Ada Audits the Level-3 Distillation Bill

Ada re-derives this chapter’s own numbers step by step, at full precision

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Ada ADA · CALCULATION AUDIT

Ada Audits the Level-3 Distillation Bill

The chapter streams a 100-sensor fleet to the cloud at about $721/month, then applies Level-3 edge processing — a 512-point FFT sending only the top 20 frequency bins — to cut the bill to about $28/month, a 96% reduction. That distillation keeps exactly the peak frequencies a bearing fault lives in. This audit works the Level-3 distillation bill line by line.

Companion to the chapter Edge in the IoT Reference Model — every number here comes from that chapter.

— a 100-sensor vibration fleet, raw vs FFT peaks, ~4 minutes

Level-3 distillation is a claim about bytes. Here is the fleet bill in strict decimal units (GB = 109 bytes), so the 96% cut is reproducible line by line.

1. Raw is samples × bytes × sensors × seconds.

Per sensor 10,000 samples/s × 2 B = 20,000 B/s; fleet × 100 = 2,000,000 B/s = 2.0 MB/s.

month = 2,000,000 B/s × 2,592,000 s = 5.184 × 1012 B = 5,184 GB = 5.18 TB

At $0.15/GB that is $777.60/month. (This audit works in strict decimal GB throughout; the chapter uses binary 1024-based KB/MB/GB and its own precise fleet rate of 1,950 KB/s = 1.9 MB/s, landing on $721 — both are internally consistent, they simply define "GB" differently.)

2. The FFT window sets the send rate.

512 samples ÷ 10,000 Hz = 0.0512 s = 51.2 ms, so 10,000 ÷ 512 = 19.53 FFTs/s. Top-20 bins as 40 bytes: 40 × 19.53 = 781.25 B/s per sensor.

3. Scale the edge stream back to a bill.

×100 = 78,125 B/s → month = 202.5 GB → $30.38. Reduction = (5184 − 202.5) ÷ 5184 = 96.1% (a 25.6× cut).

Stage Arithmetic Monthly Cost
Raw fleet 2.0 MB/s × 2,592,000 s 5,184 GB $777.60
FFT edge fleet 78,125 B/s × 2,592,000 s 202.5 GB $30.38
Reduction (5184 − 202.5) ÷ 5184 96.1% 25.6×

What the audit buys you: the spectral summary keeps exactly the peak frequencies a bearing fault lives in while cutting the monthly bill from about $778 to $30 — distillation is worth 96% here precisely because the diagnostic signal is a handful of bins, not the full 10 kHz waveform.

Every number above is taken from the chapter’s own examples and re-derived step by step.