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ipsn-experiments
GitHub指导IPSN论文实验设计与审计，强调基于真实硬件和地面真值进行能量、延迟及精度测量。涵盖基线选择、TinyML消融分析及证据匹配，确保评估严谨性以应对同行评审。
Trigger Scenarios
设计或审计IPSN论文的实验部分
验证传感器系统评估的真实性与公平性
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ipsn-experiments -g -y
SKILL.md
Frontmatter
{
"name": "ipsn-experiments",
"description": "Use when designing or auditing IPSN-lineage evaluations, covering real testbeds and deployments, ground-truth instrumentation, energy\/latency\/footprint measurement on real hardware, on-device\/TinyML profiling, estimation-theoretic baselines and bounds, and matching evidence to the shape of each sensing claim across the IP and SPOTS tracks."
}
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>
Version History
- 9f86f09 Current 2026-07-19 16:04
