Duty-cycle energy and latency budget

Build an energy-latency budget from components and explain when duty cycling helps or hurts.

Build an energy-latency budget from components and explain when duty cycling helps or hurts., your practice guide

Build an energy-latency budget from components and explain when duty cycling helps or hurts.
Predict the reading, then compare it with the measurement.

Python 3 in your browser (JupyterLite)

Python · no install

Build an energy-latency budget from components and explain when duty cycling helps or hurts.

Tier 2 · Web · paste-in setup · No account

Version tested: Python 3.12.7 / Pyodide 0.27.6 in JupyterLite 0.6.4; Chromium real browser capture, 2026-10-09. Date: 2026-10-09.

Open the notebook in your browser and run each Python cell; no install or account is needed.

Three ways to run: use JupyterLite here with no install; run main.py locally from the downloadable lab folder; or open the same notebook in Google Colab.

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Steps

Screens captured against Python 3 in your browser (JupyterLite) Python 3.12.7 / Pyodide 0.27.6 in JupyterLite 0.6.4; Chromium real browser capture, 2026-10-09 on 2026-10-09; the tool may have moved on — the text steps are the contract.

  1. Step 1

    Do
    Run the Step 1 notebook cell to inspect the labelled synthetic current and timing assumptions.
    You will see
    SYNTHETIC engineering assumptions; seed=2626; no random draws; supply=3.0 V; active=8.00 mA for 100 ms; radio=35.00 mA for 80 ms per attempt; sleep=0.040 mA; retry adds 80 ms radio-on; latency limit=300 ms; daily energy limit=30 J
    Why it matters
    Every estimate must reveal its units and assumed inputs.

  2. Step 2

    Do
    Run the Step 2 notebook cell to inspect the active, radio, and sleep terms for one cycle.
    You will see
    One 60 s reporting cycle; no retries; energy proxy mA*s = current_mA * duration_s; daily energy=25.889 J at 3.0 V; active + radio latency=180 ms
    Why it matters
    Separate terms show which state consumes the charge.

  3. Step 3

    Do
    Run the Step 3 notebook cell to compare interval length with average current and daily energy.
    You will see
    Increase reporting frequency from 60 s to 10 s; interval_s avg_mA daily_J; 10 0.3993 103.493; Shorter intervals repeat the active and radio cost more often.
    Why it matters
    More frequent reporting repeats wake and radio costs.

  4. Step 4

    Do
    Run the Step 4 notebook cell to add radio retries and inspect energy and latency.
    You will see
    Radio retries at 60 s interval; retries radio_mA*s latency_ms daily_J; 3 11.200 420 62.135; More retries add radio energy and consume latency headroom.
    Why it matters
    Retries can cross both energy and response limits.

  5. Step 5

    Do
    Run the Step 5 notebook cell to identify rows meeting both assumed limits.
    You will see
    Sweep under 30 J/day and 300 ms response limits; interval_s retry daily_J latency_ms feasible; 300 2 18.305 340 NO; YES requires both assumed limits to hold.
    Why it matters
    Feasibility requires both constraints to hold together.

  6. Step 6

    Do
    Run the Step 6 notebook cell and read the conclusion and excluded costs.
    You will see
    RESULT CARD: synthetic budget assumptions; seed=2626; 60 s, no retry: 25.889 J/day; 180 ms; The calculation excludes battery derating, wake-up, and network queueing.; It does not establish measured battery life or deployed latency.
    Why it matters
    An arithmetic budget cannot substitute for field measurements.

Chapter checks

These questions refer to the chapter’s examples. Use the return links to review their answers.

  1. Why does this chapter say fog computing is not automatically faster, cheaper, or lower power?

    Return to the chapter’s knowledge check

  2. A fog node compresses routine data before upload. What measurement decides whether this helps overall?

    Return to the chapter’s knowledge check

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