Research Article
A Task Scheduling Algorithm Based on Q-Learning for WSNs
@INPROCEEDINGS{10.1007/978-3-030-06161-6_51, author={Benhong Zhang and Wensheng Wu and Xiang Bi and Yiming Wang}, title={A Task Scheduling Algorithm Based on Q-Learning for WSNs}, proceedings={Communications and Networking. 13th EAI International Conference, ChinaCom 2018, Chengdu, China, October 23-25, 2018, Proceedings}, proceedings_a={CHINACOM}, year={2019}, month={1}, keywords={Wireless sensor networks Q-Learning Task scheduling}, doi={10.1007/978-3-030-06161-6_51} }- Benhong Zhang
Wensheng Wu
Xiang Bi
Yiming Wang
Year: 2019
A Task Scheduling Algorithm Based on Q-Learning for WSNs
CHINACOM
Springer
DOI: 10.1007/978-3-030-06161-6_51
Abstract
In industrial Wireless Sensor Networks (WSNs), the transmission of packets usually have strict deadline limitation and the problem of task scheduling has always been an important issue. The problem of task scheduling in WSNs has been proved to be an NP-hard problem, which is usually scheduled using a heuristic algorithm. In this paper, we propose a task scheduling algorithm based on Q-Learning for WSNs called Q-Learning Scheduling on Time Division Multiple Access (QS-TDMA). The algorithm considers the packet priority in combination with the total number of hops and the initial deadline. Moreover, according to the change of the transmission state of packets, QS-TDMA designs the packet transmission constraint and considers the real-time change of packets in WSNs to improve the performance of the scheduling algorithm. Simulation results demonstrate that QS-TDMA is an approximate optimal task scheduling algorithm and can improve the reliability and real-time performance of WSNs.