke, Tim
• Is Part Of: Engineering applications of artificial intelligence. Volume 55(2016:Jul.)
• Page Start: 146
• Page End: 154
• Publication Date: 2016-10
• Subjects: Q-Learning -- ALOHA -- Medium access control -- Wireless sensor networks; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.06.012
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.