Project

Adaptive Sensor Fabric (ASF)

Edge-first, sensor-agnostic AI middleware for urban intelligence.

Context

This project is a proposal submission for the Department of National Defence IDEaS Program — Challenge CFP006 - CH15: "Turning Urban Data into Real-Time Insight through AI". The system demonstrates a working prototype built for edge deployment and offline operation.

What It Is

ASF is a middleware platform that converts heterogeneous urban data sources (traffic cameras, weather stations, AIS marine traffic, ADS-B aircraft, air quality monitors, RF sensors, transit systems) into explainable, confidence-scored situational awareness alerts. It does not require new hardware — it sits on top of existing city infrastructure.

Key Capabilities

5+ Sensor Modalities

Visual/optical, radio frequency, environmental, position/tracking (ADS-B, AIS), maritime — via 44+ adapters.

7-Engine Fusion Pipeline

Sensor Intelligence → Knowledge → Context → Traffic → Fusion → Confidence → Decision Intelligence.

Explainable AI

Every alert includes evidence chain, contributing sensors, confidence percentage, and plain-language operator recommendation.

Edge Deployable

Single Docker container, no cloud dependency, runs on laptop or NVIDIA Jetson. Terminal dashboard works without a browser.

Demo: Try It Yourself

The system runs entirely offline with simulated data. No API keys, no internet connection, no databases required.

Quick Start

cd asf-poc
pip install -r requirements.txt
python3 terminal_dash.py        # Terminal dashboard, no internet
python3 dashboard.py --trial     # Web map + terminal UI

See the full Demo Procedure for a complete walkthrough with 5 demo acts (15 minutes total).

Live Map

Below is the interactive ASF map showing Halifax-area zones, observations, and fusion events. The map server runs alongside this site.

Open full-screen →

Architecture

All sensor inputs normalize to a common Observation schema (timestamp, location, sensor_type, confidence, classification). The 7-engine pipeline processes observations through successive layers of abstraction, producing Assessment objects with provenance tracking back to source observations.

Pipeline Flow

Sensor Inputs → Observation Schema → SIE → Knowledge → CIE →
Traffic → FIE → Confidence → DIE → Alerts

Technical Details

  • Codebase: ~8,000 lines of Python across 117 source files
  • Tests: 26 automated tests, all passing
  • Protocols: MQTT, REST/HTTP, WebSocket, RTSP/RTMP, file-based
  • Geofencing: 9 Halifax zones with configurable centers and radii
  • Fusion: Temporal (500ms window) + spatial (200m radius) — JDL Level 1-2
  • Deployment: Docker (slim Python 3.12, 450MB image)
  • Live integrations: ECCC weather, AQHI, USGS earthquakes, OpenSky ADS-B, AISHub AIS, Blitzortung lightning, CWFIS wildfires, CHS tides, Halifax GTFS-RT transit

Privacy & Compliance

Privacy-by-design architecture: no PII collection, public data only, edge processing keeps raw data local, structured classifications only. Compliant with PIPEDA and applicable provincial privacy legislation. Designed for the DND IDEaS program's Essential Outcome #4.

Interested in the technical details?

The full proposal, architectural documentation, and demo procedure are available.

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