Enhanced recurrent attention-deep Q learning with optimal node constrains and effective penalty based model for data transmission scheduling on wireless sensor networks
Effective scheduling of data transmission is critical to maximizing network performance and resource usage in the context of wireless sensor networks (WSNs). In order to improve the effectiveness of data transmission scheduling in wireless sensor networks (WSNs), this paper proposes a unique method...
Saved in:
Main Authors: | D.R. Anita Sofia Liz, Yesubai Rubavathi C |
---|---|
Format: | Article |
Language: | English |
Published: |
PeerJ Inc.
2025-06-01
|
Series: | PeerJ Computer Science |
Subjects: | |
Online Access: | https://peerj.com/articles/cs-2950.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Beyond Kanban: POLCA-Constrained Scheduling for Job Shops
by: Antonio Grieco, et al.
Published: (2025-06-01) -
The continuous quadrant penalty formulation of logical constraints
by: Cafieri, Sonia, et al.
Published: (2023-09-01) -
Exploring Virtual Machine Scheduling Algorithms: A Meta-Analysis
by: Salman Mahmood, et al.
Published: (2023-06-01) -
ENERGY CONSUMPTION MODEL OF WIRELESS SENSOR NET NODES AND ITS APPLICATION FOR INCREASING NETWORK OFFLINE OPERATION TIME
by: Mohammed Neamah Mohsen, et al.
Published: (2014-09-01) -
A Novel Bio-Inspired Bird Flocking Node Scheduling Algorithm for Dependable Safety-Critical Wireless Sensor Network Systems
by: Issam Al-Nader, et al.
Published: (2025-05-01)