Energy Consumption Optimization for CSMA/CA Protocol Employing Machine Learning

Paulo F. C. Barbosa, Bruna Alves da Silva, Cleber Zanchettin, Renato M. de Moraes

Abstract: The algorithms commonly used for energy control in systems with Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol involve optimization functions with considerable computational complexity and need rigorous control of the test environment. These restrictions create a gap among design, theoretical analysis, real-time processing of network devices and the dependence on human support for parameters setting. In this paper, we propose a novel approach to reach energy saving based on machine learning which considers the input and output of a power consumption control algorithm in CSMA networks, taking into account multiple physical (PHY) layer variables. The results show that the proposed approach obtained a better performance regarding processing time, computational cost and self-adaptation of the parameters currently defined by greedy search energy control algorithms.