Why edge‑primary autonomous systems require more than raw signals
A recurring misunderstanding in discussions about edge AI and event recognition is the assumption that events emerge directly from sensor data.
In practice — and in every reliable real‑world system — events emerge from comparison, not observation.
This is why the OAII Base Model treats Sensor Knowledge as a first‑class object, even though it is not itself a sensor, a signal, or an event.
The Core Distinction
In the OAII model:
- Signals answer: What is being observed right now?
- Sensor Knowledge answers: What does “normal,” “expected,” or “meaningful” look like here?
- Events answer: What changed, occurred, or now matters?
Without Sensor Knowledge, a system can observe — but it cannot interpret.
What Sensor Knowledge Is (and Is Not)
Sensor Knowledge is not:
- a global model
- a centralized dataset
- a clinical or diagnostic construct
- a static configuration file
Sensor Knowledge is:
- locally scoped
- incrementally learned
- sensor‑specific or sensor‑family‑specific
- explicitly tied to a World
It provides the reference frame against which current signals are evaluated.
Examples of Sensor Knowledge
Depending on modality, Sensor Knowledge may include:
- Calibration data (sensor offsets, drift profiles)
- Baselines (typical motion levels by time of day)
- Learned routines (customary entry/exit times, room usage patterns)
- Thresholds and tolerances (what counts as deviation vs noise)
- Feature summaries or embeddings (audio/visual descriptors, not raw media)
- Geofences and zones (for GPS or spatial inference)
Crucially, this knowledge is local and World‑specific.
Why Sensor Knowledge Must Be First‑Class
1. Events Are Comparisons, Not Detections
In the OAII Event model, recognition is defined as:
Comparing current observations to Sensor Knowledge within a World.
This comparison is what drives:
- Event candidacy
- confidence updates
- state transitions (CANDIDATE → ACTIVE → COMPLETED)
Treating Sensor Knowledge as implicit or hidden makes this process opaque and brittle.
2. Edge Systems Cannot Depend on Global Context
Edge‑primary systems must:
- operate offline
- adapt to local conditions
- respect privacy constraints
Sensor Knowledge enables local autonomy by allowing interpretation without cloud calls or global retraining.
3. Knowledge Evolves Even When Sensors Don’t
A sensor may be unchanged, but:
- routines shift
- environments change
- users adapt behavior
By modeling Sensor Knowledge explicitly, the system can evolve interpretation without changing hardware or raw signal pipelines.
How Sensor Knowledge Fits the OAII Object Model
Sensor Knowledge sits at the intersection of several OAII objects:
- Sensors produce Signals
- Sensor Knowledge contextualizes those Signals
- Events are recognized by comparing the two
- World supplies axes (time, space, activity) that scope interpretation
This is why Event methods such as:
evaluate_event_candidaterevise_event
explicitly reference Sensor Knowledge.
Why This Matters for Aging‑in‑Place
In aging‑in‑place scenarios, correctness comes from understanding change relative to the individual, not from generic models.
Examples:
- “No motion detected in kitchen for 3 hours”
- “Front door opened at an unusual time”
- “Sound pattern deviates from baseline routine”
None of these require medical inference — but all require Sensor Knowledge.
The Architectural Payoff
By making Sensor Knowledge a first‑class concept:
- Event recognition becomes explainable
- Edge processing becomes efficient
- Privacy boundaries remain intact
- Worlds can differ without breaking interoperability
Most importantly, events become meaningful without becoming speculative.
This explainer reflects the intent of the OAII Base Model v0.1.x and informs future formalization of Sensor Knowledge within the Knowledge object family.
Open Autonomous Intelligence Initiative 
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