Game Theory and Queue Learning Based Sleep/Wakeup Approach Scheduling in Wireless Sensor Network for Energy Efficiency

Abstract

Wireless Sensor Network (WSN) is a network with numerous sensor nodes for examining physical situations, communication and data collection. Sensor nodes communicate with a base station to distribute their data for the purpose of remote process and storage. While transmitting the data energy problem were occurred. This paper goal is to enables senders to predict receivers' wake-up times by using a reinforcement learning technique. If senders have packets to transmit, senders can wake up shortly before the predicted wake-up time of receivers, so the energy, which senders use for idle listening, can be saved. In this case, senders do not have to make the trade-off, because their wake-up times are totally based on receivers’ wake-up times. Receivers still face the trade-off, however, since a receiver’s wake-up time relies on our technique to scheduling function and different selections of parameters in this function will result in different wake-up intervals. In addition, before a sender can make a prediction about a receiver's wake-up time, the sender must request the parameters in the receiver’s wake-up scheduling function.