Channel Quality Prediction for TSCH Blacklisting in Highly Dynamic Networks: A Self-Supervised Deep Learning Approach-Reference-Cited by-同舟云学术

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Channel Quality Prediction for TSCH Blacklisting in Highly Dynamic Networks: A Self-Supervised Deep Learning Approach

Published:2021-09-15 Issue:18 Volume:21 Page:21059-21068
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Farahmandand Mohammad^ORCID,Nabi Majid^ORCID

Funder

SCOTT European project (www.scott-project.eu), that has received funding from the Electronic Component Systems for European Leadership Joint Undertaking

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/9539108/09467294.pdf?arnumber=9467294

Cited by 6 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. DFH: Improving the Reliability of LR-FHSS via Dynamic Frequency Hopping;2023 IEEE 31st International Conference on Network Protocols (ICNP);2023-10-10

2. Revisiting Link Quality Metrics and Models for Multichannel Low-Power Lossy Networks;Sensors;2023-01-23

3. Link Quality Estimation in 6TiSCH Networks: A Review;2022 First International Conference on Computer Communications and Intelligent Systems (I3CIS);2022-11-22

4. An Effective Channel Selection Solution for Reliable Scheduling in Industrial IoT Networks;Journal of Network and Systems Management;2022-07-19

5. eAFH: Informed Exploration for Adaptive Frequency Hopping in Bluetooth Low Energy;2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS);2022-05

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