TORUS detects, classifies, and prioritises facility security events for AI/HPC data centers. Machine learning runs where the data is born: on the node, at the gateway, and in the operations layer. Each tier turns raw signal into a smaller, more certain facility event, so what travels the network and reaches the operator is a classified track, not a raw feed or a bare alarm.
Footfall, vehicle, digging, generator vibration, or handling noise. The system reports what it is, with a confidence, not merely that something happened.
A seismic cue plus an acoustic cue from a neighbouring node is a stronger facility-security track than either alone. Fusion is where confidence is earned.
Wind, rain, traffic, wildlife, and facility maintenance are the main false-alarm sources for an unattended sensor. On-device models learn the background and suppress nuisance alarms.
AI is distributed, not centralised. Every tier runs the inference its power and vantage allow, and passes a smaller, richer event to the next.
Geophone, microphone, thermal, LiDAR, RF at the node.
Node labels the event and its confidence; discards background.
Corroborate across the local ring; run visual inference on cue.
Form tracks across the facility; suppress false alarms.
A classified track with location and confidence, ready to act on.
Each stage reduces data and raises certainty. A raw waveform becomes a labelled event, a set of events becomes a track, and a track becomes a decision, so the network carries kilobytes of event data rather than megabytes of feed.
| Tier | Runtime | Approach |
|---|---|---|
| Edge | ESP32-S3, no accelerator | Small quantized classifiers (TinyML) sized to RAM and the battery budget; wake-gated so inference runs only on a real cue |
| Gateway | NVIDIA Jetson Orin | Accelerated detection / tracking on thermal and visual streams; on-site so raw imagery stays local |
| Operations | CCISRT server | Rule-based correlation today (explainable, auditable); learned multi-target tracking and classification fusion drop in behind the same track interface |
| All tiers | Isaac Sim | Facility scenarios generate labelled data and validate detection and false-alarm rates before deployment trials |
Explainable first. The operations correlator ships as transparent rules so an operator can see why a track formed. Learned models are introduced behind that interface, with the rule-based path retained as a fallback and a sanity check.
Audio and imagery are processed where captured; only compact labelled events cross the network.
Sending decisions instead of feeds cuts bandwidth and power while keeping facility networks focused on compact event data.
Track formation and operations actions are logged, so an analyst can reconstruct why the system decided what it did.
| Now | Next |
|---|---|
| On-device seismic / acoustic classification at the node | Larger vocabularies and per-site adaptation via OTA model updates |
| Rule-based correlation and track formation at the operations layer | Learned multi-target tracking and cross-domain classification fusion behind the same interface |
| Isaac Sim facility scenario data for validation | Active learning from operator confirmations to cut false alarms over time |
| TIA-942 and OCP-aligned event evidence | Northbound integration API adapters for PSIM, DCIM, BMS, VMS, SIEM, and Redfish |
TORUS is an independent platform. Company names and product model numbers referenced in this document (including but not limited to Espressif ESP32-S3 and NVIDIA Jetson, Orin, and Isaac Sim) are used solely for engineering and bill-of-materials identification and imply no affiliation with or endorsement by those companies. Supply of any such third-party product to the designers, integrators, or evaluators of the TORUS platform remains at the sole discretion of the respective owning company, organisation, or legal entity. PacketFive Design Services can adapt TORUS AI/ML models and integrations for other customer requirements. "FOR EVALUATION" is a document-handling marking only and denotes no security classification. All trademarks are the property of their respective owners.