Correlating the Ambient Conditions and Performance Indicators of the LoRaWAN via Surrogate Gaussian Process-Based Bidirectional LSTM Stacked Autoencoder-Reference-Cited by-同舟云学术

Correlating the Ambient Conditions and Performance Indicators of the LoRaWAN via Surrogate Gaussian Process-Based Bidirectional LSTM Stacked Autoencoder

Published:2023-09 Issue:3 Volume:20 Page:3413-3427
ISSN:1932-4537
Container-title:IEEE Transactions on Network and Service Management
language:
Short-container-title:IEEE Trans. Netw. Serv. Manage.

Author:

Bhat Showkat Ahmad¹^ORCID,Huang Nen-Fu²^ORCID,Hussain Imtiyaz³,Sajjad Uzair⁴

Affiliation:

1. College of Electrical and Computer Science, National Tsing Hua University, Hsinchu, Taiwan

2. Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan

3. Graduate Program in Energy and Opto-Electronic Materials, National Taipei University of Technology, Taipei, Taiwan

4. Department of Energy and Refrigerating Airconditioning Engineering, National Taipei University of Technology, Taipei, Taiwan

Funder

Ministry of Science and Technology

Ministry of Education (subsidy for talentCultivation in international competition—College of Electrical Engineering and Computer Science—5G-AloT Technology and Application Research Center) in Taiwan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/4275028/10274602/10021603.pdf?arnumber=10021603

Reference39 articles.

1. Fair Adaptive Data Rate Allocation and Power Control in LoRaWAN

2. Gate-variants of Gated Recurrent Unit (GRU) neural networks

3. EXPLoRa: Extending the performance of LoRa by suitable spreading factor allocations

4. A Network Intrusion Detection Method Based on Stacked Autoencoder and LSTM

5. HADR: A Hybrid Adaptive Data Rate in LoRaWAN for Internet of Things

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