Benthic Guardian
Benthic Guardian is Team Terronix’s SLIoT Challenge 2026 finalist system — an intelligent IoT platform to protect Sri Lanka’s marine biodiversity through real-time coral-reef monitoring and early warning. Rising sea temperatures and bleaching outpace slow, expensive survey methods; Benthic Guardian closes that gap with underwater temperature sensing, live cloud telemetry, a monitoring dashboard, and AI forecasting for heat stress.
Hardware & ingest: ESP32-based sensor nodes POST readings (`sensor_uid` + temperature) to a FastAPI backend (`POST /data`). Approved devices write into PostgreSQL (Supabase in production; SQLite locally). Auth, sensor provisioning, scheduled prediction/alert jobs, and CORS-ready deploy on Render complete the API layer used by both field devices and the web app.
Dashboard: a Next.js + TypeScript UI for login, live readings, maps, analytics, forecasts, and alerts — wired to the backend via `NEXT_PUBLIC_API_URL`.
AI / digital twin (model repo): satellite SST + DHW stand in for seabed loggers in the prototype. A Physics-Informed Neural Network (PINN) builds a continuous space–time temperature field (advection–diffusion residual) to fill between sensors; an ANN–LSTM with 60-day history forecasts SST/DHW at +1 / +3 / +7 days (beating persistence at 3–7 days in evaluation); a NOAA-style DHW risk scorer maps Safe / Watch / Alert. Sustainable deployment research includes microbial fuel cells, solar power, and marine-grade cabling toward long-term reef use.
Year
Features
Technologies
- Detecting bleaching risk early enough for action, not only after reefs are already damaged
- Building a credible digital twin when real seabed loggers are still limited — satellite SST/DHW as proxy
- PINN alone underperforms short-term forecasts vs persistence; map and forecast roles must stay separated
- Field-to-cloud reliability: approved sensors, every-sample ingest, CORS, and cold-start deploys
- Scoring health with sustained heat (DHW), not absolute temperature alone
- End-to-end IoT pipeline: ESP32 → FastAPI → Supabase → dashboard + scheduled ML jobs
- Triangle + noise sensor simulation from SST for prototype; path to replace with live loggers
- PINN for spatial fill/maps; 60-day ANN–LSTM for +1/+3/+7 forecasts that beat persistence at 3–7 days
- Device POST without JWT keyed by registered sensor_uid; provision scripts for field prototypes
- NOAA-style DHW Watch/Alert thresholds combined with anomaly, duration, and warming rate
Key Features
ESP32 underwater temperature sensing with live cloud transmission
FastAPI ingest (`POST /data`), auth, sensor provisioning, and scheduled prediction/alert jobs
PostgreSQL / Supabase storage with migration tooling from local SQLite
Next.js dashboard: login, sensor readings, maps, analytics, forecasts, and alerts
PINN spatial twin — continuous (lat, lon, time) → temperature with heat PDE residual
ANN–LSTM 60-day forecaster for +1 / +3 / +7 day SST & DHW
DHW-aware bleaching risk scoring (Safe <4, Watch 4–8, Alert >8)
Risk maps / Folium visualization and hold-out evaluation notebooks
Prototype triangle multi-sensor simulation from satellite SST (+ noise) until real loggers
Sustainable hardware research: microbial fuel cells, solar, marine-grade cabling
Deploy path: Render (Docker/TensorFlow backend) + Vercel dashboard + field ESP32 POSTs
Technologies
Learnings
Shipped an end-to-end AIoT stack from ESP32 nodes through cloud API, dashboard, and ML forecasts
Combined physics-informed spatial modeling (PINN) with sequence forecasting (ANN–LSTM) for complementary map vs day-ahead tasks
Designed device ingest without JWT while keeping sensors registered and approved server-side
Operated production-minded data layering: SQLite locally, Supabase Postgres + Render for deploy
Evaluated forecast skill against persistence/climatology and tied risk to DHW thresholds, not raw °C alone
Worked as Team Terronix across hardware, backend, ML, and marine-conservation context for SLIoT finals
Highlights
SLIoT 2026 Finalist
PINN + ANN–LSTM
Real-time Reef Alerts
Next Project