Energy-Aware Task Scheduling By A True Online Reinforcement Learning In Wireless Sensor Networks

Wireless sensor networks (WSNs) are an attractive platform for various pervasive computing applications. A typical WSN application is composed of different tasks. In this paper, an energy-aware task scheduling method is proposed to achieve better energy consumption/performance trade-off. The proposed method exploits a true online reinforcement learning algorithm. This method is compared with the existing approaches exploiting online learning: distributed independent reinforcement learning (DIRL), reinforcement learning (RL), cooperative reinforcement learning (CRL) and exponential weight for exploration and exploitation (Exp3), in terms of tracking quality/energy consumption trade-off. Simulation results show that our proposed method outperforms existing methods for the target tracking application.