Heuristically accelerated reinforcement learning for channel assignment in wireless sensor networks Online publication date: Tue, 09-Nov-2021
by Mohamed Sahraoui; Azeddine Bilami; Abdelmalik Taleb-Ahmed
International Journal of Sensor Networks (IJSNET), Vol. 37, No. 3, 2021
Abstract: In wireless sensor networks (WSNs), multi-channel communication represents an attractive field due to its advantage in improving throughput and delivery rate. However, the major challenge that faces WSNs is the energy constraint. To overcome the channel assignment problem in an energy-efficient way, reinforcement learning (RL) approach is used. Though, RL requires several iterations to obtain the best solution, creating a communication overhead and time-wasting. In this paper, a heuristically accelerated reinforcement learning approach for channel assignment (HARL CA) in WSNs is proposed to reduce the learning iterations. The proposal considers the selected channel by the neighboring sender nodes as external information, used to accelerate the learning process and to avoid collisions, while the bandwidth of the used channel is regarded as an important factor in the scheduling process to increase the delivery rate. The results of extensive simulation experiments show the effectiveness of our approach in improving the network lifetime and performance.
Online publication date: Tue, 09-Nov-2021
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Sensor Networks (IJSNET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org