Inputs
Example data table
| Scenario | Sensors | Payload (KB) | Bandwidth (Mbps) | Streams | Window (h) |
|---|---|---|---|---|---|
| Factory floor nightly | 120 | 300 | 15 | 3 | 4 |
| Field gateways monthly | 750 | 520 | 25 | 5 | 6 |
| Lab validation batch | 40 | 900 | 5 | 2 | 8 |
Formula used
- Base payload (KB) = Sensors × Payload size.
- Total data (KB) = Base payload × (1 + (Packet% + Encryption%)/100) × (1 + Retry%/100).
- Effective Mbps = min(Bandwidth × Utilization cap, Streams × Per-stream limit).
- Update time (hours) = (Total data in Mbits ÷ Effective Mbps) ÷ 3600.
- Labor cost = (Sensors ÷ Sensors/hour) × Labor rate.
- Downtime cost = (Sensors × Downtime minutes ÷ 60) × Downtime cost/hour.
- Total cost = Labor cost + Equipment cost + Downtime cost.
How to use this calculator
- Enter your fleet size and average payload size.
- Set bandwidth and a utilization cap to preserve headroom.
- Add expected overheads and retransmissions based on history.
- Choose streams and per-stream limit from throttling policies.
- Provide window, technician throughput, and cost assumptions.
- Click Calculate to see time, feasibility, and cost outputs.
- Use CSV/PDF buttons to export the full summary.
Why update latency matters in control loops
Sensor update latency is the elapsed time between a physical change and the moment the new value is available to a controller or dashboard. In closed-loop systems, excessive latency increases phase lag, which can reduce stability margins and force lower controller gains. For example, if your process time constant is 2 seconds, an end-to-end latency of 300 ms already consumes 15% of that window and can amplify overshoot during step changes.
Sampling rate, payload size, and bandwidth limits
The update interval is driven by the sampling rate (Hz) and the amount of data per sample (bytes). The calculator estimates effective throughput by combining payload bytes, protocol overhead, and link capacity. If a sensor sends 120-byte payloads at 10 Hz, the raw payload flow is 1,200 B/s. With 40% overhead and a 9,600 bps link, congestion appears quickly, and the achievable update rate must be reduced or compressed.
Firmware overhead and processing time budgeting
Beyond the link, microcontroller tasks add delay: ADC conversion, filtering, calibration, encryption, and queueing. Processing time per update should be compared against the desired interval. A 25 ms compute path is fine for 1-second updates, but it dominates a 50 ms target. Use the “processing time” input to see whether the device becomes CPU-bound and to decide if you need lighter filters or fixed‑point math.
Reliability targets: retries, uptime, and data loss
Real networks drop packets. Retries improve data integrity but increase mean latency and burst traffic. The calculator applies an expected retry factor based on success probability to approximate the added delay. Pair this with your uptime goal (for example 99.5% monthly) and a loss tolerance (such as 1 missing sample per 10,000) to choose a practical update policy for safety and audit requirements.
Using the calculator to plan maintenance windows
When sensors are battery powered or exposed to harsh environments, updates and firmware refresh cycles must be scheduled. By converting update frequency into daily transmissions and estimated energy cost, you can align maintenance windows with predicted depletion. Keep a buffer for temperature effects and aging, then export results to CSV or PDF for work orders and stakeholder reviews.
FAQs
Q1. What does “end-to-end latency” include?
It includes sensing, conversion, firmware processing, protocol overhead, transmission time, retries, and any gateway or application delays until the value is usable.
Q2. How do I choose a reasonable sampling rate?
Start from the fastest dynamics you must observe. For most control and monitoring, sample at least 5–10× faster than the process bandwidth, then confirm storage and network capacity.
Q3. Why does protocol overhead matter so much?
Headers, acknowledgments, encryption, and framing add bytes and time that don’t carry measurements. At low bit rates, overhead can exceed payload and become the limiting factor.
Q4. When should I enable retries?
Enable retries when missing data impacts safety, compliance, or analytics quality. If latency is critical, limit retries or use forward error correction and design the controller for occasional dropouts.
Q5. Can this calculator help with battery planning?
Yes. Convert the calculated updates per day into radio-on time and estimated energy per transmission. Combine with battery capacity and derating to estimate service intervals.
Q6. What if the calculated update rate is too low?
Reduce payload size, lower sampling rate, batch data, upgrade link bandwidth, or move filtering and compression to the sensor. Also review processing time and queue sizes to prevent backlog.