Sensor Inventory & Battery Health Analytics (C2D)
Purpose of the Algorithm
This algorithm provides a foundational inventory and health analysis of CO₂ sensors operating across one or multiple farms. It focuses on sensor availability, data density, and battery behavior — critical aspects for ensuring dataset reliability and operational integrity.
The algorithm is designed for Compute‑to‑Data execution, meaning all raw sensor readings remain fully private and never leave the secure computation environment.
What the Algorithm Does
Sensor inventory and coverage
- Detects the total number of unique sensors
- Counts measurements per sensor
- Identifies active and low‑activity sensors
- Computes the temporal data window for each sensor
Sampling frequency analysis
- Estimates average measurement frequency per sensor
- Detects irregular sampling or missing data periods
- Highlights sensors with abnormal reporting behavior
Battery analytics
- Calculates min / max / average battery levels
- Estimates battery discharge rate (percentage per day)
- Detects fast‑draining sensors
- Identifies battery spikes and anomalies using percentile‑based thresholds (P01 / P99)
Cross‑sensor comparison
- Compares battery behavior between sensors
- Flags sensors whose battery performance significantly deviates from the fleet median
Value in a Compute‑to‑Data Workflow
- Ensures sensor reliability without exposing raw telemetry
- Provides early warning for failing or misconfigured devices
- Improves trust in downstream CO₂ analytics and reporting
Industry Value
This algorithm enables:
- Predictive maintenance
- Data quality certification
- IoT infrastructure benchmarking
- Monetizable “sensor health” analytics for environmental datasets