Application of reinforcement learning to medium access control for wireless sensor networks. (November 2015)

• Record Type: Journal Article
• Authors: Chu, Yi; Kosunalp, Selahattin; Mitchell, Paul D.; Grace, David; Clarke, Tim
• Is Part Of: Engineering applications of artificial intelligence. Volume 46:Part A(2015:Oct.)
• Page Start: 23
• Page End: 32
• Publication Date: 2015-11
• Subjects: Reinforcement learning -- Wireless sensor networks -- Medium access control; 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.2015.08.004
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper presents a novel approach to medium access control for single-hop wireless sensor networks. The ALOHA-Q protocol applies Q-Learning to frame based ALOHA as an intelligent slot selection strategy capable of migrating from random access to perfect scheduling. Results show that ALOHA-Q significantly outperforms Slotted ALOHA in terms of energy-efficiency, delay and throughput. It achieves comparable performance to S-MAC and Z-MAC with much lower complexity and overheads. A Markov model is developed to estimate the convergence time of its simple learning process and to validate the simulation results.