Take fewer readings and keep the same measurement quality. In the strongest sensor model, that means almost 30% more time between charges. Keep the original run time instead, and a smaller battery and cooling system make the modeled device up to 9.22% lighter and 10.37% smaller.
Input energy per modeled job
0.2304 Jenergy saved
23.04% less energy
Before1 J
→
After0.7696 J
Before
After
Saved energy model: readings use 50% of original energy; added work uses 3%.
Average power at one job/second
230.4167 mWpower saved
23.04% less power
Before1 W
→
After0.7696 W
Before
After
Derived from energy per job at a fixed one-job-per-second rate.
Heat per modeled job
0.2304 Jless heat
23.04% less heat
Before1 J
→
After0.7696 J
Before
After
Energy leaving in forms other than heat and stored-energy changes stay the same; the compared 1 J starting energy becomes heat.
Readings in the selected case
25 readingsavoided
52.08% fewer readings
Before48 readings
→
After23 readings
Before
After
Variances 1, 1, 100; old equal-plan variance limit preserved.
Computer-model result
What we compared
Inputs: Readings use half the original energy in this saved case; added FI processing uses 3%. The example starts at 1 J per job and one job per second. The heat calculation keeps energy leaving in forms other than heat and changes in stored energy the same.
Evidence: The 48 → 23 reading result is checked against every allowed allocation. Conventional exact planners tie it. Results cover 12 selected mathematical cases.
CUMULATIVE APPLICATION BUDGET / 17 OCTOBER 2026
3.25–23.04% less modeled sensing energy
Eight selected cases versus equal reading counts; readings use 50% of energy and planning costs 3%. Exact conventional planners match the reading counts. A ninth case saves 1.17%; three equal-noise cases use 3% more.
Sensing-energy model.
Related model results: D120: 33.25% longer completion; 1.4747% more energy at its worst tested condition. No winner saved energy across the required robustness cases.
Related model jobs: D120. Integration into a product remains open.
We’re exploring ways to help heart monitors, pacemakers and wearables use less energy.
SENSOR DESIGN MODEL
Battery life or weight.
What eight selected sensing cases mean for one modeled device.
The starting device: a 10 W sensor with 100 Wh of usable battery energy, 1 kg weight and 1 L volume. The model includes its full energy use at the same reporting rate.
Battery life · keep the same battery
3.36%–29.94%
longer battery life per charge
10 hours → 10.34–12.99 hours
For remote sensors and portable instruments.
Power · same reporting rate
3.25–23.04%
less average input
10 W → 9.68–7.7 W
For a sensor with limited available power.
Heat · same energy outputs apart from heat
0.33–2.3 W
less heat to remove
10 W heat load → 9.68–7.7 W
A heat-flow comparison for sealed instruments.
Weight · smaller battery and cooling
1.30–9.22%
lighter example design
1,000 g → 987–907.83 g
Battery and cooling resize at the original runtime.
Size · smaller battery and cooling
1.46–10.37%
less example device volume
1,000 cm³ → 985.38–896.31 cm³
Fixed packing and clearances in the example layout.
25 readings avoided. Extra planning uses 3% of the original job energy, included in these net savings.
Two choices: keep the 100 Wh battery and run for 10.34–12.99 hours, or use 96.75–76.96 Wh and keep the original 10 hours. The size and weight figures use the second choice, with a smaller battery and cooling assembly.
WHAT WE PAY FOR
3% added processing energy.
Extra planning uses 0.30 W in the 10 W example at the same work rate. In the best case, taking fewer readings saves 2.60 W before planning, leaving 2.30 W saved overall. With these inputs, the saving starts when readings fall by more than 6%.
In three cases with equal noise across channels, the plan skips no readings. Power rises to 10.30 W and the same battery lasts 9.71 hours instead of 10—2.91% less battery life per charge. One other case saves only 1.17% energy. The linked technical reference includes all 12 cases.
WHERE FI FITS
Application fit
This approach fits instruments whose repeated readings have different noise levels and a defined quality target. Fewer unnecessary readings could help the battery last longer or allow a lighter design.
Exact conventional planning methods reach the same reading counts. FI brings the plan, result checks and device-energy calculation together.
Whole-system effect
If sensing uses 20% of the device’s total energy, the 3.25–23.04% sensing saving becomes 0.65–4.61% less whole-device energy, assuming the other 80% stays fixed and no further costs are added. That means 0.65–4.83% longer battery life per charge on the same usable battery.
Inputs and calculations
Saved plan: net energy reduction = 50% × fraction of readings avoided − 3%. The model keeps energy per reading, required answer quality and useful work rate fixed. Readings skipped by the plan are assumed not to be taken. Eight cases span 3.25–23.0416667%, one saves 1.1666667%, and three use 3% more. The calculation covers selected model cases.
Battery life per charge = usable battery Wh ÷ average W. Battery-life increase = saving ÷ (1 − saving). Same usable capacity, conversion efficiency, cutoff and operating profile are assumed. Battery downsizing keeps energy density and packing unchanged.
Size/weight budget: 250 g and 300 cm³ of scalable battery; 150 g and 150 cm³ of scalable cooling; all remaining parts fixed; no added physical parts. At each energy endpoint, mass saved = 400 g × saving and volume saved = 450 cm³ × saving. The cooling calculation assumes the thermal design load falls by that same fraction, with unchanged ambient, surface temperature and heat transfer per area. The modeled mass and volume assume peak loads, structural limits and packing allow those part sizes; fixed parts retain their original size.
The heat comparison uses the same energy leaving the unit in forms other than heat and the same stored-energy change over the same interval. Its starting heat load is 10 W.
These figures show how the saved sensing result changes passive cooling at steady state.
Measure
Before
After
Difference
Design heat load
10 W
7.7 W
23.04% lower
Effective passive-cooling area
500 cm²
384.79 cm²
23.04% smaller
Cooling assembly mass
200 g
165.44 g
17.28% lighter
Cooling assembly volume
200 cm³
165.44 cm³
17.28% smaller
Device mass: cooling resize only
1,000 g
965.44 g
3.46% lighter
Device volume: cooling resize only
1,000 cm³
965.44 cm³
3.46% smaller
Device mass: battery + cooling resize
1,000 g
907.83 g
9.22% lighter
Device volume: battery + cooling resize
1,000 cm³
896.31 cm³
10.37% smaller
Illustrative 1 kg, 1 L design: 200 g and 200 cm³ of passive cooling, with 50 g and 50 cm³ fixed; a 250 g, 300 cm³ scalable, energy-bearing battery portion. The combined case resizes the battery for the original battery life per charge.
Calculation and sources
Convection heat balance: Q = h × A × ΔT. At fixed heat transfer per area and surface temperature, area scales with design heat load. With assumed h = 10 W/m²K and ΔT = 20 K, 10 W needs 500 cm²; 7.6958 W needs 384.79 cm².
The 150 g and 150 cm³ variable cooling portions scale with effective area; the 50 g and 50 cm³ fixed mounting/base portions remain. Thus cooling mass = 50 + 150 × (1 − 553/2400) = 165.4375 g. Cooling-only device saving = 34.5625 g and 34.5625 cm³.
For fixed original battery life per charge and unchanged battery energy density: combined mass saving = (150 + 250) × 553/2400 = 92.1667 g; combined volume saving = (150 + 300) × 553/2400 = 103.6875 cm³. The added-part budget is zero; processing energy is already included in the net reduction.
The example assumes the thermal-design load falls with average energy, stable heat-transfer effectiveness, equivalent materials and packing, unchanged ambient and temperature limits, and the same rate of required work. The modeled resize also assumes compatible peak heat, contact resistance, airflow clearances and structural support.
Radiative area has the same proportional relationship at fixed emissivity, surface/sink temperatures and external heating. Whole-device volume uses the assumed removable component volumes.
Removing the listed 10 g fan adds 3 g of permanent replacement parts. The device becomes 2.8% lighter, with its heatsink retained.
The chosen thermal budget uses a 40°C maximum ambient, an 80°C target and an assumed complete passive path of 4.15 K/W. Cutting the electronics heat envelope from 10 W to 9.5 W changes the calculated passive temperature from 81.5°C to 79.425°C. Passive capacity is 800/83 W (9.6386 W); the exact crossover reduction is 3/83 (about 3.61446%).
The calculation assumes the starting forced-air design delivers the required service. Its passive-path bound includes every hotspot, contact, operating orientation and transient requirement. The 3 g replacement total includes all permanent added parts.
Retained sensing endpoint: 7.6958 W requires 384.79 cm² of effective surface at h = 10 W/m²K and a 20 K surface-to-air rise. That surface path requires Rθ ≤ 2.5988 K/W. The package, contact and spreading paths each consume their share of the hotspot temperature budget.
The worked budget resizes an existing passive assembly from 200 g to 165.44 g.
At the same sensing rate, the model’s 3.25–23.04% energy reduction gives 3.36–29.94% longer battery life per charge on the same usable battery. The alternative resizes the battery and cooling, removing 13.00–92.17 g from the 1 kg example.
What the design aims to improve
More time between charges, or a lighter portable instrument.
THE APPLICATION
Precision sensing & metrology
Sensor manufacturers, test laboratories and instrument developers
Take fewer readings at the required quality, or keep the original readings for a steadier combined answer.
Measurement planning · Linked-error decisions
This model targets repeated sensing with unequal noise across channels and a fixed quality target. After added processing is counted, fewer readings could extend battery life or allow a lighter battery. The worked device example calculates both choices.
REFERENCES & CHECKED RESULTS
Comparison.
Measure
Reference or conventional method
FI result or stated assumption
What it means
Variance, 12 selected cases
Equal reading counts provide each case’s reference
26.2972% lower average variance at the same reading budget
Modeled reduction in variance (the spread of the answer) versus equal reading counts
Minimum readings at old quality limit
The variance (quality) limit from the equal-reading plan
23.7847% fewer readings on average
The alternative to retaining all the original readings
Capable exact planner
Greedy allocation and exhaustive search (every allowed split)
The same reading allocations in every selected case
0% demonstrated advantage over these exact methods
Energy and buyer outcome
The equal-plan quality target and the same sensing rate
3.25–23.04% less energy in eight selected model cases
3.36–29.94% longer battery life per charge on the same battery, under the stated energy-model inputs
Explore the reading-plan examples
READING-PLAN EXAMPLES / ENERGY ESTIMATE
Explore the energy tradeoff.
For this comparison, the starting job is set to 1 J. Doing one job per second gives an average power of 1 W. Choose how uneven the channel noise is, then adjust the energy assumptions.
48 → 23 readings at the original quality limit. Exact conventional planners reach the same reading count.
Before1 J/job
1 W at one job per second
After · energy projection0.7696 J/job
0.7696 W at one job per second
Result23.04%
less energy and average power
0.2304 J/job energy savedHeat changes by the same energy per job when energy leaving in other forms and changes in stored energy stay equal. Battery life per charge with the same usable energy: +29.94%.
Model inputs
Energy reduction = reading-energy share × reading-count reduction − added-energy share. The skipped readings are physically omitted. Each remaining reading uses the same energy, and useful jobs run at the same rate.
Saved sensing-energy ranges, independently checked thermal calculations and recorded software comparisons. The device calculations show energy shares, heat paths and component budgets.
Precision sensing & metrology evidence | FI Systems
