Ipsn experiments

IPSN Experiments

Use this before submission when the evaluation is not yet locked. IPSN reviewers are sensor-systems and information-processing specialists; the evaluation is where a good idea is won or lost. The organizing principle is evidence measured on real hardware against real ground truth — the evaluation must test the sensing claim the paper actually makes, on platforms and baselines a skeptic would accept.

Evaluation audit

• Measure on real hardware, not just simulation. Simulation can motivate or scale-test, but a sensing claim needs real sensors: an estimator run on real traces, a pipeline profiled on the actual MCU, a deployment in a real environment. "Simulation only" is IPSN's classic reject.
• Instrument ground truth. Localization needs surveyed positions; detection needs hand-labeled events; a physical estimate needs a co-located reference instrument. Report the ground truth's own error — perfect ground truth is a red flag.
• Measure energy, latency, and footprint on the platform. Report joules/µJ per operation from an instrumented power rail (name the shunt/instrument/sampling rate), end-to-end latency on the real SoC at a stated clock, and RAM/flash used vs available. Estimated energy is not measured energy.
• Choose fair, real baselines. Include the strongest prior method and a simple-but-reasonable alternative (often a classical-DSP or analytic baseline), run under equal conditions on the same hardware. For IP-track claims, compare to an estimation-theoretic bound (e.g., a Cramér-Rao-style lower bound) where one exists.
• Isolate the learning's marginal value (on-device/TinyML). Ablate the learned component against a heuristic/DSP baseline so "did the model help, or the sensing setup?" is answered.
• Design limits in, not on. Know before you deploy which site-specificity, calibration drift, and generalization limits the study will have, and instrument to bound them.

Claim-to-evidence design table

Sensing claim	Matching evidence	Reject pattern avoided
"Estimator is more accurate"	Error vs ground truth on real traces, with CIs, vs a tuned baseline / a bound	"Simulated inputs only"
"Runs within an energy budget"	Measured µJ/op on an instrumented rail on the real MCU	"Energy estimated from datasheet"
"Localizes to X meters"	Surveyed ground-truth positions; error distribution, not just mean	"Ground truth from the same model being tested"
"Deploys reliably"	Yield, sync error, packet loss over a real deployment duration	"Idealized single-run numbers"
"The on-device model adds value"	Ablation vs classical DSP / heuristic on the same hardware	"Model's marginal contribution never isolated"
"Scales to N nodes"	Real or emulated multi-hop at realistic scale, with the bottleneck named	"Two-node bench test, universal claim"

On-device / TinyML measurement floor

[Platform]   exact MCU/SoC, clock, RAM/flash; the sensor and sampling regime
[Energy]     µJ per inference/op, instrument named; duty cycle if always-on
[Latency]    end-to-end on-device latency; number of runs and variance
[Footprint]  model/pipeline RAM+flash vs available; what had to be quantized/pruned
[Contamination] for learned components, keep train/field data disjoint; report the split
[Ablation]   learned component vs DSP/heuristic baseline on the same node

Ground-truth and calibration floor

• Name the ground-truth reference and its own measurement error; a claim can be no better than its reference.
• Document calibration: procedure, when it was done, and drift over the deployment.
• Archive raw sensor traces and the calibration data, not just derived metrics (see ipsn-reproducibility).

Deployment reporting floor

• Report yield (fraction of expected data received), synchronization error, packet loss, and energy over the actual deployment duration, not a best single run.
• State the environment and why it is representative (or not) — external validity for a physical system is site-bound.
• Report failures: nodes that died, data gaps, and what caused them. Honest deployment reporting is itself a contribution and a reviewer trust signal.

Vignette: evaluating a localization estimator (IP track)

The paper claims a new estimator localizes better than the prior method. The matching plan: collect real RF/acoustic traces at surveyed positions; run both estimators on the same traces under equal tuning; report the full error distribution (not just the mean) with confidence intervals; compare against the relevant estimation-theoretic bound; and state the environments (indoor/outdoor, multipath regimes) as a bounded external-validity limit — every number traceable to a logged run and the surveyed ground truth in the artifact.

Output format

[Evaluation readiness] strong / adequate / weak
[Claim -> evidence map] <claim: platform / ground truth / metric / statistic>
[Real-hardware check] measured on real sensors/MCU, not simulation only? yes/no
[Energy accounting] <µJ/op measured? instrument named? footprint reported?>
[Baseline fairness] <strongest prior + simple baseline, equal conditions, same hardware?>
[Limits-by-design] <site / calibration / generalization -> instrumentation to bound it>
[Decision-critical next run] <one experiment or deployment extension>