Joint Mobile Sink Scheduling and Data Aggregation in Asynchronous Wireless Sensor Networks Using Q-Learning
Surender Redhu, Pratyush Garg, Rajesh M. Hegde
Abstract
Energy-efficient data aggregation is a challenging problem in asynchronous wireless sensor networks. Asynchronous behaviour of sensor nodes is generally due to adaptive duty cycling and it leads to information loss, buffer overflow and poor quality of services. To overcome these issues, a joint mobile sink scheduling and data aggregation scheme is proposed in this work. A reinforcement learning framework is developed herein for budgeting the energy of mobile sink while minimizing the information loss in each cluster of a clustered WSN. More specifically, a Q-learning approach is used to learn the network behaviour over time and compute adaptive halt-times for the mobile sink based on active number of nodes in each cluster. Experiments on joint mobile sink scheduling and data aggregation are conducted on a medium scale WSN. Experimental results indicate that proposed method minimizes the information loss in an asynchronous wireless sensor network. It is also observed that mobile sink performs the data gathering operation with limited energy consumption while maximizing network lifetime.
BibTeX
@inproceedings{icassp2018_jointmobilesinks,
title = {Joint Mobile Sink Scheduling and Data Aggregation in Asynchronous Wireless Sensor Networks Using Q-Learning},
author = {Surender Redhu and Pratyush Garg and Rajesh M. Hegde},
booktitle = {ICASSP 2018},
year = {2018}
}