ruvnet--RuView/docs/adr/ADR-314-information-gain-scheduler.md

ADR-314: Information-gain scheduler — sample the most informative radios

• Status: Accepted — initial implementation (ADR-300 phase 3)
• Date: 2026-08-11
• Deciders: ruv
• Tags: scheduling, active-sensing, information-gain, edge, energy, fusion, phase-3

Context

This ADR is a child of ADR-300 (perception substrate program) and owns primitive #14, information-gain scheduler. In the ADR-300 DAG it is a phase-3, research-forward primitive that sits on top of the fused world state and pairs with ADR-309 (active sensing): ADR-309 decides what to probe (waveform, sensing task); this ADR decides which radios/modalities to spend budget on next. It is authored as Proposed and is not implemented by the phase-1 swarm.

With multiple sensors, processing every stream at full rate is wasteful: many radios are, at any moment, contributing little to the current estimate while consuming compute, energy, and bandwidth — the three scarce resources on the edge nodes RuView targets (ESP32-S3/C6 and small gateways). Treating all sensors equally is precisely the design that does not survive a real deployment of "hundreds of sensors."

The scheduler assigns each candidate sensor/modality a value

Value(sensor) ≈ expected uncertainty reduction / (compute + energy + bandwidth)

and spends the next sampling/processing budget on the highest-value sensors. Expected uncertainty reduction is estimated before paying for the measurement, which is why the scheduler needs a model of what each sensor is likely to tell it — supplied by the fused state's covariance and the RF twin's forward model, not by actually sampling.

Relevant existing assets to build on rather than duplicate:

• ADR-311 (fusion) maintains the fused state and its covariance — the current uncertainty the scheduler is trying to reduce. Expected uncertainty reduction is computed against that covariance, not a private one.
• ADR-315 (RF twin) provides the per-sensor forward model used to predict a candidate measurement's expected informativeness before sampling.
• ADR-320 (RuView sensor HAL, phase 2) exposes each radio's real compute/energy/bandwidth cost descriptors; the denominator is read from the HAL, not guessed per platform.
• ADR-309 (active sensing) is the paired actuator: the scheduler ranks sensors, ADR-309 chooses the probe on the chosen sensor.
• ADR-302 (observability) defines the phenomenon the estimate is for, so the scheduler prioritizes uncertainty reduction on the objective that matters, not on nuisance dimensions.

Options considered

1. Round-robin / process-everything scheduling. Rejected: burns edge compute and energy on redundant streams and does not scale to large fleets; the strategic and external reviews named exactly this as an edge-deployment blocker.
2. Static priority per sensor type (e.g. always prefer mmWave). Rejected: ignores that a sensor's current informativeness depends on the scene and the present uncertainty — a well-placed WiFi link can dominate an occluded mmWave node in a given moment.
3. A value-of-information scheduler that ranks sensors by expected uncertainty reduction per unit cost, using the ADR-311 covariance and ADR-315 forward model, with costs from the ADR-320 HAL. Chosen.

Decision

Define an information-gain scheduler that allocates the next sampling/processing budget across available radios by value of information.

1. Value function

• For each candidate sensor/modality, estimate expected uncertainty reduction on the ADR-302 objective by evaluating how much a predicted measurement (via the ADR-315 forward model) would shrink the ADR-311 fused-state covariance — a value-of-information estimate made before paying for the measurement.
• Divide by the sensor's cost — compute + energy + bandwidth — read from the ADR-320 HAL descriptors. The exact weighting of the three cost terms is a deployment policy (a battery node weights energy heavily; a wired gateway weights bandwidth), configured, not hardcoded.

2. Allocation

• Rank candidates by value and spend the budget on the top set, subject to a configurable floor that guarantees each sensor is sampled at least occasionally (so a sensor whose value is currently low is not starved into permanent blindness and can be re-evaluated as the scene changes).
• The scheduler emits an allocation, not a measurement; ADR-309 active sensing chooses the probe/waveform on each selected sensor, and the fusion layer (ADR-311) incorporates the result.

3. Governance and honesty

• Skipping a sensor for a cycle is a deliberate reduction in coverage; the scheduler records which sensors were sampled so downstream evidence (ADR-304) reflects the actual sensing that occurred, and observability (ADR-302) can raise UNKNOWN for a zone that went under-sampled rather than reporting a stale estimate as current.

Evidence discipline

• Expected-uncertainty-reduction estimates are model predictions from the ADR-315 twin (simulation, L0 per ADR-282, SYNTHETIC); a scheduling decision is a resource choice, never a sensing claim.
• Any energy/latency/throughput improvement figure requires real-silicon measurement with a reproducer before it is tagged MEASURED (CLAUDE.md hardware rule). This ADR asserts no efficiency number.

Consequences

• Edge deployments spend scarce compute, energy, and bandwidth where they buy the most certainty, making "hundreds of sensors" operationally tractable — a capability the reviews flagged as critical for edge deployment.
• Quality is bounded by the accuracy of the ADR-315 forward model (informativeness prediction) and ADR-320 cost descriptors; a poor forward model degrades to near-round-robin, which is safe but not optimal. The sampling floor bounds the worst case.
• Hard dependency on ADR-311 (covariance), ADR-315 (forward model), and ADR-320 (cost descriptors), and paired with ADR-309; this ADR builds none of those.
• Being phase 3, this is design intent sitting on the fused world state and is expected to be revised as ADR-309, ADR-311, ADR-315, and the ADR-320 HAL land.

Validation

• Unit tests: the value function is a deterministic function of covariance + forward model + cost descriptors; a sensor predicted to reduce objective uncertainty more per unit cost ranks above one that reduces it less; the sampling floor guarantees eventual re-evaluation of a low-value sensor.
• Integration test: on a synthetic multi-sensor scene, the scheduler reduces objective uncertainty faster per unit modelled cost than round-robin, and raises ADR-302 UNKNOWN for a deliberately starved zone rather than reporting a stale estimate.
• Field validation (deferred, real-silicon): energy/latency/throughput on an instrumented multi-node deployment, reported as MEASURED with a reproducer. Until then all informativeness and cost figures are SYNTHETIC/L0. No efficiency number is asserted by this ADR.