LoRaWAN‐implemented node localisation based on received signal strength indicator-Reference-Cited by-同舟云学术

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LoRaWAN‐implemented node localisation based on received signal strength indicator

Published:2022-09-15 Issue: Volume: Page:
ISSN:2043-6386
Container-title:IET Wireless Sensor Systems
language:en
Short-container-title:IET Wireless Sensor Systems

Author:

Aqeel Ibrahim¹²,Iorkyase Ephraim¹³^ORCID,Zangoti Hussein²⁴,Tachtatzis Christos¹,Atkinson Robert¹,Andonovic Ivan¹

Affiliation:

1. Electronic and Electrical Engineering Department University of Strathclyde Glasgow UK

2. Computer Engineering and Networks Department Jazan University Jazan Saudi Arabia

3. Electronic and Electrical Engineering Department University of Agriculture Makurdi Makurdi Nigiria

4. Department of Computing and Information Science Florida International University Miami Florida USA

Publisher

Institution of Engineering and Technology (IET)

Subject

Industrial and Manufacturing Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/wss2.12039

Reference33 articles.

1. A comparative study of LPWAN technologies for large-scale IoT deployment

2. A Comprehensive Study of the Use of LoRa in the Development of Smart Cities

3. A survey on LPWA technology: LoRa and NB-IoT

4. A study of bus location system using LoRa: Bus location system for community bus “Notty”

Cited by 8 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. Using fuzzy transforms for neural networks-based wireless localization in outdoor environments;Neural Computing and Applications;2024-08-10

2. A hybrid convolutional neural network-transformer method for received signal strength indicator fingerprinting localization in Long Range Wide Area Network;Engineering Applications of Artificial Intelligence;2024-07

3. Revolutionising IoT Network Security By Assessing ML Localisation Techniques Against Jamming Attacks;2024 22nd Mediterranean Communication and Computer Networking Conference (MedComNet);2024-06-11

4. RSSI-based fingerprint localization in LoRaWAN networks using CNNs with squeeze and excitation blocks;Ad Hoc Networks;2024-06

5. Weather-Based Nonlinear Regressions for Digital TV Received Signal Strength Prediction;Elektronika ir Elektrotechnika;2024-04-26

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