#iot #lpwan #packet-parser #parser #light-weight

lorawan

Crate lorawan provides structures and tools for reading and writing LoRaWAN messages from and to a slice of bytes

23 releases

0.9.0 Aug 12, 2024
0.8.0 Jan 20, 2024
0.7.4 Sep 25, 2023
0.7.3 Apr 25, 2023
0.1.0 Oct 13, 2017

#115 in Embedded development

Download history 4/week @ 2024-07-05 4/week @ 2024-07-12 103/week @ 2024-07-26 30/week @ 2024-08-02 143/week @ 2024-08-09 26/week @ 2024-08-16 9/week @ 2024-08-23 10/week @ 2024-08-30 26/week @ 2024-09-06 32/week @ 2024-09-13 107/week @ 2024-09-20 44/week @ 2024-09-27 13/week @ 2024-10-04 17/week @ 2024-10-11 7/week @ 2024-10-18

110 downloads per month
Used in 2 crates (via lorawan-device)

MIT license

140KB
2.5K SLoC

LoRaWAN

Latest Version Docs

The lorawan library provides structures and tools for reading and writing LoRaWAN 1.0.2 messages from and to slices of bytes.

Sample Packet manipulation

Use the library

[dependencies]
lorawan = "0.8"

Packet generation

use lorawan::{creator::JoinAcceptCreator, keys, maccommands};
use lorawan::default_crypto::DefaultFactory;
use heapless;

fn main() {
    let mut data = [0; 33];
    let mut phy = JoinAcceptCreator::with_options(&mut data, DefaultFactory).unwrap();
    let key = keys::AES128([1; 16]);
    let app_nonce_bytes = [1; 3];
    phy.set_app_nonce(&app_nonce_bytes);
    phy.set_net_id(&[1; 3]);
    phy.set_dev_addr(&[1; 4]);
    phy.set_dl_settings(2);
    phy.set_rx_delay(1);
    let mut freqs: heapless::Vec<lorawan::maccommands::Frequency, 2> = heapless::Vec::new();
    freqs.push(maccommands::Frequency::new(&[0x58, 0x6e, 0x84,]).unwrap()).unwrap();
    freqs.push(maccommands::Frequency::new(&[0x88, 0x66, 0x84,]).unwrap()).unwrap();
    phy.set_c_f_list(freqs).unwrap();
    let payload = phy.build(&key).unwrap();
    println!("Payload: {:x?}", payload);
}

Packet parsing

use lorawan::parser::*;
use lorawan::keys::*;

fn main() {
    let data = vec![0x40, 0x04, 0x03, 0x02, 0x01, 0x80, 0x01, 0x00, 0x01,
    0xa6, 0x94, 0x64, 0x26, 0x15, 0xd6, 0xc3, 0xb5, 0x82];
    if let Ok(PhyPayload::Data(DataPayload::Encrypted(phy))) = parse(data) {
        let key = AES128([1; 16]);
        let decrypted = phy.decrypt(None, Some(&key), 1).unwrap();
        if let FRMPayload::Data(data_payload) = decrypted.frm_payload() {
                println!("{}", String::from_utf8_lossy(data_payload));
        }
    } else {
        panic!("failed to parse data payload");
    }
}

Benchmarks

Ran on Intel i7-8550U CPU @ 1.80GHz with 16GB RAM running Ubuntu 18.04.

  • Benchmarks brocaar/lorawan (the code for the benchmarks can be found here, results were obtained by running go test -bench . -benchtime=5s, go1.13.1)
pkg: github.com/brocaar/lorawan
BenchmarkDecode-8                  40410            150498 ns/op
BenchmarkValidateMic-8              2959           2026736 ns/op
BenchmarkDecrypt-8                  9390            648402 ns/op
  • Benchmarks rust-lorawan (the code is inside benches/lorawan.rs, results are obtained running cargo bench --workspace, rustc 1.43.0)
  Running target/release/deps/lorawan-32e80b41705c7d41
Gnuplot not found, using plotters backend

data_payload_headers_parsing
      time:   [30.354 ns 30.430 ns 30.497 ns]
      change: [-5.5657% -5.1359% -4.7052%] (p = 0.00 < 0.05)
      Performance has improved.
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high mild

Approximate memory usage per iteration: 1 from 303847227

data_payload_mic_validation
      time:   [2.2334 us 2.2388 us 2.2476 us]
      change: [-3.7708% -3.3970% -2.8941%] (p = 0.00 < 0.05)
      Performance has improved.
Found 20 outliers among 100 measurements (20.00%)
  2 (2.00%) low severe
  5 (5.00%) low mild
  2 (2.00%) high mild
  11 (11.00%) high severe

Approximate memory usage per iteration: 114 from 4349451

data_payload_decrypt
      time:   [1.1179 us 1.1186 us 1.1193 us]
      change: [-0.8167% -0.4650% -0.1514%] (p = 0.00 < 0.05)
      Change within noise threshold.
Found 8 outliers among 100 measurements (8.00%)
  2 (2.00%) low severe
  2 (2.00%) low mild
  3 (3.00%) high mild
  1 (1.00%) high severe

Approximate memory usage per iteration: 57 from 8668603

Used code and inspiration

I would like to thank the projects lorawan by brocaar for the inspiration and useful examples.

Dependencies

~210–580KB
~13K SLoC