ruvnet--RuView/docs/research/sota-2026-05-22/PROGRESS.md

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:

1. Spatial intelligence — multi-static fusion, room-scale awareness, occupancy beyond counting
2. RF feature engineering — phase, ToA, subcarrier dynamics, Fresnel zones
3. 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.rs does 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. Stoer-Wagner adversarial-node detection. DONE — 3/3 detection rate (replay/shift/noise). See R7-multilink-consistency.md. Cross-links: R5 top-8 saliency subcarriers are priority targets for partial-spectrum attackers; fills cog-person-count::fusion::fuse_with_mincut_clip() stub (ADR-103 v0.2.0). Next tick: Stackelberg-game adaptive attacker.

RSSI Alone (no CSI)

• R8. RSSI-only person count. DONE — 59.1% = 94.82% of full-CSI (62.3%). 656 params, 5 KB, 0.72 s CPU. See R8-rssi-only-count.md. Cross-links: R5 band-spread saliency explains the retained accuracy; R9 extends same stream to localisation; ADR-104 MCP server should grow ruview_count_infer --rssi mode for non-CSI chips. Next: 3-class ceiling, multi-room replication.
• 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-wifiscan crate 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.py runs; first measurement on cog-person-count v0.0.2 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). See R5-subcarrier-saliency.md §"First measurement" + §"Implications".

2026-05-22 tick 2 (03:14 UTC)

• ✅ R8 first measurement — examples/research-sota/r8_rssi_only_count.py ships 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. See R8-rssi-only-count.md for 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.py ships 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 the cog-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. See R7-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)