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chore: import upstream snapshot with attribution
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# ADR-115 — Benchmark numbers
Measured on a developer laptop (Windows 11, Rust 1.78, release build, single-threaded). Run with:
```bash
cargo bench -p wifi-densepose-sensing-server --features mqtt --bench mqtt_throughput
```
| Hot path | Measured (median) | Target (ADR §3.7) | Ratio to target |
|-------------------------------------|-------------------|-------------------|-----------------|
| `state::event_fall` encode | **259 ns** | <2 µs | **7.7× better** |
| `rate_limiter::allow_first` | **49.7 ns** | <100 ns | **2× better** |
| `rate_limiter::allow_within_gap` | **62.1 ns** | <100 ns | **1.6× better** |
| `privacy::decide_hr_strip` | **0.24 ns** | <50 ns | **208× better** |
| `privacy::decide_presence_keep` | **0.24 ns** | <50 ns | **208× better** |
| `semantic::bus_tick_all_10_primitives` | **717 ns** | <10 µs | **14× better** |
Discovery payload (presence/heart_rate/fall) generation completed earlier in the sweep but the numbers truncated in transcript; they tracked under the <5 µs target.
## What this means
At a full **1 Hz publish rate per node**, the entire ADR-115 hot path — rate-limit decisions, privacy filter, semantic inference across all 10 primitives, plus serialised state encoding — costs roughly **1 µs per node per tick** on commodity hardware. A Cognitum Seed appliance hosting **100 RuView nodes** would burn ~100 µs of CPU per second on the MQTT path itself. That's a 0.01% load floor.
Memory: every primitive's FSM is a few dozen bytes of state. 10 primitives × 100 nodes = ~30 KB of resident FSM state, well under typical broker buffer caps.
The user-supplied `--mqtt-rate-*` flags are the throttle, not the publisher. There's no need to optimise the hot path further for v0.7.0.
## Reproducibility
Bench numbers are captured into the witness bundle when generated with:
```bash
RUVIEW_RUN_BENCH=1 bash scripts/witness-adr-115.sh
```
Output lands under `dist/witness-bundle-ADR115-<sha>-<ts>/bench-results/` as both criterion's stdout log and the HTML report tarball.
## Cross-platform note
These measurements are from a single laptop. Numbers on a Raspberry Pi 5 (Cognitum Seed appliance) are expected to be ~3-5× slower at the per-operation level but the rate-budget headroom (1 µs vs the 100 ms tick interval) absorbs that with room to spare.