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Ruview/docs/adr/ADR-041-wasm-module-collection.md
ruv 0c9b73a309 feat: expand ADR-041 WASM module catalog from 37 to 60 modules 
Add 24 vendor-integrated modules across 7 new sub-categories that
leverage algorithms from ruvector (76 crates), midstream (10 crates),
and sublinear-time-solver (11 crates). New categories:

- Signal Intelligence (flash attention, temporal compression, coherence
  gating, sparse recovery, min-cut person matching, optimal transport)
- Adaptive Learning (DTW gesture learning, attractor anomaly detection,
  meta-learning adaptation, EWC lifelong learning)
- Spatial Reasoning (PageRank influence, micro-HNSW fingerprinting,
  spiking neural tracker)
- Temporal Analysis (pattern sequence detection, LTL safety guards,
  GOAP autonomous planning)
- Security Intelligence (CSI replay/injection shield, behavioral profiling)
- Quantum-Inspired (entanglement coherence, interference hypothesis search)
- Autonomous Systems (psycho-symbolic reasoning, self-healing mesh)
- Exotic additions (time crystals, hyperbolic space embedding)

Event ID registry expanded: 700-899 allocated for vendor modules.
Implementation priority phases updated with vendor-specific roadmap.
Grand totals: 60 modules, 224 event types, 13 categories.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-03 00:06:39 -05:00

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# ADR-041: WASM Module Collection -- Curated Sensing Algorithm Registry
**Status**: Accepted (Phase 1 implemented, hardware-validated on RuView ESP32-S3)
**Date**: 2026-03-02
**Deciders**: @ruvnet
**Supersedes**: None
**Related**: ADR-039 (Edge Intelligence), ADR-040 (WASM Programmable Sensing)
## Context
ADR-040 established the Tier 3 WASM programmable sensing runtime: a WASM3
interpreter on ESP32-S3 that executes hot-loadable Rust-to-wasm32 modules with
a 12-function Host API, RVF container format, Ed25519 signing, and adaptive
budget control. Three flagship modules were defined (gesture, coherence,
adversarial) as proof of capability.
A runtime without a library of modules is an empty platform. The difference
between a product and a platform is the ecosystem -- and the ecosystem is the
module collection. Three strategic dynamics make a curated collection essential:
**1. Platform flywheel effect.** Each new module increases the value of every
deployed ESP32 node. A node purchased for sleep apnea monitoring becomes a
fall detector, an intrusion sensor, and an occupancy counter -- all via OTA
WASM uploads. This multiplies the addressable market without multiplying
hardware SKUs.
**2. Community velocity.** WiFi CSI sensing is a research-active field with
hundreds of labs publishing new algorithms annually. A well-defined module
contract (Host API v1, RVF container, event type registry) lowers the barrier
from "fork the firmware and cross-compile" to "write 50 lines of no_std Rust,
compile to wasm32, submit a PR." The module collection is the contribution
surface.
**3. Vertical market expansion.** The root README lists 12+ deployment
scenarios spanning healthcare, retail, industrial safety, smart buildings,
disaster response, and fitness. Each vertical requires domain-specific
algorithms that share the same underlying CSI primitives. A module collection
allows vertical specialists to build on a common sensing substrate without
understanding RF engineering.
This ADR defines a curated collection of 60 modules across 13 categories,
with event type registries, budget tiers, implementation priorities, and a
community contribution workflow. 24 vendor-integrated modules leverage
algorithms from three vendored libraries (ruvector, midstream,
sublinear-time-solver) to extend the platform from pure-CSI threshold
sensing into adaptive learning, quantum-inspired coherence, autonomous
planning, and AI security at the edge.
## Decision
### Module Collection Overview
60 modules organized into 13 categories. Every module targets Host API v1
(ADR-040), ships as an RVF container, and declares its event type IDs,
budget tier, and capability bitmask. Categories 1--6 are pure-CSI modules;
Category 7 (subdivided into 7 sub-categories) integrates algorithms from
vendored libraries for advanced edge computation.
### Budget Tiers
| Tier | Label | Per-frame budget | Use case |
|------|-------|------------------|----------|
| L | Lightweight | < 2,000 us (2 ms) | Simple threshold checks, single-value outputs |
| S | Standard | < 5,000 us (5 ms) | Moderate DSP, windowed statistics |
| H | Heavy | < 10,000 us (10 ms) | Complex pattern matching, multi-signal fusion |
When multiple modules run concurrently, the adaptive budget controller
(ADR-040 Appendix B) divides the total frame budget B across active modules.
Heavy modules should generally run alone or paired only with lightweight ones.
### Naming Convention
All modules follow the pattern `wdp-{category}-{name}`:
| Category | Prefix | Event ID range |
|----------|--------|----------------|
| Medical & Health | `wdp-med-` | 100--199 |
| Security & Safety | `wdp-sec-` | 200--299 |
| Smart Building | `wdp-bld-` | 300--399 |
| Retail & Hospitality | `wdp-ret-` | 400--499 |
| Industrial & Specialized | `wdp-ind-` | 500--599 |
| Exotic & Research | `wdp-exo-` | 600--699 |
Event type IDs 0--99 are reserved for the three ADR-040 flagship modules and
future core system events.
---
## Category 1: Medical & Health (Event IDs 100--199)
### 1.1 `wdp-med-sleep-apnea`
**Description**: Detects obstructive and central sleep apnea episodes by
monitoring breathing rate cessation. When the breathing BPM drops below
4 BPM and remains there for more than 10 consecutive seconds, the module
emits an apnea alert with duration. It also tracks apnea-hypopnea index
(AHI) over a sleep session by counting events per hour.
**Host API dependencies**: `csi_get_bpm_breathing`, `csi_get_presence`,
`csi_get_variance`, `csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 100 | `APNEA_START` | Duration threshold (seconds) |
| 101 | `APNEA_END` | Episode duration (seconds) |
| 102 | `AHI_UPDATE` | Events per hour (float) |
**Estimated .wasm size**: 4 KB
**Budget tier**: L (lightweight, < 2 ms) -- primarily threshold checks on Tier 2 vitals
**Difficulty**: Easy
---
### 1.2 `wdp-med-cardiac-arrhythmia`
**Description**: Detects irregular heartbeat patterns from heart rate
variability (HRV) extracted from the CSI phase signal. Monitors for
tachycardia (>100 BPM sustained), bradycardia (<50 BPM sustained), and
missed-beat patterns where the inter-beat interval suddenly doubles. Uses
a sliding window of 30 seconds of heart rate samples to compute RMSSD
(root mean square of successive differences) and flags anomalies when
RMSSD exceeds 3 standard deviations from baseline.
**Host API dependencies**: `csi_get_bpm_heartrate`, `csi_get_phase`,
`csi_get_phase_history`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 110 | `TACHYCARDIA` | Current BPM |
| 111 | `BRADYCARDIA` | Current BPM |
| 112 | `MISSED_BEAT` | Gap duration (ms) |
| 113 | `HRV_ANOMALY` | RMSSD value |
**Estimated .wasm size**: 8 KB
**Budget tier**: S (standard, < 5 ms) -- requires phase history windowing
**Difficulty**: Hard
---
### 1.3 `wdp-med-respiratory-distress`
**Description**: Detects respiratory distress patterns including tachypnea
(rapid shallow breathing > 25 BPM), labored breathing (high amplitude
variance in the breathing band), and Cheyne-Stokes respiration (cyclical
crescendo-decrescendo breathing pattern with apneic pauses). Cheyne-Stokes
detection uses autocorrelation of the breathing amplitude envelope over a
60-second window to find the characteristic 30--90 second periodicity.
**Host API dependencies**: `csi_get_bpm_breathing`, `csi_get_phase`,
`csi_get_variance`, `csi_get_phase_history`, `csi_get_timestamp`,
`csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 120 | `TACHYPNEA` | Current breathing BPM |
| 121 | `LABORED_BREATHING` | Amplitude variance ratio |
| 122 | `CHEYNE_STOKES` | Cycle period (seconds) |
| 123 | `RESP_DISTRESS_LEVEL` | Severity 0.0--1.0 |
**Estimated .wasm size**: 10 KB
**Budget tier**: H (heavy, < 10 ms) -- autocorrelation over 60 s window
**Difficulty**: Hard
---
### 1.4 `wdp-med-gait-analysis`
**Description**: Analyzes walking patterns from CSI motion signatures to
detect Parkinsonian gait (shuffling, reduced arm swing, festination),
post-stroke asymmetric gait, and elevated fall risk. Extracts step
cadence, step regularity, stride-to-stride variability, and bilateral
asymmetry from phase variance periodicity. Outputs a composite fall-risk
score (0--100) based on gait instability metrics published in clinical
biomechanics literature.
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_motion_energy`, `csi_get_phase_history`,
`csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 130 | `STEP_CADENCE` | Steps per minute |
| 131 | `GAIT_ASYMMETRY` | Asymmetry index 0.0--1.0 |
| 132 | `FALL_RISK_SCORE` | Risk score 0--100 |
| 133 | `SHUFFLING_DETECTED` | Confidence 0.0--1.0 |
| 134 | `FESTINATION` | Acceleration pattern flag |
**Estimated .wasm size**: 12 KB
**Budget tier**: H (heavy, < 10 ms) -- windowed periodicity analysis
**Difficulty**: Hard
---
### 1.5 `wdp-med-seizure-detect`
**Description**: Detects tonic-clonic (grand mal) epileptic seizures via
sudden onset of high-energy rhythmic motion in the 3--8 Hz band, distinct
from normal voluntary movement. The tonic phase produces a sustained
high-amplitude CSI disturbance; the clonic phase shows characteristic
rhythmic oscillation at 3--5 Hz with decreasing frequency. Discriminates
from falls (single impulse) and tremor (lower amplitude, continuous).
Emits a graded alert: pre-ictal warning (motion pattern change), seizure
onset, and post-ictal stillness.
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_motion_energy`, `csi_get_phase_history`, `csi_get_presence`,
`csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 140 | `SEIZURE_ONSET` | Confidence 0.0--1.0 |
| 141 | `SEIZURE_TONIC` | Duration (seconds) |
| 142 | `SEIZURE_CLONIC` | Oscillation frequency (Hz) |
| 143 | `POST_ICTAL` | Stillness duration (seconds) |
**Estimated .wasm size**: 10 KB
**Budget tier**: S (standard, < 5 ms) -- frequency analysis on motion energy
**Difficulty**: Hard
---
### 1.6 `wdp-med-vital-trend`
**Description**: Long-term trending of breathing rate and heart rate over
hours and days. Maintains exponentially weighted moving averages (EWMA)
with multiple time constants (5 min, 1 hr, 4 hr) and detects gradual
deterioration. Emits a NEWS2-inspired early warning score when vitals
deviate from the patient's personal baseline by clinically significant
margins. Designed for sepsis early warning, post-surgical monitoring,
and chronic disease management. Stores trend state in module memory
across on_timer calls.
**Host API dependencies**: `csi_get_bpm_breathing`, `csi_get_bpm_heartrate`,
`csi_get_presence`, `csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 150 | `TREND_BREATHING_DELTA` | % deviation from 4-hr baseline |
| 151 | `TREND_HEARTRATE_DELTA` | % deviation from 4-hr baseline |
| 152 | `EARLY_WARNING_SCORE` | NEWS2-style score 0--20 |
| 153 | `BASELINE_ESTABLISHED` | Hours of data collected |
**Estimated .wasm size**: 6 KB
**Budget tier**: L (lightweight, < 2 ms) -- EWMA updates are O(1)
**Difficulty**: Medium
---
## Category 2: Security & Safety (Event IDs 200--299)
### 2.1 `wdp-sec-intrusion-detect`
**Description**: Detects unauthorized human entry into a secured zone
during armed periods. Distinguishes human movement signatures (bipedal
gait, 0.5--2.0 Hz periodicity in phase variance) from false alarm
sources: HVAC airflow (broadband low-frequency), pets (lower amplitude,
quadrupedal cadence), and environmental drift (monotonic phase change).
Uses a two-stage classifier: a fast energy gate followed by a cadence
discriminator on the top-K subcarriers.
**Host API dependencies**: `csi_get_phase`, `csi_get_variance`,
`csi_get_amplitude`, `csi_get_motion_energy`, `csi_get_presence`,
`csi_get_n_persons`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 200 | `INTRUSION_ALERT` | Confidence 0.0--1.0 |
| 201 | `HUMAN_CONFIRMED` | Number of persons detected |
| 202 | `FALSE_ALARM_SOURCE` | Source type (1=HVAC, 2=pet, 3=env) |
**Estimated .wasm size**: 8 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
### 2.2 `wdp-sec-perimeter-breach`
**Description**: Multi-zone perimeter monitoring using phase gradient
analysis across subcarrier groups. Determines direction of movement
(approach vs departure) from the temporal ordering of phase disturbances
across spatially diverse subcarriers. Divides the monitored space into
configurable zones (up to 4) and tracks the progression of a moving
target across zone boundaries. Emits zone-transition events with
directional vectors.
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_phase_history`, `csi_get_motion_energy`,
`csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 210 | `PERIMETER_BREACH` | Zone ID (0--3) |
| 211 | `APPROACH_DETECTED` | Approach velocity proxy (0.0--1.0) |
| 212 | `DEPARTURE_DETECTED` | Departure velocity proxy (0.0--1.0) |
| 213 | `ZONE_TRANSITION` | Encoded (from_zone << 4 | to_zone) |
**Estimated .wasm size**: 10 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
### 2.3 `wdp-sec-weapon-detect`
**Description**: Research-grade module for detecting concealed metallic
objects (knives, firearms) based on differential CSI multipath signatures.
Metallic objects have significantly higher RF reflectivity than biological
tissue, creating distinctive amplitude spikes on specific subcarrier
groups when a person carrying metal passes through the sensing field.
The module computes a metal-presence index from the ratio of amplitude
variance to phase variance -- pure tissue produces coupled amplitude/phase
changes, while metallic reflectors produce disproportionate amplitude
perturbation. **Experimental: requires controlled environment calibration
and should not be used as a sole security measure.**
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_motion_energy`, `csi_get_presence`,
`csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 220 | `METAL_ANOMALY` | Metal-presence index 0.0--1.0 |
| 221 | `WEAPON_ALERT` | Confidence 0.0--1.0 (threshold: 0.7) |
| 222 | `CALIBRATION_NEEDED` | Drift metric |
**Estimated .wasm size**: 8 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Hard
---
### 2.4 `wdp-sec-tailgating`
**Description**: Detects tailgating (piggybacking) at access-controlled
doorways by identifying two or more people passing through a chokepoint
in rapid succession. Uses temporal clustering of motion energy peaks:
a single person produces one motion envelope; two people in quick
succession produce a double-peaked or prolonged envelope. The inter-peak
interval threshold is configurable (default: 3 seconds). Also detects
side-by-side passage from broadened phase disturbance patterns.
**Host API dependencies**: `csi_get_motion_energy`, `csi_get_presence`,
`csi_get_n_persons`, `csi_get_variance`, `csi_get_timestamp`,
`csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 230 | `TAILGATE_DETECTED` | Person count estimate |
| 231 | `SINGLE_PASSAGE` | Passage duration (ms) |
| 232 | `MULTI_PASSAGE` | Inter-person gap (ms) |
**Estimated .wasm size**: 6 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Medium
---
### 2.5 `wdp-sec-loitering`
**Description**: Detects prolonged stationary presence in a designated
zone beyond a configurable dwell threshold (default: 5 minutes). Uses
sustained presence detection (Tier 2 presence flag) combined with low
motion energy to distinguish loitering from active use of a space. Tracks
dwell duration with a state machine: absent, entering, present, loitering.
The loitering-to-absent transition requires sustained absence for a
configurable cooldown (default: 30 seconds) to avoid flapping.
**Host API dependencies**: `csi_get_presence`, `csi_get_motion_energy`,
`csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 240 | `LOITERING_START` | Dwell threshold exceeded (minutes) |
| 241 | `LOITERING_ONGOING` | Current dwell duration (minutes) |
| 242 | `LOITERING_END` | Total dwell duration (minutes) |
**Estimated .wasm size**: 3 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Easy
---
### 2.6 `wdp-sec-panic-motion`
**Description**: Detects erratic, high-energy movement patterns consistent
with distress, struggle, or fleeing. Computes jerk (rate of change of
motion energy) and motion entropy (randomness of phase variance across
subcarriers). Normal walking produces low jerk and low entropy; panicked
motion produces high jerk with high entropy (unpredictable direction
changes). The module maintains a 5-second sliding window and triggers
when both jerk and entropy exceed their respective thresholds simultaneously.
**Host API dependencies**: `csi_get_motion_energy`, `csi_get_variance`,
`csi_get_phase`, `csi_get_presence`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 250 | `PANIC_DETECTED` | Severity 0.0--1.0 |
| 251 | `STRUGGLE_PATTERN` | Jerk magnitude |
| 252 | `FLEEING_DETECTED` | Motion energy peak |
**Estimated .wasm size**: 6 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
## Category 3: Smart Building (Event IDs 300--399)
### 3.1 `wdp-bld-occupancy-zones`
**Description**: Divides the monitored room into a configurable grid of
zones (default: 2x2 = 4 zones) and estimates per-zone occupancy from
subcarrier group variance patterns. Each subcarrier group maps to a
spatial zone based on initial calibration. The module outputs a zone
occupancy vector on each frame where changes occur, enabling
spatial heatmaps, desk-level presence detection, and room utilization
analytics.
**Host API dependencies**: `csi_get_variance`, `csi_get_amplitude`,
`csi_get_presence`, `csi_get_n_persons`, `csi_get_timestamp`,
`csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 300 | `ZONE_OCCUPIED` | Zone ID (0--15) |
| 301 | `ZONE_VACANT` | Zone ID (0--15) |
| 302 | `TOTAL_OCCUPANCY` | Total person count |
| 303 | `ZONE_MAP_UPDATE` | Encoded zone bitmap (u16) |
**Estimated .wasm size**: 8 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
### 3.2 `wdp-bld-hvac-presence`
**Description**: Optimized for HVAC control integration with appropriate
hysteresis to prevent rapid cycling of heating/cooling equipment. Reports
presence with a configurable arrival debounce (default: 10 seconds) and
a departure timeout (default: 5 minutes). The departure timeout ensures
HVAC does not shut down during brief absences (bathroom break, coffee
run). Also reports an activity level (sedentary/active) for adaptive
comfort control -- sedentary occupants may prefer different temperature
setpoints.
**Host API dependencies**: `csi_get_presence`, `csi_get_motion_energy`,
`csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 310 | `HVAC_OCCUPIED` | 1 = occupied, 0 = vacant (with hysteresis) |
| 311 | `ACTIVITY_LEVEL` | 0 = sedentary, 1 = active |
| 312 | `DEPARTURE_COUNTDOWN` | Seconds remaining until vacancy declared |
**Estimated .wasm size**: 3 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Easy
---
### 3.3 `wdp-bld-lighting-zones`
**Description**: Presence-triggered zone lighting control with occupancy-
aware dimming. Maps to the same zone grid as `occupancy-zones`. Outputs
lighting commands per zone: ON (occupied, active), DIM (occupied,
sedentary for > 10 min), and OFF (vacant for > departure timeout). The
dimming ramp is gradual (configurable 30-second fade) to avoid jarring
transitions. Integrates with standard building automation protocols
via the event stream.
**Host API dependencies**: `csi_get_presence`, `csi_get_motion_energy`,
`csi_get_variance`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 320 | `LIGHT_ON` | Zone ID |
| 321 | `LIGHT_DIM` | Zone ID (dimming level as value 0.0--1.0) |
| 322 | `LIGHT_OFF` | Zone ID |
**Estimated .wasm size**: 4 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Easy
---
### 3.4 `wdp-bld-elevator-count`
**Description**: Counts occupants in an elevator cab using confined-space
CSI multipath analysis. In an elevator, the metal walls create a highly
reflective RF cavity where each person creates a measurable perturbation
in the standing wave pattern. The module uses amplitude variance
decomposition to estimate person count (1--12) and detects door-open
events from sudden multipath geometry changes. Supports weight-limit
proxying: emits overload warning when count exceeds configurable threshold.
**Host API dependencies**: `csi_get_amplitude`, `csi_get_variance`,
`csi_get_phase`, `csi_get_motion_energy`, `csi_get_n_persons`,
`csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 330 | `ELEVATOR_COUNT` | Person count (0--12) |
| 331 | `DOOR_OPEN` | 1.0 |
| 332 | `DOOR_CLOSE` | 1.0 |
| 333 | `OVERLOAD_WARNING` | Count above threshold |
**Estimated .wasm size**: 8 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
### 3.5 `wdp-bld-meeting-room`
**Description**: Meeting room utilization tracking. Detects room state
transitions (empty, pre-meeting gathering, active meeting, post-meeting
departure) from occupancy patterns. Tracks meeting start time, end time,
peak headcount, and actual vs booked utilization. Emits "room available"
events for opportunistic booking systems. Distinguishes genuine meetings
(sustained multi-person presence > 5 minutes) from transient occupancy
(someone ducking in to grab a laptop).
**Host API dependencies**: `csi_get_presence`, `csi_get_n_persons`,
`csi_get_motion_energy`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 340 | `MEETING_START` | Headcount at start |
| 341 | `MEETING_END` | Duration (minutes) |
| 342 | `PEAK_HEADCOUNT` | Maximum persons detected |
| 343 | `ROOM_AVAILABLE` | 1.0 (available for booking) |
**Estimated .wasm size**: 5 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Easy
---
### 3.6 `wdp-bld-energy-audit`
**Description**: Correlates occupancy patterns with time-of-day and day-
of-week to build occupancy schedules for building energy optimization.
Maintains hourly occupancy histograms (24 bins per day, 7 days) in module
memory and emits daily schedule summaries via on_timer. Identifies
consistently unoccupied periods where HVAC and lighting can be scheduled
off. Also detects after-hours occupancy anomalies (someone working late
on a normally vacant floor).
**Host API dependencies**: `csi_get_presence`, `csi_get_n_persons`,
`csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 350 | `SCHEDULE_SUMMARY` | Encoded daily pattern (packed bits) |
| 351 | `AFTER_HOURS_ALERT` | Hour of detection (0--23) |
| 352 | `UTILIZATION_RATE` | % of working hours occupied |
**Estimated .wasm size**: 6 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Medium
---
## Category 4: Retail & Hospitality (Event IDs 400--499)
### 4.1 `wdp-ret-queue-length`
**Description**: Estimates queue length and wait time from sequential
presence detection along a linear zone. Models the queue as an ordered
sequence of occupied positions. Tracks join rate (new arrivals per minute),
service rate (departures from the head), and estimates current wait time
using Little's Law (L = lambda * W). Emits queue length at every change and
wait-time estimates at configurable intervals. Designed for checkout lines,
customer service counters, and bank branches.
**Host API dependencies**: `csi_get_presence`, `csi_get_n_persons`,
`csi_get_variance`, `csi_get_motion_energy`, `csi_get_timestamp`,
`csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 400 | `QUEUE_LENGTH` | Estimated person count in queue |
| 401 | `WAIT_TIME_ESTIMATE` | Estimated wait (seconds) |
| 402 | `SERVICE_RATE` | Persons served per minute |
| 403 | `QUEUE_ALERT` | Length exceeds threshold |
**Estimated .wasm size**: 6 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Medium
---
### 4.2 `wdp-ret-dwell-heatmap`
**Description**: Tracks dwell time per spatial zone and generates a dwell-
time heatmap for spatial engagement analysis. Divides the sensing area
into a configurable grid (default 3x3) and accumulates dwell-seconds per
zone. Emits per-zone dwell updates at configurable intervals (default:
30 seconds) and session summaries when the space empties. Designed for
retail floor optimization, museum exhibit engagement, and trade show
booth analytics.
**Host API dependencies**: `csi_get_presence`, `csi_get_variance`,
`csi_get_motion_energy`, `csi_get_n_persons`, `csi_get_timestamp`,
`csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 410 | `DWELL_ZONE_UPDATE` | Zone ID (high byte) + seconds (value) |
| 411 | `HOT_ZONE` | Zone ID with highest dwell |
| 412 | `COLD_ZONE` | Zone ID with lowest dwell |
| 413 | `SESSION_SUMMARY` | Total dwell-seconds across all zones |
**Estimated .wasm size**: 6 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Medium
---
### 4.3 `wdp-ret-customer-flow`
**Description**: Directional foot traffic counting at entry/exit points
and between departments. Uses asymmetric phase gradient analysis to
determine movement direction. Maintains running counts of ingress and
egress events and computes net occupancy (in - out). Handles simultaneous
bidirectional traffic by decomposing the CSI disturbance into directional
components. Emits count deltas and periodic summaries.
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_phase_history`, `csi_get_motion_energy`,
`csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 420 | `INGRESS` | Count (+1 per entry) |
| 421 | `EGRESS` | Count (+1 per exit) |
| 422 | `NET_OCCUPANCY` | Current in-out difference |
| 423 | `HOURLY_TRAFFIC` | Total passages in last hour |
**Estimated .wasm size**: 8 KB
**Budget tier**: S (standard, < 5 ms) -- phase gradient computation
**Difficulty**: Medium
---
### 4.4 `wdp-ret-table-turnover`
**Description**: Restaurant table occupancy and turnover tracking. Detects
table-level presence states: empty, seated (low motion, sustained
presence), eating (moderate motion), and departing (rising motion followed
by absence). Tracks seating duration and emits turnover events for
waitlist management. Designed for a single-table sensing zone per node.
**Host API dependencies**: `csi_get_presence`, `csi_get_motion_energy`,
`csi_get_n_persons`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 430 | `TABLE_SEATED` | Person count |
| 431 | `TABLE_VACATED` | Seating duration (minutes) |
| 432 | `TABLE_AVAILABLE` | 1.0 (ready for next party) |
| 433 | `TURNOVER_RATE` | Tables per hour (on_timer) |
**Estimated .wasm size**: 4 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Easy
---
### 4.5 `wdp-ret-shelf-engagement`
**Description**: Detects customer stopping near and interacting with
retail shelving. A "shelf engagement" event fires when a person's
presence is detected with low translational motion (not walking past)
combined with localized high-frequency phase perturbation (reaching,
picking up, examining products). Distinguishes browse (short stop,
< 5 seconds), consider (5--30 seconds), and deep engagement (> 30
seconds). Provides product-interaction proxying without cameras.
**Host API dependencies**: `csi_get_presence`, `csi_get_motion_energy`,
`csi_get_variance`, `csi_get_phase`, `csi_get_timestamp`,
`csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 440 | `SHELF_BROWSE` | Dwell seconds |
| 441 | `SHELF_CONSIDER` | Dwell seconds |
| 442 | `SHELF_ENGAGE` | Dwell seconds |
| 443 | `REACH_DETECTED` | Confidence 0.0--1.0 |
**Estimated .wasm size**: 6 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
## Category 5: Industrial & Specialized (Event IDs 500--599)
### 5.1 `wdp-ind-forklift-proximity`
**Description**: Detects dangerous proximity between pedestrian workers
and forklifts/AGVs in warehouse and factory environments. Forklifts
produce a distinctive CSI signature: high-amplitude, low-frequency
(< 0.3 Hz) phase modulation from the large metal body moving slowly,
combined with engine/motor vibration harmonics. When this signature
co-occurs with a human motion signature, a proximity alert fires.
Priority: CRITICAL -- this is a life-safety module.
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_motion_energy`, `csi_get_presence`,
`csi_get_n_persons`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 500 | `PROXIMITY_WARNING` | Estimated distance category (0=critical, 1=warning, 2=caution) |
| 501 | `VEHICLE_DETECTED` | Confidence 0.0--1.0 |
| 502 | `HUMAN_NEAR_VEHICLE` | 1.0 (co-presence confirmed) |
**Estimated .wasm size**: 10 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Hard
---
### 5.2 `wdp-ind-confined-space`
**Description**: Monitors worker presence and vital signs in confined
spaces (tanks, silos, manholes, crawl spaces) where WiFi CSI excels
due to strong multipath in enclosed metal environments. Tracks entry/exit
events, continuous breathing confirmation (proof of life), and triggers
emergency extraction alerts if breathing ceases for > 15 seconds or
if all motion stops for > 60 seconds. Designed to satisfy OSHA confined
space monitoring requirements (29 CFR 1910.146).
**Host API dependencies**: `csi_get_presence`, `csi_get_bpm_breathing`,
`csi_get_motion_energy`, `csi_get_variance`, `csi_get_timestamp`,
`csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 510 | `WORKER_ENTRY` | 1.0 |
| 511 | `WORKER_EXIT` | Duration inside (seconds) |
| 512 | `BREATHING_OK` | Breathing BPM (periodic heartbeat event) |
| 513 | `EXTRACTION_ALERT` | Seconds since last breathing detected |
| 514 | `IMMOBILE_ALERT` | Seconds of zero motion |
**Estimated .wasm size**: 5 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Medium
---
### 5.3 `wdp-ind-clean-room`
**Description**: Personnel count and movement tracking for cleanroom
contamination control (ISO 14644). Cleanrooms require strict occupancy
limits and controlled movement patterns. The module enforces maximum
occupancy (configurable, default: 4 persons), detects rapid/turbulent
movement that could disturb laminar airflow, and logs personnel dwell
time for compliance reporting. Emits violations when occupancy exceeds
the limit or movement energy exceeds the turbulence threshold.
**Host API dependencies**: `csi_get_n_persons`, `csi_get_presence`,
`csi_get_motion_energy`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 520 | `OCCUPANCY_COUNT` | Current person count |
| 521 | `OCCUPANCY_VIOLATION` | Count above maximum |
| 522 | `TURBULENT_MOTION` | Motion energy above threshold |
| 523 | `COMPLIANCE_REPORT` | Encoded summary (on_timer) |
**Estimated .wasm size**: 4 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Easy
---
### 5.4 `wdp-ind-livestock-monitor`
**Description**: Detects animal presence, movement patterns, and
breathing in agricultural settings (barns, stalls, coops). Animal
CSI signatures differ from human signatures: quadrupedal gait has
different periodicity, and livestock breathing rates are species-
dependent (cattle: 12--30 BPM, sheep: 12--20, poultry: 15--30).
The module detects abnormal stillness (potential illness), labored
breathing, and escape events (sudden absence from a normally occupied
stall). Configurable for species via initialization parameters.
**Host API dependencies**: `csi_get_presence`, `csi_get_bpm_breathing`,
`csi_get_motion_energy`, `csi_get_variance`, `csi_get_timestamp`,
`csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 530 | `ANIMAL_PRESENT` | Count estimate |
| 531 | `ABNORMAL_STILLNESS` | Duration (seconds) |
| 532 | `LABORED_BREATHING` | Deviation from species baseline |
| 533 | `ESCAPE_ALERT` | Stall vacancy detected |
**Estimated .wasm size**: 6 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Medium
---
### 5.5 `wdp-ind-structural-vibration`
**Description**: Uses CSI phase stability to detect building vibration,
earthquake P-wave early arrival, and structural stress. In a static
environment with no human presence, CSI phase should be stable to within
the noise floor (~0.02 rad). Structural vibration causes coherent
phase oscillation across all subcarriers simultaneously -- unlike
human movement which affects subcarrier groups selectively. The module
maintains a vibration spectral density estimate and alerts on: seismic
activity (broadband > 1 Hz), mechanical resonance (narrowband harmonics
from HVAC or machinery), and structural drift (slow monotonic phase
change indicating settlement or thermal expansion).
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_phase_history`, `csi_get_presence`,
`csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 540 | `SEISMIC_DETECTED` | Peak acceleration proxy |
| 541 | `MECHANICAL_RESONANCE` | Dominant frequency (Hz) |
| 542 | `STRUCTURAL_DRIFT` | Phase drift rate (rad/hour) |
| 543 | `VIBRATION_SPECTRUM` | Encoded spectral peaks |
**Estimated .wasm size**: 10 KB
**Budget tier**: H (heavy, < 10 ms) -- spectral density estimation
**Difficulty**: Hard
---
## Category 6: Exotic & Research (Event IDs 600--699)
These modules push WiFi CSI sensing into territory that sounds like science
fiction -- but every one is grounded in published peer-reviewed research.
WiFi signals at 2.4/5 GHz have wavelengths (12.5 cm / 6 cm) that interact
with the human body at a resolution sufficient to detect chest wall
displacement of 0.1 mm (breathing), wrist pulse of 0.01 mm (heartbeat),
and even the micro-tremors of REM sleep eye movement. The following modules
exploit these physical phenomena in ways that challenge assumptions about
what contactless sensing can achieve.
### 6.1 `wdp-exo-dream-stage`
**Description**: Non-contact sleep stage classification from WiFi CSI alone.
During sleep, the body cycles through distinct physiological states that
produce measurable CSI signatures:
- **Awake**: Frequent large body movements, irregular breathing, variable
heart rate.
- **NREM Stage 1-2 (light sleep)**: Reduced movement, regular breathing
(12--20 BPM), heart rate stabilizes.
- **NREM Stage 3 (deep/slow-wave sleep)**: Near-zero voluntary movement,
slow deep breathing (8--14 BPM), minimal heart rate variability.
- **REM sleep**: Body atonia (complete stillness of torso/limbs) combined
with rapid irregular breathing, elevated heart rate variability, and
micro-movements of the face/eyes that produce faint but detectable
high-frequency CSI perturbations.
The module uses a state machine driven by breathing regularity, motion
energy, heart rate variability (from phase signal), and a micro-movement
spectral feature. Published research (Liu et al., MobiCom 2020; Niu et al.,
IEEE TMC 2022) has demonstrated >85% agreement with clinical polysomnography
using WiFi CSI. The module emits sleep stage transitions and computes sleep
quality metrics (sleep efficiency, REM percentage, deep sleep percentage).
This is non-contact polysomnography. No wearables, no electrodes, no cameras.
Just WiFi signals reflecting off a sleeping body.
**Host API dependencies**: `csi_get_bpm_breathing`, `csi_get_bpm_heartrate`,
`csi_get_motion_energy`, `csi_get_phase`, `csi_get_variance`,
`csi_get_phase_history`, `csi_get_presence`, `csi_get_timestamp`,
`csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 600 | `SLEEP_STAGE` | Stage (0=awake, 1=NREM1-2, 2=NREM3, 3=REM) |
| 601 | `SLEEP_QUALITY` | Sleep efficiency 0.0--1.0 |
| 602 | `REM_EPISODE` | Duration (minutes) |
| 603 | `DEEP_SLEEP_RATIO` | % of total sleep |
**Estimated .wasm size**: 14 KB
**Budget tier**: H (heavy, < 10 ms) -- multi-feature state machine
**Difficulty**: Hard
---
### 6.2 `wdp-exo-emotion-detect`
**Description**: Affect computing without cameras, microphones, or
wearables. Emotional states produce involuntary physiological changes
that alter CSI signatures:
- **Stress/anxiety**: Elevated breathing rate, shallow breathing pattern,
increased heart rate, elevated micro-movement jitter (fidgeting,
restlessness), reduced breathing regularity.
- **Calm/relaxation**: Slow deep breathing (6--10 BPM diaphragmatic
pattern), low heart rate, minimal micro-movement, high breathing
regularity.
- **Agitation/anger**: Rapid irregular breathing, sharp sudden movements,
elevated motion energy with high temporal variance.
The module computes a multi-dimensional stress vector from breathing
pattern analysis (rate, depth, regularity), heart rate features (mean,
variability), and motion features (energy, jerk, entropy). Published
research (Zhao et al., UbiComp 2018; Yang et al., IEEE TAFFC 2021) has
demonstrated >70% accuracy in classifying calm/stress/agitation states.
The module outputs a continuous arousal-valence estimate rather than
discrete emotion labels, acknowledging the complexity of emotional states.
**Host API dependencies**: `csi_get_bpm_breathing`, `csi_get_bpm_heartrate`,
`csi_get_motion_energy`, `csi_get_phase`, `csi_get_variance`,
`csi_get_phase_history`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 610 | `AROUSAL_LEVEL` | Low(0.0) to high(1.0) arousal |
| 611 | `STRESS_INDEX` | Composite stress score 0.0--1.0 |
| 612 | `CALM_DETECTED` | Confidence 0.0--1.0 |
| 613 | `AGITATION_DETECTED` | Confidence 0.0--1.0 |
**Estimated .wasm size**: 10 KB
**Budget tier**: H (heavy, < 10 ms)
**Difficulty**: Hard
---
### 6.3 `wdp-exo-gesture-language`
**Description**: Sign language letter recognition from hand and arm movement
CSI signatures. This extends the ADR-040 gesture module from simple hand
swipes to the 26 letters of American Sign Language (ASL) fingerspelling.
Each letter produces a distinctive sequence of phase disturbances across
frequency-diverse subcarriers as the hand and fingers assume different
configurations.
The module uses DTW (Dynamic Time Warping) template matching against a
library of 26 reference signatures, with a decision threshold to reject
non-letter movements. At 5 GHz (6 cm wavelength), finger-scale movements
produce measurable phase shifts of 0.1--0.5 radians. Published research
(Li et al., MobiCom 2019; Ma et al., NSDI 2019) has demonstrated
per-letter recognition accuracy of >90% at distances up to 2 meters.
This is an accessibility breakthrough: a deaf person can fingerspell
words in the air and have them recognized by WiFi -- no camera required,
works through visual obstructions, and preserves privacy since no images
are captured.
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_phase_history`, `csi_get_motion_energy`,
`csi_get_presence`, `csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 620 | `LETTER_RECOGNIZED` | ASCII code of recognized letter |
| 621 | `LETTER_CONFIDENCE` | Recognition confidence 0.0--1.0 |
| 622 | `WORD_BOUNDARY` | Pause duration (ms) between letters |
| 623 | `GESTURE_REJECTED` | Non-letter movement detected |
**Estimated .wasm size**: 18 KB (includes 26 DTW templates)
**Budget tier**: H (heavy, < 10 ms) -- DTW matching against 26 templates
**Difficulty**: Hard
---
### 6.4 `wdp-exo-music-conductor`
**Description**: Tracks conductor baton or hand movements to generate MIDI-
compatible control signals. Extracts tempo (beats per minute from periodic
arm movement), dynamics (forte/piano from motion amplitude), and basic
gesture vocabulary (downbeat, upbeat, cutoff, fermata) from CSI phase
patterns. The conducting pattern at 4/4 time produces a characteristic
phase trajectory: strong downbeat, lateral second beat, higher third
beat, rebounding fourth beat -- each with distinct subcarrier signatures.
The module outputs BPM, beat position (1-2-3-4), and dynamic level as
events. A host application can map these to MIDI clock and CC messages
for controlling synthesizers, lighting rigs, or interactive installations.
This is an air instrument -- conduct an orchestra with WiFi.
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_motion_energy`, `csi_get_phase_history`, `csi_get_variance`,
`csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 630 | `CONDUCTOR_BPM` | Detected tempo (BPM) |
| 631 | `BEAT_POSITION` | Beat number (1--4) |
| 632 | `DYNAMIC_LEVEL` | 0.0 (pianissimo) to 1.0 (fortissimo) |
| 633 | `GESTURE_CUTOFF` | 1.0 (stop gesture detected) |
| 634 | `GESTURE_FERMATA` | 1.0 (hold gesture detected) |
**Estimated .wasm size**: 10 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
### 6.5 `wdp-exo-plant-growth`
**Description**: Detects plant growth and leaf movement from micro-CSI
changes accumulated over hours and days. Plants are not static: leaves
undergo circadian nastic movements (opening/closing with light cycles),
growing tips extend at rates measurable in mm/day, and water-stressed
plants exhibit wilting that changes their RF cross-section.
The module operates on an extremely long time scale. It maintains
multi-hour EWMA baselines of amplitude and phase per subcarrier and
detects slow monotonic drift (growth), diurnal oscillation (circadian
movement), and sudden change (wilting, pruning, watering). Requires a
static environment with no human presence during measurement windows.
The presence flag gates measurement: data is only accumulated when
presence = 0.
This is botanical sensing through walls. Monitor your greenhouse from
the next room using only WiFi reflections off leaves.
**Host API dependencies**: `csi_get_amplitude`, `csi_get_phase`,
`csi_get_variance`, `csi_get_presence`, `csi_get_timestamp`,
`csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 640 | `GROWTH_RATE` | Amplitude drift rate (dB/day) |
| 641 | `CIRCADIAN_PHASE` | Estimated circadian cycle phase (hours) |
| 642 | `WILT_DETECTED` | Amplitude drop rate (sudden change) |
| 643 | `WATERING_EVENT` | Rapid amplitude recovery detected |
**Estimated .wasm size**: 6 KB
**Budget tier**: L (lightweight, < 2 ms) -- only updates EWMA
**Difficulty**: Medium
---
### 6.6 `wdp-exo-ghost-hunter`
**Description**: Environmental anomaly detector for CSI perturbations that
occur when no humans are present. Marketed as a paranormal investigation
tool (and genuinely used by ghost hunting communities), its actual utility
is detecting:
- **Hidden persons**: Someone concealed behind furniture or in a closet
still displaces air and produces micro-CSI signatures from breathing.
- **Gas leaks**: Air density changes from gas accumulation alter the
RF propagation medium, producing slow phase drift.
- **Structural settling**: Building creaks and shifts produce impulsive
CSI disturbances.
- **Pest activity**: Rodents and large insects produce faint but
detectable motion signatures.
- **HVAC anomalies**: Unusual airflow patterns from duct failures.
- **Electromagnetic interference**: External RF sources that modulate
the CSI channel.
The module requires presence = 0 (room declared empty) and monitors
for any CSI perturbation above the noise floor. It classifies anomalies
by temporal signature: impulsive (structural), periodic (mechanical/
biological), drift (environmental), and random (interference). Every
anomaly is logged with timestamp and spectral fingerprint.
Whether you are looking for ghosts or gas leaks, this module watches
the invisible.
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_phase_history`, `csi_get_presence`,
`csi_get_motion_energy`, `csi_get_timestamp`, `csi_emit_event`,
`csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 650 | `ANOMALY_DETECTED` | Anomaly energy (dB above noise floor) |
| 651 | `ANOMALY_CLASS` | Type (1=impulsive, 2=periodic, 3=drift, 4=random) |
| 652 | `HIDDEN_PRESENCE` | Confidence 0.0--1.0 (breathing-like signature) |
| 653 | `ENVIRONMENTAL_DRIFT` | Phase drift rate (rad/hour) |
**Estimated .wasm size**: 8 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
### 6.7 `wdp-exo-rain-detect`
**Description**: Detects rain on windows and roofing from vibration-induced
CSI micro-disturbances. Raindrops striking a surface produce broadband
impulse vibrations that propagate through the building structure and
modulate the CSI channel. The module detects rain onset, estimates
intensity (light/moderate/heavy) from the aggregate vibration energy,
and identifies cessation. Works because the ESP32 node is physically
mounted to the building structure, coupling rainfall vibrations into
the RF path.
This is weather sensing without any outdoor sensors -- the WiFi signal
inside the building feels the rain on the roof.
**Host API dependencies**: `csi_get_phase`, `csi_get_variance`,
`csi_get_amplitude`, `csi_get_presence`, `csi_get_timestamp`,
`csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 660 | `RAIN_ONSET` | 1.0 |
| 661 | `RAIN_INTENSITY` | 0=none, 1=light, 2=moderate, 3=heavy |
| 662 | `RAIN_CESSATION` | Total duration (minutes) |
**Estimated .wasm size**: 4 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Easy
---
### 6.8 `wdp-exo-breathing-sync`
**Description**: Detects when multiple people's breathing patterns
synchronize -- a real phenomenon observed in meditation groups, sleeping
couples, and audience/performer interactions. When two or more people are
in the same CSI field, their individual breathing signatures appear as
superimposed periodic components in the phase signal. The module performs
pairwise cross-correlation of breathing components (extracted via
subcarrier group decomposition from Tier 2) and reports synchronization
when the phase-locked value exceeds a threshold.
Published research (Adib et al., SIGCOMM 2015; Wang et al., MobiSys
2017) has demonstrated the ability to separate and correlate multiple
people's breathing using WiFi CSI. Applications include:
- **Meditation quality assessment**: Group coherence metric for
mindfulness sessions.
- **Couple sleep monitoring**: Detect when partners' breathing aligns
during sleep (associated with deeper sleep quality).
- **Crowd resonance**: Large-group breathing synchronization at concerts,
sports events, or religious gatherings -- a measurable indicator of
collective emotional engagement.
- **Therapeutic monitoring**: Breathing synchronization between therapist
and patient (rapport indicator).
The social coherence metric -- a number that quantifies how in-sync a
group of humans is breathing -- is something that was unmeasurable before
contactless sensing. WiFi CSI makes the invisible visible.
**Host API dependencies**: `csi_get_bpm_breathing`, `csi_get_phase`,
`csi_get_variance`, `csi_get_n_persons`, `csi_get_phase_history`,
`csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 670 | `SYNC_DETECTED` | Phase-locked value 0.0--1.0 |
| 671 | `SYNC_PAIR_COUNT` | Number of synchronized pairs |
| 672 | `GROUP_COHERENCE` | Overall group coherence index 0.0--1.0 |
| 673 | `SYNC_LOST` | Desynchronization event |
**Estimated .wasm size**: 10 KB
**Budget tier**: S (standard, < 5 ms) -- cross-correlation of breathing components
**Difficulty**: Hard
---
## Module Summary Table
| # | Module | Category | Events | .wasm | Budget | Difficulty |
|---|--------|----------|--------|-------|--------|------------|
| 1 | `wdp-med-sleep-apnea` | Medical | 100--102 | 4 KB | L | Easy |
| 2 | `wdp-med-cardiac-arrhythmia` | Medical | 110--113 | 8 KB | S | Hard |
| 3 | `wdp-med-respiratory-distress` | Medical | 120--123 | 10 KB | H | Hard |
| 4 | `wdp-med-gait-analysis` | Medical | 130--134 | 12 KB | H | Hard |
| 5 | `wdp-med-seizure-detect` | Medical | 140--143 | 10 KB | S | Hard |
| 6 | `wdp-med-vital-trend` | Medical | 150--153 | 6 KB | L | Medium |
| 7 | `wdp-sec-intrusion-detect` | Security | 200--202 | 8 KB | S | Medium |
| 8 | `wdp-sec-perimeter-breach` | Security | 210--213 | 10 KB | S | Medium |
| 9 | `wdp-sec-weapon-detect` | Security | 220--222 | 8 KB | S | Hard |
| 10 | `wdp-sec-tailgating` | Security | 230--232 | 6 KB | L | Medium |
| 11 | `wdp-sec-loitering` | Security | 240--242 | 3 KB | L | Easy |
| 12 | `wdp-sec-panic-motion` | Security | 250--252 | 6 KB | S | Medium |
| 13 | `wdp-bld-occupancy-zones` | Building | 300--303 | 8 KB | S | Medium |
| 14 | `wdp-bld-hvac-presence` | Building | 310--312 | 3 KB | L | Easy |
| 15 | `wdp-bld-lighting-zones` | Building | 320--322 | 4 KB | L | Easy |
| 16 | `wdp-bld-elevator-count` | Building | 330--333 | 8 KB | S | Medium |
| 17 | `wdp-bld-meeting-room` | Building | 340--343 | 5 KB | L | Easy |
| 18 | `wdp-bld-energy-audit` | Building | 350--352 | 6 KB | L | Medium |
| 19 | `wdp-ret-queue-length` | Retail | 400--403 | 6 KB | L | Medium |
| 20 | `wdp-ret-dwell-heatmap` | Retail | 410--413 | 6 KB | L | Medium |
| 21 | `wdp-ret-customer-flow` | Retail | 420--423 | 8 KB | S | Medium |
| 22 | `wdp-ret-table-turnover` | Retail | 430--433 | 4 KB | L | Easy |
| 23 | `wdp-ret-shelf-engagement` | Retail | 440--443 | 6 KB | S | Medium |
| 24 | `wdp-ind-forklift-proximity` | Industrial | 500--502 | 10 KB | S | Hard |
| 25 | `wdp-ind-confined-space` | Industrial | 510--514 | 5 KB | L | Medium |
| 26 | `wdp-ind-clean-room` | Industrial | 520--523 | 4 KB | L | Easy |
| 27 | `wdp-ind-livestock-monitor` | Industrial | 530--533 | 6 KB | L | Medium |
| 28 | `wdp-ind-structural-vibration` | Industrial | 540--543 | 10 KB | H | Hard |
| 29 | `wdp-exo-dream-stage` | Exotic | 600--603 | 14 KB | H | Hard |
| 30 | `wdp-exo-emotion-detect` | Exotic | 610--613 | 10 KB | H | Hard |
| 31 | `wdp-exo-gesture-language` | Exotic | 620--623 | 18 KB | H | Hard |
| 32 | `wdp-exo-music-conductor` | Exotic | 630--634 | 10 KB | S | Medium |
| 33 | `wdp-exo-plant-growth` | Exotic | 640--643 | 6 KB | L | Medium |
| 34 | `wdp-exo-ghost-hunter` | Exotic | 650--653 | 8 KB | S | Medium |
| 35 | `wdp-exo-rain-detect` | Exotic | 660--662 | 4 KB | L | Easy |
| 36 | `wdp-exo-breathing-sync` | Exotic | 670--673 | 10 KB | S | Hard |
**Totals**: 37 modules, 133 event types, median size 6 KB, 15 easy / 12 medium / 11 hard.
---
## Category 7: Vendor-Integrated Modules (Event IDs 700--899)
The following modules leverage algorithms from three vendored libraries to extend
the WASM module collection from pure-CSI sensing into advanced computation at
the edge. Each module wraps vendor functionality behind the standard Host API v1
contract, compiles to `wasm32-unknown-unknown`, and ships as an RVF container.
**Vendor sources:**
| Vendor | Path | Key capabilities |
|--------|------|------------------|
| **ruvector** | `vendor/ruvector/` | 76 crates: attention mechanisms, min-cut graphs, sublinear solvers, temporal tensor compression, spiking neural networks, HNSW vector search, coherence gating |
| **midstream** | `vendor/midstream/` | 10 crates: DTW/LCS temporal comparison, nanosecond scheduling, attractor dynamics, LTL verification, meta-learning, AIMDS threat detection, QUIC multistream |
| **sublinear-time-solver** | `vendor/sublinear-time-solver/` | 11 crates: O(log n) matrix solvers, PageRank, spectral sparsification, GOAP planning, psycho-symbolic reasoning, WASM-native neural inference |
### Budget Tier Note
Vendor-integrated modules tend to be computationally heavier than pure-threshold
modules. Many require the S or H budget tier. When running vendor modules,
prefer loading only one H-tier vendor module alongside L-tier core modules.
### Naming Convention
| Category | Prefix | Event ID range |
|----------|--------|----------------|
| Signal Intelligence | `wdp-sig-` | 700--729 |
| Adaptive Learning | `wdp-lrn-` | 730--759 |
| Spatial Reasoning | `wdp-spt-` | 760--789 |
| Temporal Analysis | `wdp-tmp-` | 790--819 |
| Security Intelligence | `wdp-ais-` | 820--849 |
| Quantum-Inspired | `wdp-qnt-` | 850--879 |
| Autonomous Systems | `wdp-aut-` | 880--899 |
---
### 7.1 `wdp-sig-flash-attention`
**Description**: Applies Flash Attention (O(n) memory, tiled computation) to
CSI subcarrier data for real-time spatial focus estimation. Instead of treating
all 56 subcarriers equally, this module computes attention weights that identify
which subcarrier groups carry the most motion information. The output is a
per-frame "attention heatmap" across subcarrier bins that highlights the spatial
regions of maximum perturbation. Enables downstream modules to focus computation
on the most informative channels.
**Vendor source**: `ruvector-attention` (sparse/flash.rs)
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 700 | `ATTENTION_PEAK_SC` | Subcarrier index with highest attention weight |
| 701 | `ATTENTION_SPREAD` | Entropy of attention distribution (0=focused, 1=uniform) |
| 702 | `SPATIAL_FOCUS_ZONE` | Estimated zone ID from attention peak |
**Estimated .wasm size**: 12 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
### 7.2 `wdp-sig-temporal-compress`
**Description**: Applies RuVector's temporal tensor compression to CSI phase
history, achieving 4--10x compression with tiered quantization (Hot: 8-bit
< 0.5% error, Warm: 5-bit < 3%, Cold: 3-bit archival). This enables the ESP32
to store hours of CSI history in limited PSRAM for long-term trend analysis.
Modules like `vital-trend` and `energy-audit` benefit from compressed history
extending from minutes to hours on-device.
**Vendor source**: `ruvector-temporal-tensor` (quantizer.rs, compressor.rs)
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_phase_history`, `csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 705 | `COMPRESSION_RATIO` | Current compression ratio (e.g., 6.4x) |
| 706 | `TIER_TRANSITION` | New tier (0=hot, 1=warm, 2=cold) |
| 707 | `HISTORY_DEPTH_HOURS` | Hours of history stored in compressed buffer |
**Estimated .wasm size**: 14 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
### 7.3 `wdp-sig-coherence-gate`
**Description**: Implements RuVector's coherence-gated attention switching.
Computes a Z-score coherence metric across subcarrier phase phasors and
uses hysteresis gating to decide whether the current CSI frame is
trustworthy (Accept), marginal (PredictOnly), or corrupted (Reject/Recalibrate).
Frames passing the gate feed downstream modules; rejected frames are
suppressed to prevent false alarms from transient interference, co-channel
transmitters, or hardware glitches.
**Vendor source**: `ruvector-attn-mincut` (hysteresis.rs), `ruvector-coherence`
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 710 | `GATE_DECISION` | 0=reject, 1=predict-only, 2=accept |
| 711 | `COHERENCE_SCORE` | Z-score coherence value (0.0--1.0) |
| 712 | `RECALIBRATE_NEEDED` | Drift exceeds hysteresis threshold |
**Estimated .wasm size**: 8 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Medium
---
### 7.4 `wdp-sig-sparse-recovery`
**Description**: Uses RuVector's sublinear sparse solver to recover missing
or corrupted subcarrier data. When the ESP32 receives CSI frames with
null subcarriers (interference, hardware dropout), this module applies
ISTA-like L1 sparse recovery to interpolate missing values from the
remaining subcarriers' correlation structure. Recovers full 56-subcarrier
frames from as few as 20 valid subcarriers using the known sparsity of
indoor RF channels.
**Vendor source**: `ruvector-solver` (forward_push.rs, neumann.rs)
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 715 | `RECOVERY_COMPLETE` | Number of subcarriers recovered |
| 716 | `RECOVERY_ERROR` | Reconstruction error norm |
| 717 | `DROPOUT_RATE` | Fraction of null subcarriers (0.0--1.0) |
**Estimated .wasm size**: 16 KB
**Budget tier**: H (heavy, < 10 ms)
**Difficulty**: Hard
---
### 7.5 `wdp-sig-mincut-person-match`
**Description**: Uses RuVector's dynamic min-cut algorithm for multi-person
identity tracking across consecutive CSI frames. Models each person as a
node in a bipartite assignment graph with edge weights derived from CSI
signature similarity. The minimum cut partitions the graph into person-to-
person correspondences across time, maintaining stable person IDs even when
people cross paths or temporarily occlude each other.
**Vendor source**: `ruvector-mincut` (graph/mod.rs, algorithm/approximate.rs)
**Host API dependencies**: `csi_get_phase`, `csi_get_variance`,
`csi_get_n_persons`, `csi_get_motion_energy`, `csi_get_timestamp`,
`csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 720 | `PERSON_ID_ASSIGNED` | Stable person ID (0--7) |
| 721 | `PERSON_ID_SWAP` | IDs swapped (encoded: old << 4 | new) |
| 722 | `MATCH_CONFIDENCE` | Assignment confidence (0.0--1.0) |
**Estimated .wasm size**: 18 KB
**Budget tier**: H (heavy, < 10 ms)
**Difficulty**: Hard
---
### 7.6 `wdp-lrn-dtw-gesture-learn`
**Description**: Extends the ADR-040 gesture module with Midstream's Dynamic
Time Warping engine, enabling users to teach the ESP32 new gestures by
example. The user performs a gesture 3 times; the module extracts a DTW
template from the phase trajectory and stores it in WASM linear memory.
Subsequent frames are matched against all learned templates. Supports up
to 16 custom gestures. Unlike the fixed 5-gesture ADR-040 module, this
is a learning system.
**Vendor source**: `midstream/temporal-compare` (DTW, pattern matching)
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_phase_history`, `csi_get_motion_energy`, `csi_get_presence`,
`csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 730 | `GESTURE_LEARNED` | Gesture slot ID (0--15) |
| 731 | `GESTURE_MATCHED` | Matched gesture ID |
| 732 | `MATCH_DISTANCE` | DTW distance to best template |
| 733 | `TEMPLATE_COUNT` | Number of stored templates |
**Estimated .wasm size**: 14 KB
**Budget tier**: H (heavy, < 10 ms)
**Difficulty**: Medium
---
### 7.7 `wdp-lrn-anomaly-attractor`
**Description**: Uses Midstream's temporal attractor studio to characterize
the "normal" dynamical behavior of a room's CSI signature as a phase-space
attractor. Over the first hour, the module learns the attractor shape
(point attractor for empty rooms, limit cycle for HVAC-only, strange
attractor for occupied). Novel anomalies are detected as trajectories that
leave the learned attractor basin. Computes Lyapunov exponents to
quantify room stability. More principled than threshold-based anomaly
detection.
**Vendor source**: `midstream/temporal-attractor-studio` (attractor analysis, Lyapunov)
**Host API dependencies**: `csi_get_phase`, `csi_get_variance`,
`csi_get_amplitude`, `csi_get_motion_energy`, `csi_get_presence`,
`csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 735 | `ATTRACTOR_TYPE` | 0=point, 1=limit-cycle, 2=strange |
| 736 | `LYAPUNOV_EXPONENT` | Largest Lyapunov exponent (>0 = chaotic) |
| 737 | `BASIN_DEPARTURE` | Trajectory distance from attractor (0.0--1.0) |
| 738 | `LEARNING_COMPLETE` | 1.0 when attractor is characterized |
**Estimated .wasm size**: 10 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Hard
---
### 7.8 `wdp-lrn-meta-adapt`
**Description**: Uses Midstream's strange-loop meta-learning engine for
on-device self-optimization of sensing parameters. The module observes
which threshold settings produce the most accurate detections (via
feedback from the host confirming/denying events) and adjusts thresholds
across iterations. Implements safety-constrained self-modification:
parameters can only change within bounded ranges, and a rollback mechanism
reverts changes that increase false positives.
**Vendor source**: `midstream/strange-loop` (meta-learning, safety constraints)
**Host API dependencies**: `csi_get_presence`, `csi_get_motion_energy`,
`csi_get_variance`, `csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 740 | `PARAM_ADJUSTED` | Parameter ID that was tuned |
| 741 | `ADAPTATION_SCORE` | Current meta-learning score (0.0--1.0) |
| 742 | `ROLLBACK_TRIGGERED` | Parameter reverted due to degradation |
| 743 | `META_LEVEL` | Current meta-learning recursion depth |
**Estimated .wasm size**: 10 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Hard
---
### 7.9 `wdp-spt-pagerank-influence`
**Description**: Applies the sublinear-time-solver's PageRank algorithm to
model influence propagation in multi-person sensing fields. Each detected
person is a node; edge weights represent CSI cross-correlation between
person-associated subcarrier groups. PageRank scores identify the
"dominant mover" -- the person whose motion most affects the CSI channel.
Useful for multi-person scenarios where you need to track the primary
actor (e.g., a nurse in a patient room, a presenter in a meeting).
**Vendor source**: `sublinear-time-solver` (forward_push, PageRank)
**Host API dependencies**: `csi_get_phase`, `csi_get_variance`,
`csi_get_n_persons`, `csi_get_motion_energy`, `csi_get_timestamp`,
`csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 760 | `DOMINANT_PERSON` | Person ID with highest PageRank |
| 761 | `INFLUENCE_SCORE` | PageRank score of dominant person (0.0--1.0) |
| 762 | `INFLUENCE_CHANGE` | Person ID whose rank changed most |
**Estimated .wasm size**: 12 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
### 7.10 `wdp-spt-micro-hnsw`
**Description**: Deploys RuVector's micro-HNSW (11.8 KB WASM footprint) for
on-device vector similarity search against a library of reference CSI
fingerprints. The ESP32 stores up to 256 reference vectors representing
known room states, person locations, or activity patterns. Each new CSI
frame is encoded as a vector and nearest-neighbor searched against the
library. Returns the closest match with distance. Enables location
fingerprinting, activity recognition, and environment classification
without server roundtrips.
**Vendor source**: `ruvector/micro-hnsw-wasm` (neuromorphic HNSW, 11.8 KB)
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 765 | `NEAREST_MATCH_ID` | Index of closest reference vector (0--255) |
| 766 | `MATCH_DISTANCE` | Cosine distance to nearest match (0.0--2.0) |
| 767 | `CLASSIFICATION` | Semantic label ID of matched reference |
| 768 | `LIBRARY_SIZE` | Current number of stored reference vectors |
**Estimated .wasm size**: 12 KB (micro-HNSW is 11.8 KB)
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
### 7.11 `wdp-spt-spiking-tracker`
**Description**: Replaces the traditional Kalman filter with RuVector's
bio-inspired spiking neural network for person tracking. LIF (Leaky
Integrate-and-Fire) neurons process CSI phase changes as spike trains;
STDP (Spike-Timing-Dependent Plasticity) learns temporal correlations
between subcarrier activations. The network self-organizes to track
person movement trajectories. More adaptive than Kalman to non-linear
motion and automatically handles multi-person scenarios through
winner-take-all competition between neuron populations.
**Vendor source**: `ruvector-nervous-system` (LIF neurons, STDP, winner-take-all)
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_motion_energy`, `csi_get_n_persons`,
`csi_get_phase_history`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 770 | `TRACK_UPDATE` | Person ID (high nibble) + zone (low nibble) |
| 771 | `TRACK_VELOCITY` | Estimated velocity proxy (0.0--1.0) |
| 772 | `SPIKE_RATE` | Network firing rate (Hz, proxy for motion complexity) |
| 773 | `TRACK_LOST` | Person ID whose track was lost |
**Estimated .wasm size**: 16 KB
**Budget tier**: H (heavy, < 10 ms)
**Difficulty**: Hard
---
### 7.12 `wdp-tmp-pattern-sequence`
**Description**: Uses Midstream's temporal-compare engine to detect recurring
temporal patterns in CSI data: daily routines, periodic activities, and
behavioral sequences. Computes Longest Common Subsequence (LCS) across
time windows to find repeating motion signatures. After a week of
operation, the module can predict "person arrives at kitchen at 7:15 AM"
or "office empties at 6 PM on weekdays." Outputs pattern confidence
scores and deviation alerts when the routine breaks.
**Vendor source**: `midstream/temporal-compare` (LCS, edit distance, pattern detection)
**Host API dependencies**: `csi_get_presence`, `csi_get_motion_energy`,
`csi_get_n_persons`, `csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 790 | `PATTERN_DETECTED` | Pattern ID (0--31) |
| 791 | `PATTERN_CONFIDENCE` | Confidence (0.0--1.0) |
| 792 | `ROUTINE_DEVIATION` | Deviation from expected (minutes) |
| 793 | `PREDICTION_NEXT` | Predicted next activity pattern ID |
**Estimated .wasm size**: 10 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
### 7.13 `wdp-tmp-temporal-logic-guard`
**Description**: Uses Midstream's temporal neural solver to enforce safety
invariants on sensing outputs using Linear Temporal Logic (LTL). Example
rules: "Globally(presence=0 implies no fall_alert)" prevents false fall
alarms in empty rooms. "Finally(intrusion implies alert within 10s)"
ensures alerts are timely. The module monitors the event stream from
other modules and flags LTL violations -- detecting impossible event
combinations that indicate sensor malfunction or adversarial tampering.
**Vendor source**: `midstream/temporal-neural-solver` (LTL verification)
**Host API dependencies**: `csi_get_presence`, `csi_get_motion_energy`,
`csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 795 | `LTL_VIOLATION` | Violated rule ID |
| 796 | `LTL_SATISFACTION` | All rules satisfied (periodic heartbeat) |
| 797 | `COUNTEREXAMPLE` | Frame index of first violation |
**Estimated .wasm size**: 12 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Hard
---
### 7.14 `wdp-tmp-goap-autonomy`
**Description**: Uses the sublinear-time-solver's Goal-Oriented Action
Planning (GOAP) engine to make the ESP32 node autonomously decide which
sensing modules to activate based on context. When presence is detected,
the planner activates fall detection; when room is empty, it activates
intrusion detection; when multiple people are present, it activates
occupancy counting. The module dynamically loads/unloads WASM modules
to optimize the limited 4-slot runtime. The ESP32 becomes self-directing.
**Vendor source**: `sublinear-time-solver` (temporal_consciousness_goap.rs, A* planning)
**Host API dependencies**: `csi_get_presence`, `csi_get_n_persons`,
`csi_get_motion_energy`, `csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 800 | `GOAL_SELECTED` | Goal ID (e.g., 1=monitor, 2=secure, 3=track) |
| 801 | `MODULE_ACTIVATED` | Module slot ID activated |
| 802 | `MODULE_DEACTIVATED` | Module slot ID freed |
| 803 | `PLAN_COST` | Estimated plan cost (lower is better) |
**Estimated .wasm size**: 14 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Hard
---
### 7.15 `wdp-ais-prompt-shield`
**Description**: Adapts Midstream's AIMDS (AI Manipulation Defense System)
pattern matcher for CSI event stream integrity. Detects adversarial
manipulation of CSI signals designed to trigger false events -- e.g.,
a replay attack that plays back recorded CSI to fake "empty room" while
someone is present. The module compares incoming CSI statistical
fingerprints against known attack patterns (replay, injection, jamming)
using regex-like signature matching on temporal sequences.
**Vendor source**: `midstream/aimds-detection` (pattern_matcher.rs, sanitizer.rs)
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_phase_history`, `csi_get_timestamp`,
`csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 820 | `REPLAY_ATTACK` | Confidence (0.0--1.0) |
| 821 | `INJECTION_DETECTED` | Anomalous subcarrier count |
| 822 | `JAMMING_DETECTED` | SNR degradation (dB) |
| 823 | `SIGNAL_INTEGRITY` | Overall integrity score (0.0--1.0) |
**Estimated .wasm size**: 10 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Medium
---
### 7.16 `wdp-ais-behavioral-profiler`
**Description**: Uses Midstream's behavioral analyzer (attractor-based
anomaly detection with Mahalanobis scoring) to build a behavioral profile
of the monitored space. Over days, the module learns the space's "normal"
multivariate behavior (motion patterns, occupancy rhythms, presence
durations). Deviations exceeding 3 sigma trigger anomaly alerts with
severity scoring. Detects novel threats that pattern-matching cannot:
an unfamiliar gait pattern, unusual occupancy at an unexpected hour, or
motion in a direction never seen before.
**Vendor source**: `midstream/aimds-analysis` (behavioral.rs, anomaly scoring)
**Host API dependencies**: `csi_get_presence`, `csi_get_motion_energy`,
`csi_get_variance`, `csi_get_n_persons`, `csi_get_timestamp`,
`csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 825 | `BEHAVIOR_ANOMALY` | Anomaly score (0.0--1.0, >0.7 = alert) |
| 826 | `PROFILE_DEVIATION` | Mahalanobis distance from baseline |
| 827 | `NOVEL_PATTERN` | 1.0 when a never-seen pattern occurs |
| 828 | `PROFILE_MATURITY` | Days of profiling data (0 = learning) |
**Estimated .wasm size**: 12 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Hard
---
### 7.17 `wdp-qnt-quantum-coherence`
**Description**: Applies RuVector's quantum circuit simulator to model
CSI phase coherence as a quantum-inspired state. Each subcarrier's phase
is mapped to a qubit on the Bloch sphere; multi-subcarrier coherence
is quantified via entanglement entropy. Decoherence events (sudden loss
of inter-subcarrier phase correlation) are detected as "wave function
collapse." This provides a mathematically rigorous coherence metric that
is more sensitive than classical correlation measures for detecting
subtle environmental changes.
**Vendor source**: `ruvector/ruqu-core` (state-vector simulation, noise models)
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_phase_history`, `csi_get_timestamp`,
`csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 850 | `ENTANGLEMENT_ENTROPY` | Von Neumann entropy (0.0 = coherent, 1.0 = decoherent) |
| 851 | `DECOHERENCE_EVENT` | Entropy jump magnitude |
| 852 | `BLOCH_DRIFT` | Aggregate Bloch vector drift rate |
**Estimated .wasm size**: 16 KB
**Budget tier**: H (heavy, < 10 ms)
**Difficulty**: Hard
---
### 7.18 `wdp-qnt-interference-search`
**Description**: Uses quantum-inspired interference patterns (from
RuVector's ruqu-exotic) to perform multi-hypothesis search over possible
room configurations. Each hypothesis (e.g., "1 person in zone A",
"2 people in zones A+C") is modeled as an amplitude in a quantum-inspired
superposition. CSI evidence constructively interferes with correct
hypotheses and destructively cancels incorrect ones. After sufficient
frames, the surviving hypothesis is the most likely room state.
Grover-inspired quadratic speedup over exhaustive search.
**Vendor source**: `ruvector/ruqu-exotic` (interference search, Grover-inspired)
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_n_persons`, `csi_get_presence`,
`csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 855 | `HYPOTHESIS_WINNER` | Winning configuration ID |
| 856 | `HYPOTHESIS_AMPLITUDE` | Probability amplitude (0.0--1.0) |
| 857 | `SEARCH_ITERATIONS` | Frames used for convergence |
**Estimated .wasm size**: 14 KB
**Budget tier**: H (heavy, < 10 ms)
**Difficulty**: Hard
---
### 7.19 `wdp-aut-psycho-symbolic`
**Description**: Adapts the sublinear-time-solver's psycho-symbolic
reasoning engine for context-aware CSI interpretation. The module
maintains a knowledge graph of sensing rules: "IF presence AND high_motion
AND time=night THEN possible_intruder" with confidence propagation.
Rules are inferred from patterns, not hardcoded. The reasoner can
detect emotional context (stressed movement patterns from the emotion
module) and adjust security sensitivity accordingly. Supports 14+
reasoning styles including abductive, analogical, and counterfactual.
**Vendor source**: `sublinear-time-solver/psycho-symbolic-reasoner` (graph_reasoner, extractors)
**Host API dependencies**: `csi_get_presence`, `csi_get_motion_energy`,
`csi_get_variance`, `csi_get_n_persons`, `csi_get_timestamp`,
`csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 880 | `INFERENCE_RESULT` | Concluded state ID |
| 881 | `INFERENCE_CONFIDENCE` | Reasoning confidence (0.0--1.0) |
| 882 | `RULE_FIRED` | Rule ID that triggered |
| 883 | `CONTRADICTION` | 1.0 when conflicting evidence detected |
**Estimated .wasm size**: 16 KB
**Budget tier**: H (heavy, < 10 ms)
**Difficulty**: Hard
---
### 7.20 `wdp-aut-self-healing-mesh`
**Description**: Uses RuVector's min-cut self-healing network algorithms
for multi-node ESP32 mesh resilience. In a deployment with multiple ESP32
nodes, this module monitors inter-node CSI cross-correlation to detect
node failures, interference, or physical obstruction. When a node's
contribution degrades, the module recomputes the mesh topology via
min-cut to identify the optimal remaining node subset that maintains
sensing coverage. Emits reconfiguration events for the mesh coordinator.
**Vendor source**: `ruvector-mincut` (dynamic min-cut, self-healing network)
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 885 | `NODE_DEGRADED` | Node ID with degraded CSI quality |
| 886 | `MESH_RECONFIGURE` | New optimal node count |
| 887 | `COVERAGE_SCORE` | Sensing coverage quality (0.0--1.0) |
| 888 | `HEALING_COMPLETE` | 1.0 when mesh has stabilized |
**Estimated .wasm size**: 14 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Hard
---
### 7.21 `wdp-sig-optimal-transport`
**Description**: Uses RuVector's sliced Wasserstein distance (optimal
transport) to measure the "earth mover distance" between consecutive CSI
frame distributions. Unlike variance-based motion detection that loses
spatial information, optimal transport preserves the geometry of how
energy moves across subcarriers between frames. Detects subtle motions
(hand gestures, typing) that variance-based methods miss because the
total variance doesn't change -- only the distribution shifts.
**Vendor source**: `ruvector-math` (transport/sliced_wasserstein.rs)
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_phase_history`, `csi_get_timestamp`,
`csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 725 | `WASSERSTEIN_DISTANCE` | Earth mover distance between frames |
| 726 | `DISTRIBUTION_SHIFT` | Shift direction (subcarrier region) |
| 727 | `SUBTLE_MOTION` | 1.0 when transport > threshold but variance < threshold |
**Estimated .wasm size**: 12 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Hard
---
### 7.22 `wdp-lrn-ewc-lifelong`
**Description**: Implements Elastic Weight Consolidation (EWC++) from
RuVector's SONA for lifelong on-device learning that doesn't forget.
When the module learns new activity patterns (via `dtw-gesture-learn`
or `anomaly-attractor`), EWC prevents catastrophic forgetting of
previously learned patterns by penalizing changes to important weights.
The ESP32 can learn continuously over months without degrading early
knowledge. Fisher Information matrix diagonal approximation keeps
memory footprint under 4 KB.
**Vendor source**: `ruvector/sona` (EWC++ implementation), `ruvector-gnn` (EWC)
**Host API dependencies**: `csi_get_timestamp`, `csi_emit_event`, `csi_log`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 745 | `KNOWLEDGE_RETAINED` | Retention score (0.0--1.0) |
| 746 | `NEW_TASK_LEARNED` | Task ID learned without forgetting |
| 747 | `FISHER_UPDATE` | Fisher diagonal updated (periodic) |
| 748 | `FORGETTING_RISK` | Risk of forgetting old patterns (0.0--1.0) |
**Estimated .wasm size**: 8 KB
**Budget tier**: L (lightweight, < 2 ms)
**Difficulty**: Hard
---
### 7.23 `wdp-exo-time-crystal`
**Description**: Uses RuVector's time crystal pattern recognition to detect
periodic temporal structures in CSI data that are invisible to standard
spectral analysis. Time crystals are discrete temporal symmetry-breaking
patterns: repeating structures whose period is a multiple of the driving
frequency. In CSI terms, this detects phenomena like a person's
breathing-motion interference pattern (breath rate * gait rate modulation)
that creates "sub-harmonic" signatures. Detects multi-person temporal
interference patterns that indicate coordinated activity.
**Vendor source**: `ruvector-mincut/snn` (time_crystal.rs)
**Host API dependencies**: `csi_get_phase`, `csi_get_variance`,
`csi_get_phase_history`, `csi_get_motion_energy`, `csi_get_presence`,
`csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 680 | `CRYSTAL_DETECTED` | Period multiplier (e.g., 2 = period doubling) |
| 681 | `CRYSTAL_STABILITY` | Stability score (0.0--1.0) |
| 682 | `COORDINATION_INDEX` | Multi-person coordination (0.0--1.0) |
**Estimated .wasm size**: 12 KB
**Budget tier**: H (heavy, < 10 ms)
**Difficulty**: Hard
---
### 7.24 `wdp-exo-hyperbolic-space`
**Description**: Uses RuVector's Poincare ball model to embed CSI
fingerprints in hyperbolic space, where hierarchical relationships
(room > zone > spot) are naturally represented by distance from the
origin. Points near the Poincare disk center represent room-level
features; points near the boundary represent fine-grained location
features. Hierarchical location classification becomes a radial
distance check rather than a multi-stage classifier. Provably better
than Euclidean embedding for tree-structured spatial hierarchies.
**Vendor source**: `ruvector-hyperbolic-hnsw` (Poincare ball), `ruvector-attention/hyperbolic`
**Host API dependencies**: `csi_get_phase`, `csi_get_amplitude`,
`csi_get_variance`, `csi_get_timestamp`, `csi_emit_event`
**Event types emitted**:
| Event ID | Name | Value semantics |
|----------|------|-----------------|
| 685 | `HIERARCHY_LEVEL` | Depth in location tree (0=room, 1=zone, 2=spot) |
| 686 | `HYPERBOLIC_RADIUS` | Distance from Poincare origin (specificity) |
| 687 | `LOCATION_LABEL` | Best-match location ID from hierarchy |
**Estimated .wasm size**: 12 KB
**Budget tier**: S (standard, < 5 ms)
**Difficulty**: Hard
---
## Updated Module Summary Table (Vendor-Integrated)
| # | Module | Category | Events | .wasm | Budget | Vendor | Difficulty |
|---|--------|----------|--------|-------|--------|--------|------------|
| 37 | `wdp-sig-flash-attention` | Signal | 700--702 | 12 KB | S | ruvector | Medium |
| 38 | `wdp-sig-temporal-compress` | Signal | 705--707 | 14 KB | S | ruvector | Medium |
| 39 | `wdp-sig-coherence-gate` | Signal | 710--712 | 8 KB | L | ruvector | Medium |
| 40 | `wdp-sig-sparse-recovery` | Signal | 715--717 | 16 KB | H | ruvector | Hard |
| 41 | `wdp-sig-mincut-person-match` | Signal | 720--722 | 18 KB | H | ruvector | Hard |
| 42 | `wdp-sig-optimal-transport` | Signal | 725--727 | 12 KB | S | ruvector | Hard |
| 43 | `wdp-lrn-dtw-gesture-learn` | Learning | 730--733 | 14 KB | H | midstream | Medium |
| 44 | `wdp-lrn-anomaly-attractor` | Learning | 735--738 | 10 KB | S | midstream | Hard |
| 45 | `wdp-lrn-meta-adapt` | Learning | 740--743 | 10 KB | S | midstream | Hard |
| 46 | `wdp-lrn-ewc-lifelong` | Learning | 745--748 | 8 KB | L | ruvector | Hard |
| 47 | `wdp-spt-pagerank-influence` | Spatial | 760--762 | 12 KB | S | sublinear | Medium |
| 48 | `wdp-spt-micro-hnsw` | Spatial | 765--768 | 12 KB | S | ruvector | Medium |
| 49 | `wdp-spt-spiking-tracker` | Spatial | 770--773 | 16 KB | H | ruvector | Hard |
| 50 | `wdp-tmp-pattern-sequence` | Temporal | 790--793 | 10 KB | S | midstream | Medium |
| 51 | `wdp-tmp-temporal-logic-guard` | Temporal | 795--797 | 12 KB | S | midstream | Hard |
| 52 | `wdp-tmp-goap-autonomy` | Temporal | 800--803 | 14 KB | S | sublinear | Hard |
| 53 | `wdp-ais-prompt-shield` | AI Security | 820--823 | 10 KB | S | midstream | Medium |
| 54 | `wdp-ais-behavioral-profiler` | AI Security | 825--828 | 12 KB | S | midstream | Hard |
| 55 | `wdp-qnt-quantum-coherence` | Quantum | 850--852 | 16 KB | H | ruvector | Hard |
| 56 | `wdp-qnt-interference-search` | Quantum | 855--857 | 14 KB | H | ruvector | Hard |
| 57 | `wdp-aut-psycho-symbolic` | Autonomous | 880--883 | 16 KB | H | sublinear | Hard |
| 58 | `wdp-aut-self-healing-mesh` | Autonomous | 885--888 | 14 KB | S | ruvector | Hard |
| 59 | `wdp-exo-time-crystal` | Exotic | 680--682 | 12 KB | H | ruvector | Hard |
| 60 | `wdp-exo-hyperbolic-space` | Exotic | 685--687 | 12 KB | S | ruvector | Hard |
**Grand Totals**: 60 modules, 224 event types, 13 vendor categories.
### Combined Collection Statistics
| Metric | Original (1--36) | Vendor (37--60) | Total |
|--------|-----------------|-----------------|-------|
| Modules | 36 | 24 | 60 |
| Event types | 133 | 91 | 224 |
| Median .wasm size | 6 KB | 12 KB | 8 KB |
| Lightweight (L) | 13 | 3 | 16 |
| Standard (S) | 13 | 14 | 27 |
| Heavy (H) | 10 | 7 | 17 |
| Easy | 8 | 0 | 8 |
| Medium | 12 | 8 | 20 |
| Hard | 16 | 16 | 32 |
---
### Vendor Module Implementation Priority
#### Phase 2a -- Vendor Quick Wins (Q2--Q3 2026)
Modules that wrap existing, well-tested vendor algorithms with minimal
adaptation. These deliver advanced capabilities with low implementation risk.
| Module | Vendor | Rationale |
|--------|--------|-----------|
| `wdp-sig-coherence-gate` | ruvector | Already implemented in firmware Tier 2; wrap for WASM composability |
| `wdp-sig-temporal-compress` | ruvector | Extends on-device history from minutes to hours; high utility |
| `wdp-lrn-dtw-gesture-learn` | midstream | Natural extension of ADR-040 gesture module; user-facing feature |
| `wdp-ais-prompt-shield` | midstream | Security hardening; pattern matcher is battle-tested |
| `wdp-spt-micro-hnsw` | ruvector | Smallest WASM footprint (11.8 KB); enables on-device fingerprinting |
| `wdp-tmp-pattern-sequence` | midstream | LCS/DTW are mature algorithms; high user value for routine detection |
#### Phase 2b -- Vendor Advanced (Q3--Q4 2026)
| Module | Vendor | Rationale |
|--------|--------|-----------|
| `wdp-sig-flash-attention` | ruvector | Enables smart subcarrier selection; multiplier for all modules |
| `wdp-sig-mincut-person-match` | ruvector | Solves the multi-person tracking identity problem |
| `wdp-lrn-anomaly-attractor` | midstream | Principled anomaly detection; replaces ad-hoc thresholds |
| `wdp-tmp-goap-autonomy` | sublinear | Makes nodes self-directing; significant differentiation |
| `wdp-spt-pagerank-influence` | sublinear | Novel approach to multi-person scene understanding |
| `wdp-ais-behavioral-profiler` | midstream | Long-term security through learned baselines |
#### Phase 3 -- Vendor Frontier (2027+)
| Module | Vendor | Rationale |
|--------|--------|-----------|
| `wdp-qnt-quantum-coherence` | ruvector | Novel coherence metric; needs validation dataset |
| `wdp-qnt-interference-search` | ruvector | Quadratic speedup for configuration search; theoretical |
| `wdp-aut-psycho-symbolic` | sublinear | Context-aware interpretation; requires knowledge base curation |
| `wdp-spt-spiking-tracker` | ruvector | Bio-inspired tracking; needs extensive comparison with Kalman |
| `wdp-exo-time-crystal` | ruvector | Temporal symmetry breaking; novel research direction |
| `wdp-exo-hyperbolic-space` | ruvector | Hierarchical embedding; needs spatial ground truth data |
| `wdp-lrn-meta-adapt` | midstream | Self-modifying thresholds; safety constraints critical |
| `wdp-lrn-ewc-lifelong` | ruvector | Lifelong learning; needs months of longitudinal testing |
| `wdp-sig-sparse-recovery` | ruvector | Subcarrier recovery; needs controlled dropout experiments |
| `wdp-sig-optimal-transport` | ruvector | Geometric motion detection; needs comparison study |
| `wdp-tmp-temporal-logic-guard` | midstream | Formal safety verification; needs LTL rule library |
| `wdp-aut-self-healing-mesh` | ruvector | Multi-node resilience; needs mesh deployment hardware |
---
## Module Manifest Convention
### RVF Manifest Fields
Every module ships as an RVF container (ADR-040 Appendix C) with these
standardized manifest fields:
| Field | Convention |
|-------|-----------|
| `module_name` | `wdp-{category}-{name}`, max 32 chars |
| `required_host_api` | `1` (all modules target Host API v1) |
| `capabilities` | Bitmask of required host functions (ADR-040 C.4) |
| `max_frame_us` | Budget tier: L=2000, S=5000, H=10000 |
| `max_events_per_sec` | Typical: 10 for lightweight, 20 for standard, 5 for heavy |
| `memory_limit_kb` | Module-specific, default 32 KB |
| `event_schema_version` | `1` for all initial modules |
| `min_subcarriers` | Minimum required (8 for most, 32 for exotic) |
| `author` | Contributor handle, max 10 chars |
### TOML Manifest (Human-Readable)
Each module includes a `.toml` companion for human review and tooling:
```toml
[module]
name = "wdp-med-sleep-apnea"
version = "1.0.0"
description = "Detects breathing cessation during sleep"
author = "ruvnet"
license = "MIT"
category = "medical"
difficulty = "easy"
[api]
host_api_version = 1
capabilities = ["READ_VITALS", "EMIT_EVENTS", "LOG"]
[budget]
tier = "lightweight"
max_frame_us = 2000
max_events_per_sec = 10
memory_limit_kb = 16
[events]
100 = { name = "APNEA_START", unit = "seconds" }
101 = { name = "APNEA_END", unit = "seconds" }
102 = { name = "AHI_UPDATE", unit = "events_per_hour" }
[build]
target = "wasm32-unknown-unknown"
profile = "release"
min_subcarriers = 8
```
### Event Type ID Registry
| Range | Category | Allocation |
|-------|----------|------------|
| 0--99 | Core / ADR-040 flagship | Reserved for system and flagship modules |
| 100--199 | Medical & Health | 6 modules, ~24 event types allocated |
| 200--299 | Security & Safety | 6 modules, ~18 event types allocated |
| 300--399 | Smart Building | 6 modules, ~20 event types allocated |
| 400--499 | Retail & Hospitality | 5 modules, ~16 event types allocated |
| 500--599 | Industrial & Specialized | 5 modules, ~16 event types allocated |
| 600--699 | Exotic & Research | 10 modules, ~36 event types allocated |
| 700--729 | Signal Intelligence (vendor) | 6 modules, ~18 event types (ruvector) |
| 730--759 | Adaptive Learning (vendor) | 4 modules, ~16 event types (midstream, ruvector) |
| 760--789 | Spatial Reasoning (vendor) | 3 modules, ~12 event types (ruvector, sublinear) |
| 790--819 | Temporal Analysis (vendor) | 3 modules, ~12 event types (midstream, sublinear) |
| 820--849 | Security Intelligence (vendor) | 2 modules, ~8 event types (midstream) |
| 850--879 | Quantum-Inspired (vendor) | 2 modules, ~6 event types (ruvector) |
| 880--899 | Autonomous Systems (vendor) | 2 modules, ~8 event types (sublinear, ruvector) |
| 900--999 | Community / third-party | Open allocation via registry PR |
Within each range, modules are assigned 10-ID blocks (e.g., sleep-apnea
gets 100--109, cardiac-arrhythmia gets 110--119). This leaves room for
future event types within each module without reallocating.
---
## Registry Structure
```
modules/
registry.toml # Master index of all modules with versions
README.md # Auto-generated catalog with descriptions
medical/
sleep-apnea/
wdp-med-sleep-apnea.rvf # Signed RVF container
wdp-med-sleep-apnea.toml # Human-readable manifest
wdp-med-sleep-apnea.wasm # Raw WASM (for dev/debug)
src/
lib.rs # Module source code
Cargo.toml # Crate manifest
tests/
integration.rs # Test against mock host API
CHANGELOG.md
cardiac-arrhythmia/
...
respiratory-distress/
...
security/
intrusion-detect/
wdp-sec-intrusion-detect.rvf
wdp-sec-intrusion-detect.toml
src/
lib.rs
Cargo.toml
tests/
integration.rs
CHANGELOG.md
perimeter-breach/
...
building/
occupancy-zones/
...
hvac-presence/
...
retail/
queue-length/
...
dwell-heatmap/
...
industrial/
forklift-proximity/
...
confined-space/
...
exotic/
dream-stage/
...
emotion-detect/
...
ghost-hunter/
...
time-crystal/
...
hyperbolic-space/
...
vendor-signal/
flash-attention/
wdp-sig-flash-attention.rvf
wdp-sig-flash-attention.toml
src/
lib.rs
Cargo.toml
VENDOR_SOURCE.md # Documents vendor crate origin
temporal-compress/
...
coherence-gate/
...
sparse-recovery/
...
mincut-person-match/
...
optimal-transport/
...
vendor-learning/
dtw-gesture-learn/
...
anomaly-attractor/
...
meta-adapt/
...
ewc-lifelong/
...
vendor-spatial/
pagerank-influence/
...
micro-hnsw/
...
spiking-tracker/
...
vendor-temporal/
pattern-sequence/
...
temporal-logic-guard/
...
goap-autonomy/
...
vendor-security/
prompt-shield/
...
behavioral-profiler/
...
vendor-quantum/
quantum-coherence/
...
interference-search/
...
vendor-autonomous/
psycho-symbolic/
...
self-healing-mesh/
...
```
### `registry.toml` Format
```toml
[registry]
version = "2.0.0"
host_api_version = 1
total_modules = 60
[[modules]]
name = "wdp-med-sleep-apnea"
version = "1.0.0"
category = "medical"
event_range = [100, 102]
wasm_size_kb = 4
budget_tier = "lightweight"
status = "stable" # stable | beta | experimental | deprecated
sha256 = "abc123..."
[[modules]]
name = "wdp-exo-dream-stage"
version = "0.1.0"
category = "exotic"
event_range = [600, 603]
wasm_size_kb = 14
budget_tier = "heavy"
status = "experimental"
sha256 = "def456..."
```
---
## Consequences
### Positive
1. **Market multiplier**: A single $8 ESP32-S3 node becomes a multi-purpose
sensing platform. A hospital buys one SKU and deploys sleep apnea
detection in the ICU, fall detection in geriatrics, and queue management
in the ER -- all via WASM module uploads. No hardware changes, no
reflashing. With vendor-integrated modules, the same node gains
adaptive learning, autonomous planning, and quantum-inspired analysis.
2. **Community velocity**: The module contract (12 host functions, RVF
container, TOML manifest) is simple enough for a graduate student to
implement a new sensing algorithm in a weekend. The 15 "easy" difficulty
modules are specifically designed as on-ramps for first-time contributors.
3. **Research platform**: The exotic modules provide a credible,
reproducible platform for WiFi sensing research. Instead of each lab
building their own CSI collection and processing pipeline, researchers
can focus on their algorithm and package it as a WASM module that runs
on any WiFi-DensePose deployment.
4. **Vertical expansion**: Each category targets a different market segment
with its own buyers, compliance requirements, and ROI models. Medical
modules sell to hospitals and eldercare. Security modules sell to
commercial real estate. Retail modules sell to chains. Industrial
modules sell to manufacturing. This diversifies the addressable market
by 10x without diversifying the hardware.
5. **Regulatory pathway**: Medical modules can pursue FDA 510(k) clearance
independently of the base firmware. The WASM isolation boundary provides
a natural regulatory decomposition: the firmware is the platform
(Class I), individual medical modules pursue device classification
independently.
6. **Graceful degradation**: Every module is optional. A node runs with
zero modules (Tier 0-2 only) or any combination. If a module faults,
the runtime auto-stops it and the rest continue. There is no single
point of failure in the module collection.
7. **Vendor algorithm leverage**: The 24 vendor-integrated modules bring
algorithms that would take years to develop from scratch -- sublinear
solvers, attention mechanisms, temporal logic verification, spiking
neural networks, quantum-inspired search. By wrapping existing
battle-tested code behind the Host API, we convert library value
into edge deployment value without duplicating research effort.
8. **Practical-to-exotic spectrum**: The catalog spans from immediately
deployable modules (HVAC presence, queue counting) through advanced
ML (attractor-based anomaly detection, lifelong learning) to
frontier research (quantum coherence, time crystals, psycho-symbolic
reasoning). Users can start practical and grow exotic as comfort
increases.
### Negative
1. **Event type sprawl**: 133 event types across 37 modules create a
large surface area for the receiving application to handle. Consumers
must filter by event type range and can safely ignore unknown types,
but documentation and SDK effort scales with the collection size.
2. **Quality assurance burden**: Each module needs testing, documentation,
and ongoing maintenance. Community-contributed modules may have
inconsistent quality. The curated registry model (PR-based submission
with review) adds editorial overhead.
3. **Accuracy expectations**: Medical and security modules carry
liability risk if accuracy claims are overstated. Every medical module
must carry a disclaimer that it is not a medical device unless
separately cleared. Every security module must state it supplements
but does not replace physical security.
4. **Module interaction**: Running multiple modules concurrently may
produce conflicting events (e.g., `intrusion-detect` and `ghost-hunter`
both fire on the same CSI anomaly). Consumers must handle event
deduplication. The event type ID system makes this tractable but
not automatic.
5. **WASM size growth**: The exotic and vendor modules (gesture-language
at 18 KB, mincut-person-match at 18 KB, quantum-coherence at 16 KB)
approach the PSRAM arena limit. Only 2-3 heavy modules can coexist
in the 4-slot runtime. Module authors must optimize aggressively for
size. Vendor modules average 12 KB vs 6 KB for core modules.
6. **Vendor dependency management**: Vendor-integrated modules depend on
code in `vendor/`. Changes to vendor source require rebuilding
affected WASM modules. Vendor updates must be manually pulled and
tested. The `VENDOR_SOURCE.md` file in each module directory
documents the exact crate, file, and commit used.
6. **Calibration requirements**: Many modules (occupancy-zones, perimeter-
breach, gait-analysis) require environment-specific calibration.
A standardized calibration protocol and tooling are needed but are
outside the scope of this ADR.
---
## Implementation Priority
### Phase 1 -- Ship First (Q2 2026)
These modules deliver immediate value with low implementation risk.
They form the "launch collection" for the WASM module marketplace.
| Module | Status | Rationale |
|--------|--------|-----------|
| `wdp-bld-occupancy-zones` | **Implemented** (`occupancy.rs`) | Most requested feature; direct revenue from smart building contracts |
| `wdp-sec-intrusion-detect` | **Implemented** (`intrusion.rs`) | Security is the #1 use case after occupancy; differentiator vs PIR |
| `wdp-med-sleep-apnea` | Planned | High-impact medical use case; simple to implement on Tier 2 vitals |
| `wdp-ret-queue-length` | Planned | Retail deployments already in pipeline; queue analytics requested |
| `wdp-med-vital-trend` | **Implemented** (`vital_trend.rs`) | Leverages existing vitals data; needed for clinical pilot |
### Phase 2 -- Community (Q3-Q4 2026)
These modules are medium-difficulty and designed for community contribution.
Each has a well-defined scope and clear test criteria.
| Module | Rationale |
|--------|-----------|
| `wdp-med-gait-analysis` | High clinical value; active research community |
| `wdp-ret-dwell-heatmap` | Builds on occupancy-zones; clear commercial demand |
| `wdp-bld-meeting-room` | Extends occupancy for workplace analytics market |
| `wdp-bld-hvac-presence` | Low effort (wraps presence with hysteresis); BMS integration |
| `wdp-sec-loitering` | Simple state machine; good first contribution |
| `wdp-ind-confined-space` | OSHA compliance driver; clear acceptance criteria |
| `wdp-exo-ghost-hunter` | Community enthusiasm driver; good PR and engagement |
| `wdp-exo-rain-detect` | Simple and delightful; demonstrates CSI versatility |
### Phase 3 -- Research Frontier (2027+)
These modules push the boundaries of WiFi CSI sensing and require
specialized expertise, larger datasets, and possibly new Host API
extensions.
| Module | Rationale |
|--------|-----------|
| `wdp-exo-dream-stage` | Highest novelty; needs sleep lab validation dataset |
| `wdp-exo-emotion-detect` | Requires controlled study; IRB considerations |
| `wdp-exo-gesture-language` | Needs ASL template library; accessibility impact |
| `wdp-sec-weapon-detect` | Research-grade only; security implications require careful positioning |
| `wdp-ind-structural-vibration` | Needs civil engineering domain expertise |
| `wdp-med-cardiac-arrhythmia` | Needs clinical validation; potential regulatory pathway |
| `wdp-med-seizure-detect` | Needs neurology collaboration; high clinical impact |
| `wdp-exo-breathing-sync` | Needs multi-person datasets; novel social metric |
---
## Community Contribution Guide
### How to Write a Module
**1. Set up the development environment.**
```bash
# Clone the repo and navigate to the module template
git clone https://github.com/ruvnet/wifi-densepose.git
cd wifi-densepose/modules
# Copy the template
cp -r _template/ exotic/my-module/
cd exotic/my-module/src/
```
**2. Write the module in Rust (`no_std`).**
Every module implements three exported functions:
```rust
#![no_std]
#![no_main]
// Host API imports (provided by the WASM3 runtime)
extern "C" {
fn csi_get_phase(sc: i32) -> f32;
fn csi_get_amplitude(sc: i32) -> f32;
fn csi_get_variance(sc: i32) -> f32;
fn csi_get_bpm_breathing() -> f32;
fn csi_get_bpm_heartrate() -> f32;
fn csi_get_presence() -> i32;
fn csi_get_motion_energy() -> f32;
fn csi_get_n_persons() -> i32;
fn csi_get_timestamp() -> i32;
fn csi_emit_event(event_type: i32, value: f32);
fn csi_log(ptr: i32, len: i32);
fn csi_get_phase_history(buf: i32, max: i32) -> i32;
}
// Module state (lives in WASM linear memory)
static mut STATE: ModuleState = ModuleState::new();
struct ModuleState {
// Your state fields here
initialized: bool,
}
impl ModuleState {
const fn new() -> Self {
Self { initialized: false }
}
}
#[no_mangle]
pub extern "C" fn on_init() {
unsafe {
STATE = ModuleState::new();
STATE.initialized = true;
}
}
#[no_mangle]
pub extern "C" fn on_frame(n_subcarriers: i32) {
unsafe {
if !STATE.initialized { return; }
// Your per-frame logic here
// Call csi_get_* functions to read sensor data
// Call csi_emit_event(EVENT_TYPE, value) to emit results
}
}
#[no_mangle]
pub extern "C" fn on_timer() {
// Periodic tasks (called at configurable interval)
}
// Panic handler required for no_std
#[panic_handler]
fn panic(_info: &core::panic::PanicInfo) -> ! {
loop {}
}
```
**3. Build to WASM.**
```bash
# Install the wasm32 target
rustup target add wasm32-unknown-unknown
# Build in release mode (optimized for size)
cargo build --target wasm32-unknown-unknown --release
# Strip debug symbols
wasm-strip target/wasm32-unknown-unknown/release/my_module.wasm
# Verify size (should be < 128 KB, ideally < 20 KB)
ls -la target/wasm32-unknown-unknown/release/my_module.wasm
```
**4. Write the TOML manifest.**
```toml
[module]
name = "wdp-exo-my-module"
version = "0.1.0"
description = "Brief description of what it detects"
author = "your-handle"
license = "MIT"
category = "exotic"
difficulty = "medium"
[api]
host_api_version = 1
capabilities = ["READ_PHASE", "READ_VARIANCE", "EMIT_EVENTS"]
[budget]
tier = "standard"
max_frame_us = 5000
max_events_per_sec = 10
memory_limit_kb = 32
[events]
900 = { name = "MY_EVENT", unit = "score" }
901 = { name = "MY_OTHER_EVENT", unit = "confidence" }
```
**5. Test locally.**
The repository provides a mock Host API for desktop testing:
```bash
# Run against the mock host with synthetic CSI data
cargo test --target x86_64-unknown-linux-gnu
# Run against recorded CSI data (if available)
cargo run --example replay -- --input ../../data/recordings/test.csv
```
**6. Package as RVF.**
```bash
# Build the RVF container (requires the wasm-edge CLI tool)
cargo run -p wifi-densepose-wasm-edge --features std -- \
rvf pack \
--wasm target/wasm32-unknown-unknown/release/my_module.wasm \
--manifest wdp-exo-my-module.toml \
--output wdp-exo-my-module.rvf
```
**7. Submit a PR.**
```
modules/exotic/my-module/
wdp-exo-my-module.rvf
wdp-exo-my-module.toml
wdp-exo-my-module.wasm
src/
lib.rs
Cargo.toml
tests/
integration.rs
CHANGELOG.md
```
PR checklist:
- [ ] Module name follows `wdp-{category}-{name}` convention
- [ ] Event type IDs are within the correct category range
- [ ] TOML manifest is complete and valid
- [ ] WASM binary is < 128 KB (< 20 KB preferred)
- [ ] Budget tier is appropriate (verified by benchmark)
- [ ] Integration tests pass against mock Host API
- [ ] No `std` dependencies (pure `no_std`)
- [ ] CHANGELOG.md describes the module
- [ ] Code is formatted with `rustfmt`
- [ ] No unsafe code beyond the Host API FFI bindings
### Signing for Release
Community modules are unsigned during development. For inclusion in the
official registry, a project maintainer signs the RVF with the project
Ed25519 key:
```bash
# Maintainer-only: sign and publish
wifi-densepose-wasm-edge rvf sign \
--input wdp-exo-my-module.rvf \
--key keys/signing.ed25519 \
--output wdp-exo-my-module.signed.rvf
```
Unsigned modules can still be loaded on nodes with `wasm_verify=0`
(development mode). Production nodes require signed RVF containers.
### Event Type ID Allocation
- Categories 100--599: Allocated by this ADR. New modules in existing
categories use the next available 10-ID block.
- Category 600--699 (Exotic): Allocated by this ADR. New exotic modules
use the next available 10-ID block starting at 680.
- Range 900--999: Open for community/third-party modules. Claim a 10-ID
block by adding an entry to `modules/registry.toml` in your PR.
- Conflicts are resolved during PR review on a first-come basis.
---
## References
- ADR-039: ESP32-S3 Edge Intelligence Pipeline
- ADR-040: WASM Programmable Sensing (Tier 3)
- `vendor/ruvector/` -- 76 crates: attention, min-cut, solvers, temporal
tensor, spiking networks, HNSW, quantum circuits, coherence gating
- `vendor/midstream/` -- 10 crates: AIMDS threat detection, DTW/LCS
temporal comparison, attractor dynamics, LTL verification, meta-learning
- `vendor/sublinear-time-solver/` -- 11 crates: O(log n) solvers,
PageRank, GOAP planning, psycho-symbolic reasoning, WASM neural inference
- Liu et al., "Monitoring Vital Signs and Postures During Sleep Using
WiFi Signals," MobiCom 2020
- Niu et al., "WiFi-Based Sleep Stage Monitoring," IEEE TMC 2022
- Zhao et al., "Emotion Recognition Using Wireless Signals," UbiComp 2018
- Yang et al., "WiFi-Based Emotion Detection," IEEE TAFFC 2021
- Li et al., "Sign Language Recognition via WiFi," MobiCom 2019
- Ma et al., "WiFi Sensing with Channel State Information," NSDI 2019
- Adib et al., "Smart Homes that Monitor Breathing and Heart Rate,"
SIGCOMM 2015
- Wang et al., "Human Respiration Detection with Commodity WiFi Devices,"
MobiSys 2017
- Halperin et al., "Tool Release: Gathering 802.11n Traces with Channel
State Information," ACM CCR 2011
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