Edge Intelligence Architecture (RAEIF)

Resource-Aware Edge Intelligence for Real-Time Anomaly Triage

Lightweight neural models (SNN, CAE, Isolation Forest) execute local sensor inference with 18.4ms latency on Raspberry Pi 4 edge nodes. When critical multi-sensor drift occurs, aggregated telemetry triggers Claude 3.5 Sonnet cognitive agents for root-cause diagnosis.

$ pip install wvamoss-edge-runtime

18.4ms

SNN Measured Latency

0.892

Detection F1-Score

42MB

RAM Footprint

ARM64 Native Local CSV Fallback (Plan C) Zero MQTT Lock-in

NODE_01 pi4-strawberry-hydroponic // RAEIF Telemetry

STREAMING

ACTIVE MODEL:

Latency: 18.4ms · RAM: 42MB · F1: 0.892

Sensor 01: Water pH 6.12 pH

Threshold: 5.5 - 6.5 NOMINAL

Sensor 02: EC Nutrients 1.68 mS/cm

Threshold: 1.2 - 2.0 NOMINAL

Sensor 03: Temperature 19.4 °C

Threshold: 18.0 - 22.0 °C NOMINAL

Sensor 04: Dissolved O₂ 6.2 mg/L

Ground Truth: > 5.0 mg/L OPTIMAL

Live Telemetry Waveform Stream (EC Sliding Window) Buffer: 50 Samples

Cognitive Reasoning Layer (Claude 3.5 Sonnet Diagnostic Agent) Prompt Caching · Tool Use

# System operating nominally. SAW Decision Gate: F1=0.892, Latency=18.4ms.

✓ Local edge loop handling all sensor windows. Zero unnecessary cloud token spend.

Edge Hardware: Raspberry Pi 4 Model B (Quad-core Cortex-A72 @ 1.5GHz) Edge Engine Active

Systems Architecture

Decoupled Edge-to-Agent Pipeline

A multi-tier architecture engineered to eliminate 98% of unnecessary cloud LLM inference while maintaining zero-latency physical edge safety.

01 // LOCAL DATA ACQUISITION

High-Frequency Ingestion

Physical sensors (pH, EC, DO, Temperature) ingest into sliding ring buffers at 1.0Hz. Z-score normalization and sliding window slicing execute directly in local memory.

Plan C CSV fallback · Zero external message broker

02 // ADAPTIVE MODEL SELECTION

SAW Decision Selector

Simple Additive Weighting (SAW) dynamically ranks SNN (32-16), CAE (16-8-16), and Isolation Forest. Models are selected based on real-time CPU thermal headroom and detection urgency.

Weights: Latency 0.35 · F1 0.25 · RAM/CPU 0.20

03 // COGNITIVE REASONING LAYER

Claude 3.5 Sonnet Agent

When persistent multi-window anomaly drift breaches safety gates, an aggregated vector is forwarded to Claude 3.5 Sonnet via Tool Use. The agent synthesizes root causes and fires automated actuator relays.

Prompt Caching · Sub-300ms Automated Actuation

Empirical Evaluation

Raspberry Pi 4 Physical Benchmark Data

Hardware: BCM2711 Cortex-A72 @ 1.5GHz (Raspberry Pi 4B)

MODEL ARCHITECTURE	F1-SCORE (BENEFIT)	MEASURED LATENCY	RAM FOOTPRINT	CPU USAGE	SAW RANKING
Spiking Neural Network (SNN 32-16) Sigmoid > 0.5 Activation	0.892	18.4 ms	42.1 MB	12.4%	0.884 [RANK 1]
Convolutional Autoencoder (CAE 16-8-16) Error > 95th Percentile	0.914	46.2 ms	88.5 MB	24.8%	0.762 [RANK 2]
Isolation Forest (iForest) 100 Trees, 256 Max Samples	0.841	84.0 ms	124.0 MB	38.1%	0.618 [RANK 3]

Decision Matrix

Interactive SAW Weight Simulator

Adjust the multi-criteria optimization trade-offs below to see how the framework switches the active edge AI model in real time.

Latency Weight (Cost): 0.35

F1-Score Weight (Benefit): 0.25

SAW MODEL RANKING OUTPUT OPTIMAL: SNN (32-16)

1. Spiking Neural Network (SNN) Lowest latency spike inference for real-time edge safety

Score: 0.884

2. Convolutional Autoencoder (CAE) Higher precision reconstruction, moderate memory cost

Score: 0.762

3. Isolation Forest (iForest) Tree ensemble baseline

Score: 0.618

Developer First

Native Python SDK for Edge Nodes

The RAEIF SDK embeds into any telemetry loop in less than 15 lines of code. Direct support for hardware GPIO, ADC, and local serial buses.

✓ Python 3.9 - 3.12 / Native C++ Bindings

✓ Raspberry Pi 4, Jetson Nano, & Orange Pi verified

✓ Native Claude 3.5 Sonnet Tool Use integration

app_edge_diagnostics.py Python 3.11

from wvamoss_edge import TelemetryEngine, SAWEvaluator, ClaudeAgent

# 1. Connect physical sensor array
engine = TelemetryEngine(sensors=["pH", "EC", "Temp", "DO"], window_sec=1.0)

# 2. Select model dynamically via SAW criteria
model = SAWEvaluator.select(latency_cost=0.35, f1_benefit=0.25)

# 3. Autonomous detection & triage loop
for window in engine.stream():
    anomaly = model.predict(window)
    if anomaly.is_persistent:
        # Stream anomaly vector to Claude 3.5 Sonnet diagnostic agent
        triage = ClaudeAgent.diagnose(anomaly)
        triage.dispatch_actuator()