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http://hdl.handle.net/1942/40522
Title: | Physical-Layer Fingerprinting Of Lora Devices Using Supervised And Zero-Shot Learning | Data Creator - person: | ROBYNS, Pieter Marin, Eduard LAMOTTE, Wim QUAX, Peter Singelée, Dave Preneel, Bart |
Data Creator - organization: | Hasselt University KU Leuven |
Data Curator - person: | Robyns, Pieter | Data Curator - organization: | Hasselt University | Rights Holder - person: | ROBYNS, Pieter | Rights Holder - organization: | Hasselt University | Publisher: | Zenodo | Issue Date: | 2017 | Abstract: | This dataset contains all raw signals (complex float I/Q samples) used in the LoRa fingerprinting experiments of the paper entitled "Physical-Layer Fingerprinting of LoRa devices using Supervised and Zero-Shot Learning". There are 4 databases included: lora1msps, lora2msps, lora5msps, and lora10msps. Each document in the databases is a symbol extracted from a 4-byte random payload LoRa frame, transmitted by a RN2483 radio and received by a USRP B210 sampling at a rate corresponding to the database name. A total of 22 different transmitters were used. For more information, please consult the paper. The document structure is as follows: _id: Unique MongoDB document ID chirp: Base 64 encoded binary float complex I/Q data field: Symbol location inside a LoRa frame tag: Name of the device that sent the frame date: Time and date of reception fn: Frame number rand: Random number for sorting How to import Extract the tar archive. Inside the directory, run the following command to import the lora2msps database: mongorestore --gzip -d lora2msps ./lora2msps This process can be repeated for each dataset. Alternatively, all datasets can be imported automatically by executing: mongorestore --gzip . How to use After the data has been imported, an experiment can be run by simply providing the corresponding config file to tf_train (see https://github.com/rpp0/lora-phy-fingerprinting), e.g.: ./tf_train.py train conf/experiment_lora2msps_mlp.conf | Research Discipline: | Natural sciences > Information and computing sciences > Computer architecture and networks > Computer system security (01020203) | Keywords: | LoRa;PHY-layer;Fingerprinting | DOI: | 10.5281/zenodo.583965 | Link to publication/dataset: | https://zenodo.org/record/583965 | Source: | Zenodo. 10.5281/zenodo.583965 https://zenodo.org/record/583965 | Publications related to the dataset: | 10.1145/3098243.3098267 | License: | Creative Commons Attribution 4.0 International (CC-BY-4.0) | Access Rights: | Open Access | Version: | 1.0 | Category: | DS | Type: | Dataset |
Appears in Collections: | Datasets |
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