ML-RPL: Machine Learning-Based Routing Protocol for Wireless Smart Grid Networks-Reference-Cited by-同舟云学术

ML-RPL: Machine Learning-Based Routing Protocol for Wireless Smart Grid Networks

Published:2023 Issue: Volume:11 Page:57401-57414
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Santos Carlos Lester Duenas¹^ORCID,Mezher Ahmad Mohamad¹^ORCID,León Juan Pablo Astudillo²^ORCID,Barrera Julian Cardenas¹^ORCID,Guerra Eduardo Castillo¹^ORCID,Meng Julian¹^ORCID

Affiliation:

1. Electrical and Computer Engineering Department, University of New Brunswick, Fredericton, Canada

2. School of Mathematical and Computational Sciences, Yachay Tech University, Urcuqui, Ecuador

Funder

Atlantic Canada Opportunities Agency

NSERC Collaborating Research and Development

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10144317.pdf?arnumber=10144317

Reference34 articles.

1. Predictive Traffic Control and Differentiation on Smart Grid Neighborhood Area Networks

2. DTMR

3. Classification assessment methods

4. DCRL‐RPL: Dual context‐based routing and load balancing in RPL for IoT networks

5. An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Are ML Models Scenario-Independent in Enhancing Routing Efficiency for Smart Grid Networks?;Proceedings of the Int'l ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks;2023-10-30

2. IEEE Access Special Section: Advances on High Performance Wireless Networks for Automation and IIoT;IEEE Access;2023