Understanding LoRaWAN Transmissions in Harsh Environments: A Measurement-Based Campaign Through Unmanned Aerial/Surface Vehicles-Reference-Cited by-同舟云学术

Understanding LoRaWAN Transmissions in Harsh Environments: A Measurement-Based Campaign Through Unmanned Aerial/Surface Vehicles

Published:2024 Issue: Volume:73 Page:1-14
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Drăgulinescu Ana-Maria¹^ORCID,Zamfirescu Ciprian²^ORCID,Halunga Simona¹^ORCID,Marcu Ioana¹^ORCID,Li Frank Y.³^ORCID,Dobre Octavia A.⁴^ORCID

Affiliation:

1. Department of Telecommunications, National University of Science and Technology Politehnica Bucharest (UNSTPB), Bucharest, Romania

2. Vodafone România, Bucharest, Romania

3. Department of Information and Communication Technology, University of Agder (UiA), Grimstad, Norway

4. Faculty of Engineering and Applied Science, Memorial University, St. John’s, Canada

Funder

European Economic Area (EEA) Norway (NO) through the Project “A Massive MIMO Enabled IoT Platform with Networking Slicing for Beyond 5G IoV/V2X and Maritime Services (SOLID-B5G)”

National Program for Research of the National Association of Technical Universities—GNAC ARUT 2023

Ministry of Agriculture and Rural Development through Smart Viticulture Based on IoT Techniques—Premise for Climate Change Management

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10385137.pdf?arnumber=10385137

Reference32 articles.

1. Online Velocity Control and Data Capture of Drones for the Internet of Things: An Onboard Deep Reinforcement Learning Approach

2. Receiver-Initiated Data Collection in Wake-Up Radio Enabled mIoT Networks: Achieving Collision-Free Transmissions by Hashing and Partitioning

3. Collaborative Computation Offloading and Resource Allocation in Multi-UAV-Assisted IoT Networks: A Deep Reinforcement Learning Approach

4. Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model

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