Int. J. of Sensor Networks   »   2017 Vol.25, No.4

 

 

Title: Energy-aware task scheduling by a true online reinforcement learning in wireless sensor networks

 

Authors: Muhidul Islam Khan; Kewen Xia; Ahmad Ali; Nelofar Aslam

 

Addresses:
School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China

 

Abstract: 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.

 

Keywords: WSNs; wireless sensor networks; task scheduling; energy-awareness; true online reinforcement learning; DIRL; distributed independent reinforcement learning; CRL; cooperative reinforcement learning; adversarial bandit solvers.

 

DOI: 10.1504/IJSNET.2016.10001403

 

Int. J. of Sensor Networks, 2017 Vol.25, No.4, pp.244 - 258

 

Available online: 28 Oct 2017

 

 

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