@trillboards/edge-sdk

Turn any screen into an AI-powered DOOH advertising endpoint. Edge AI audience sensing, ad delivery, federated learning, and cloud inference in one package. Linux and Windows.

Quick Start

npm install @trillboards/edge-sdk
npx trillboards-edge download-models
npx trillboards-edge start --config trillboards.config.json

What This Does

The Edge SDK runs on digital signage hardware (kiosks, vending machines, billboards, CTV devices) and provides:

• Audience sensing — on-device face detection, gaze tracking, emotion analysis, foot traffic estimation via ONNX models (no cloud dependency)
• Ad delivery — Chromium browser lifecycle, display management, VAST/VPAID ad rendering
• Federated learning — privacy-preserving on-device model training with sparse gradient uploads
• Cloud inference — Gemini Vision scene analysis, content moderation, contextual targeting
• Device management — heartbeat, health monitoring, auto-recovery, signal buffering for offline resilience

All audience data stays on-device. Only aggregated, anonymized metrics are transmitted.

Configuration

Create trillboards.config.json:

{
  "apiBaseUrl": "https://api.trillboards.com",
  "deviceToken": "your-device-token",
  "camera": { "enabled": true, "width": 640, "height": 480, "fps": 10 },
  "audio": { "enabled": true },
  "models": { "dir": "./models", "executionProvider": "cpu" },
  "kiosk": { "enabled": true, "url": "https://screen.trillboards.com" },
  "cloud": { "enabled": false },
  "federated": { "enabled": true },
  "logLevel": "info"
}

Programmatic Usage

import { EdgeAgent, loadConfig } from '@trillboards/edge-sdk';

const config = loadConfig('trillboards.config.json');
const agent = new EdgeAgent(config);

agent.on('started', ({ fingerprint, tier }) => {
  console.log(`Device ${fingerprint} running at TIER_${tier}`);
});

await agent.start();

CLI Commands

| Command | Description | |---------|-------------| | trillboards-edge start [--config path] | Start the edge agent | | trillboards-edge download-models [--dir path] | Download ONNX models (BlazeFace, YAMNet) | | trillboards-edge version | Print SDK version |

Capability Tiers

The SDK auto-detects device capabilities and adjusts inference accordingly:

| Tier | RAM | NPU | Models | FPS | |------|-----|-----|--------|-----| | TIER_1 | < 2 GB | No | Face only | 2 | | TIER_2 | 2-4 GB | No | Face + Audio | 5 | | TIER_3 | 4-8 GB | Optional | All models | 10 | | TIER_4 | 8+ GB | Yes | All + high-res | 15+ |

Execution Providers

• cpu — works everywhere (default)
• openvino — Intel integrated GPU (2-3x faster)
• directml — Windows GPU acceleration
• cuda — NVIDIA GPU

Sub-Packages

This is the umbrella package. For fine-grained control, install sub-packages individually:

| Package | Purpose | |---------|---------| | @trillboards/edge-core | Device identity, Socket.io, config, signal buffer, health monitoring | | @trillboards/edge-sensing | ONNX face detection, audio classification, audience metrics | | @trillboards/edge-ads | Chromium browser lifecycle, display management, ad delivery | | @trillboards/edge-federated | On-device federated learning, sparse gradient upload | | @trillboards/edge-cloud | Gemini Vision scene analysis, contextual targeting | | @trillboards/edge-platform-linux | V4L2 camera, PulseAudio, systemd, kiosk mode | | @trillboards/edge-platform-windows | DirectShow, RTSP IP cameras, WASAPI audio |

Platform Support

• Linux — Ubuntu 20.04+, Debian 11+, Raspberry Pi OS (ARM64)
• Windows — Windows 10/11 (x64)

Get a Device Token

npx @trillboards/ads-sdk init

Or register via the Trillboards API.

License

MIT