The lorawan library provides structures and tools for reading and writing LoRaWAN 1.0.2 messages from and to slices of bytes.
The create lorawan
has been renamed to lorawan-encoding
in order to be less
ambiguous and as more crates are now hosted in the same repository. Please
find the new create here.
If migrating your code would be too much effort, please consider using something like
[dependencies]
lorawan-encoding = { package = "lorawan", version = "0.6.1" }
toml
[dependencies]
lorawan = "0.6.1"
```rust use lorawan::{creator, keys, maccommands}; use heapless;
fn main() {
let mut phy = creator::JoinAcceptCreator::new();
let key = keys::AES128([1; 16]);
let appnoncebytes = [1; 3];
phy.setappnonce(&appnoncebytes);
phy.setnetid(&[1; 3]);
phy.setdevaddr(&[1; 4]);
phy.setdlsettings(2);
phy.setrxdelay(1);
let mut freqs: heapless::Vec
```rust 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 Ok(FRMPayload::Data(datapayload)) = decrypted.frmpayload() { println!("{}", String::fromutf8lossy(data_payload)); } } else { panic!("failed to parse data payload"); } } ```
Ran on Intel i7-8550U CPU @ 1.80GHz with 16GB RAM running Ubuntu 18.04.
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
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
datapayloadheaders_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
datapayloadmic_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
datapayloaddecrypt 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 ```
Please read the contributing guidelines
I would like to thank the projects lorawan by brocaar for the inspiration and useful examples.