Live hardware system
Edge-AI Field Camera
Solar power, embedded inference, resilient camera transport, and public operational proof.
What I owned
Hardware integration, embedded services, relay architecture, telemetry UI, security boundary, and operations.
Problem
Expose a real solar-powered edge system publicly without leaking private infrastructure or letting live hardware failures become invisible.
Built
Integrated camera streaming, on-device RKNN inference, solar telemetry, thermal and fan health, Cloudflare relay routing, and production diagnostics.
Outcome
A live hardware system that behaves like a maintained product: opt-in streams, health fallbacks, quality checks, recovery paths, and deploy-time regression gates.
The conditions that shaped the system
The field node must tolerate intermittent power, address changes, and upstream service restarts.
Public visitors need useful proof without receiving camera credentials or infrastructure tokens.
Media transport must degrade through WebRTC, HLS, MJPEG, and snapshot fallbacks without hiding failure state.
Tradeoffs made explicit
Read-only public edge
Media and health can traverse the public gateway; physical writes stay behind signed sessions and server-held relay credentials.
Always-on relay ownership
The camera path terminates on a dedicated relay host instead of depending on a personal laptop.
Observable failure modes
The interface distinguishes stale telemetry, media failure, service failure, and training readiness instead of showing one generic offline state.
Demonstrates embedded Linux services, power telemetry, camera transport, edge inference, and full-stack operational visibility in one public system.