跳到主要内容
知仓学习社ZHICANG

sensys-experiments

Use when designing or auditing a SenSys evaluation — energy and low-power measurement with a named instrument, real-testbed and deployment realism, …

不碰外部(只输出文字)无严重或高危命中brycewang-stanford/Awesome-Journal-Skills

它会碰到什么

扫了多少1 个文本文件,5 KB
它会碰到什么不碰外部(只输出文字)
命中总数0 处
命中统计严重 0 · 高 0 · 中 0 · 低 0

这一栏是扫描器报的事实,不是结论。命中多不等于有毒(安全工具、规则库、示例脚本本来就会包含危险写法),命中少也不等于干净。它和你手上的凭据、文件、网络有什么关系,需要你自己看。

技能内容

SenSys Experiments

At SenSys the evaluation is the contribution's proof. A mechanism is only as strong as the

measurements that show it behaving on real hardware, under real energy budgets, against honest

ground truth. This skill audits an evaluation for the failure modes SenSys reviewers flag first:

unmeasured energy, simulation standing in for deployment, and accuracy scored against an unstated

truth.

The five measurement axes

Every SenSys evaluation should be explicit about these; a gap in any one is a reviewer question.

| Axis | What to report | Common failure |

|---|---|---|

| Energy / power | Energy per operation, average current (µA/mA), duty cycle — with instrument + sampling rate + wake/sleep boundaries | "Low-power" as an adjective; no method |

| Latency | On-device latency as a distribution (median, tail), not a single number | One workstation timing, no tail |

| Accuracy vs. ground truth | Metric plus how truth was obtained and its own error | Accuracy with unstated reference |

| Deployment realism | Node count, placement, environment, duration, uptime/failures | One-run bench result called a deployment |

| Footprint (embedded AI) | Quantized model size, RAM/flash peak on the actual MCU | Offline model size on a workstation |

Measure energy like it is the headline

Because it is. State the instrument (source-meter, shunt + DAQ, or power monitor), its

sampling rate, and the boundaries of what you integrated (does "energy per inference"

include sensor acquisition and radio, or only compute?). Report energy with the same rigor as a

latency CDF — ideally a power trace annotated with the phases it covers.

Energy reporting template (put the method in the paper, not just the number):
  Instrument:     Keithley/Otii/INA-class monitor, model + firmware
  Sampling rate:  e.g. 10 kHz; enough to resolve the wake spike
  Integration:    from sensor-on to label-out; radio TX included? Y/N
  Boundaries:     sleep floor measured separately; not inferred from datasheet
  Report:         energy/op + duty cycle + projected lifetime with battery/harvester spec

A projected battery life or harvesting budget must be derived from measured draw, not from a

datasheet's nominal current — reviewers who have deployed will catch the difference.

Ground truth is a first-class artifact

An accuracy number is only as trustworthy as the truth it is scored against. State how reference

labels were obtained — a reference instrument, a controlled stimulus, or a documented human

annotation protocol — and the truth's own uncertainty. Sensor experiments where "ground

truth" is another uncalibrated sensor, or where labels were assigned by the authors without a

protocol, invite exactly the challenge that sinks the result in review.

Deployment vs. bench, kept separate

Bench experiments control variables to isolate a mechanism; deployments expose it to reality.

Report both and label which is which. A deployment carries node count, placement, environmental

conditions, duration, and honest uptime/failure accounting — a node that died on day 3

is data, not an embarrassment to hide. A single controlled bench run is not a deployment claim.

Baselines on the same hardware

Compare against the right prior system on the same platform, tuned as well as your own. An

apples-to-oranges comparison — your system on an optimized MCU against a baseline you ran

untuned, or against numbers copied from a paper that used different silicon — is the most common

reviewer objection. If you must cite cross-hardware numbers, say so and bound the comparison.

Intermittent power and harvesting

For batteryless or energy-harvesting systems, the evaluation must include the **energy source's

behavior**: the harvest trace (indoor light, RF, vibration), the capacitor/energy-buffer sizing,

and behavior across power failures. A harvesting claim without the input-energy conditions is not

reproducible even with the code (see sensys-reproducibility).

Audit checklist

[ ] Energy reported with instrument + sampling rate + integration boundaries.
[ ] Latency as a distribution (median + tail), measured on the target device.
[ ] Accuracy paired with ground-truth provenance and the truth's own error.
[ ] Deployment: node count, environment, duration, uptime/failures stated.
[ ] Baselines run on the same hardware, tuned; cross-hardware numbers flagged.
[ ] Embedded-AI: quantized size + RAM/flash peak on the actual MCU.
[ ] Harvesting: input-energy trace + buffer sizing + power-failure behavior.
[ ] No simulation-only or single-run claim standing in for deployed behavior.

Output format

[Axes]     which of the five measurement axes are covered / missing
[Energy]   method stated? instrument + sampling rate + boundaries — pass/gap
[Truth]    ground-truth provenance and its error — pass/gap
[Deploy]   deployment realism + honest uptime — pass/gap
[Baseline] same-hardware, tuned comparison — pass/gap
[Open]     the single measurement whose absence most weakens the paper

想直接用这个技能?

本站把开放许可(MIT / Apache 等)的技能按仓库打包整理到网盘,点一下转存到你自己的网盘,不用一个个从 GitHub 拉。许可未声明的技能只给原始仓库链接,不打包。