mirror of
https://github.com/ruvnet/RuView
synced 2026-08-08 20:11:43 +00:00
rUv
bb92419ccb
Premise: in a multi-node CSI mesh, all nodes see the same physical
scene through slightly different multipath. Their per-window CSI
vectors cluster tightly under cosine similarity. An adversarial node
(replay / shift / noise injection) sits *outside* that cluster. The
Stoer-Wagner minimum cut on the inter-node similarity graph isolates
it cleanly when the cut is sharp.
Demo synthesises 4 honest nodes (one real CSI window from the paired
data + per-node Gaussian noise 6 dB below signal) and 1 adversarial
node under three attack modes. Cosine-similarity matrix, then
Stoer-Wagner mincut, then check whether partition_B is the singleton
{4} — the adversarial node.
Attack Mincut value Partition_B Isolated?
------- ------------ ----------- ---------
replay 3.4513 {4} YES
shift 3.5724 {4} YES
noise 2.5586 {4} YES
Detection rate: 3/3 = 100%.
Architectural payoff: this is the primitive that fills the stub at
. ADR-103 v0.2.0
can wire it in directly. The mincut value also becomes a continuous
'mesh trustworthiness' metric for the cog-gateway dashboard.
Honest scope: the demo uses sloppy attackers. Adaptive attackers who
have read this note can almost certainly evade by adding calibrated
noise that keeps cosine similarity above the cluster floor. The next
research step is the Stackelberg-game extension. See the
'Honest scope of this result' section in the research note.
Connections:
* R5 — top-8 saliency subcarriers are the priority list for a
more-targeted per-subcarrier consistency check.
* R8 — same primitive likely works at lower SNR with RSSI-only
metrics; cluster structure is preserved by the band integral.
Files:
* examples/research-sota/r7_multilink_consistency.py — pure-NumPy
Stoer-Wagner mincut + synthetic-adversary harness.
* examples/research-sota/r7_multilink_consistency_results.json —
full result JSON for cross-tick reproducibility.
* docs/research/sota-2026-05-22/R7-multilink-consistency.md — note.
* docs/research/sota-2026-05-22/PROGRESS.md — updated index + Done.
7.3 KiB
7.3 KiB
SOTA Research Loop — 2026-05-22
Started: 2026-05-21 ~20:00 ET. Auto-stops: 2026-05-22 08:00 ET. Cron d6e5c473 (*/10 * * * *).
Mandate
Push WiFi-CSI sensing past 2026 published SOTA in three axes:
- Spatial intelligence — multi-static fusion, room-scale awareness, occupancy beyond counting
- RF feature engineering — phase, ToA, subcarrier dynamics, Fresnel zones
- RSSI alone — what's achievable without CSI capture (massive deployment story — every WiFi chip emits RSSI)
Plus practical verticals (exotic & beyond) on a 10–20 year horizon.
Output goes to docs/research/sota-2026-05-22/ (research notes, benchmarks, negative results) + examples/research-sota/ (runnable code).
Working principle
Each loop tick picks ONE unfinished thread from below and produces ONE concrete artifact:
- a research note (Markdown with sources + measured numbers if possible)
- an experiment / micro-benchmark
- a working example under
examples/research-sota/ - a negative result ("X doesn't work because Y, here's the data")
- an ADR if the thread is mature enough to land
Stay 8 minutes / tick. Commit + PR + auto-merge per piece. Future-tick re-entry is via this PROGRESS.md.
Research vectors
Spatial Intelligence
- R1. Multi-static Time-of-Arrival (ToA) from OFDM phase coherence. Three or more ESP32-S3s with shared time base reconstruct a person's (x, y) by triangulating phase-of-flight. 2026 SOTA assumes 3×3 MIMO research NICs; we propose synthetic-aperture aggregation across N independent 1×1 SISO nodes. Calls out subcarrier-level phase unwrapping and per-node clock-offset estimation as the open problems.
- R2. Persistent room field model — eigenstructure perturbation. Already in
wifi-densepose-signal/src/ruvsense/field_model.rs(SVD on empty-room CSI). Push it: derive a per-room embedding ("RF signature of this geometry") that's stable across days, identifies environmental changes (furniture moved, structural drift). Vertical: building-integrity monitoring. - R3. Cross-room re-identification via gait CSI signatures. Per-person walking-style fingerprint that survives walking through different rooms. Different from
AETHER(in-room re-ID) — this is inter-room continuity. - R4. Federated learning of room models. Pi cluster runs per-room LoRA fine-tunes; central learner aggregates without sharing raw CSI. Privacy-preserving spatial intelligence.
RF Feature Engineering
- R5. Subcarrier attention over time → "RF saliency map". Visualize which subcarriers carry the most information per task. ADR-097 hints at this; nothing in repo computes it. Useful for picking the smallest-K subcarrier set that preserves accuracy → enables CSI on chips with severe bandwidth caps.
- R6. Fresnel-zone forward model for through-wall sensing. Code in
wifi-densepose-signal/src/ruvsense/tomography.rsdoes ISTA L1 inversion already; we lack a forward model that predicts CSI from a known scene. Forward model unlocks (a) synthetic data augmentation, (b) self-supervised consistency loss. - R7. Quantum-inspired Stoer-Wagner sampling for adversarial robustness. Use the mincut primitive to detect spoofed CSI by checking the multi-link consistency graph. Lands in
cognitum-rvcsiif it works.
RSSI Alone (no CSI)
- R8. RSSI-only presence + vitals. The entire WiFi-chip ecosystem reports RSSI; only a tiny minority report CSI. A presence + crude vitals model from RSSI alone generalises to billions of devices. Hard problem (very low information rate) but enormous downstream value. Start with literature survey + first model experiment.
- R9. RSSI fingerprint topology — graph neural network on WiFi-scan beacons. Without CSI, can we still do room-localisation by which BSSIDs are visible at what RSSI? Existing
wifi-densepose-wifiscancrate already streams BSSID lists; nothing trains on them yet.
Exotic & Future (10–20 year)
- R10. Through-foliage wildlife sensing. Same physics as through-wall, but at much lower SNR. Gait recognition on a per-species basis. Practical: non-invasive population monitoring without cameras.
- R11. Through-bulkhead maritime crew tracking. Steel attenuates but doesn't eliminate WiFi multipath. Limited range, requires per-vessel calibration.
- R12. RF "weather" mapping. Building-scale Fresnel reflectivity profile over time — detects structural drift, water damage, HVAC failures.
- R13. Contactless blood pressure from sub-mm chest displacement. Already in #271 as a stretch goal; revisit with current model + multi-node fusion.
- R14. Empathic appliances. Smart home appliances modulate behaviour based on breathing-rate-derived stress. Long-horizon — needs both the sensing accuracy and an ethical framework.
- R15. RF biometric across rooms. Gait + breathing + heart-rate signature as a multi-modal biometric for whole-home authentication. Replaces fingerprint/face on the home-network layer.
Done
2026-05-21 kickoff tick
- ✅ R5 in-flight —
examples/research-sota/r5_subcarrier_saliency.pyruns; first measurement oncog-person-countv0.0.2 ships: top-8 subcarriers spread across the band, max/mean ratio 2.85×, suggests bandwidth-capped deployments + RSSI-only models are more viable than feared (band-spread signal retains its integral in RSSI). SeeR5-subcarrier-saliency.md§"First measurement" + §"Implications".
2026-05-22 tick 2 (03:14 UTC)
- ✅ R8 first measurement —
examples/research-sota/r8_rssi_only_count.pyships an RSSI-only person counter trained on a 20-frame band-mean signal. Result: 59.1% accuracy = 94.82% of the full-CSI v0.0.2 baseline (62.3%). Tiny model: 656 params (~5 KB), 56× smaller input, trains in 0.72 s on CPU. Commercial enablement result: moves the cog from "ESP32-S3 only" to "any WiFi receiver". Class accuracy balanced (59.5 / 58.6 vs v0.0.2's skewed 86.2 / 34.3). Caveats: single-room data, 2-class problem, single random draw — needs multi-room replication. SeeR8-rssi-only-count.mdfor full method + interpretation + 3 follow-up experiments queued. Connects directly to R5 (band-spread signal explains why RSSI works) + R9 (same RSSI sequence enables localisation).
2026-05-22 tick 3 (03:25 UTC)
- ✅ R7 first demo —
examples/research-sota/r7_multilink_consistency.pyships a Stoer-Wagner-mincut-based adversarial-node detector for multi-node CSI meshes. Result: 3/3 detection rate across replay / constant-shift / noise-injection attacks in a synthetic 4-honest + 1-adversarial scenario. Mincut isolates the adversarial node cleanly in all three modes (cut values 2.56–3.57, partition_B ={4}consistently). Pure-NumPy demo, no framework deps. Architectural payoff: this is exactly the primitive that fills thecog-person-count::fusion::fuse_with_mincut_clip()stub (ADR-103 v0.2.0). Honest scope: the demo uses sloppy attackers; adaptive attackers who've read this note can probably evade — next thread is the Stackelberg-game extension. SeeR7-multilink-consistency.md.
Negative results
(populated when we discover something doesn't work — these are explicit, not failures)
Index by date
- 2026-05-21 — kickoff (this file)
- 2026-05-22 — tick 2: R8 RSSI-only count (59.1% / 94.82% retained)
- 2026-05-22 — tick 3: R7 multi-link consistency detection (3/3 attack modes detected by Stoer-Wagner mincut)