Technical contribution
Sensor and camera deployment support, environmental data integration, monitoring workflow design, field troubleshooting, and technical documentation.
Case study · IoT + field systems
Smart Hive Monitoring: from sensors to signal.
A low-cost monitoring workflow designed around the realities of moisture, power, connectivity, propolis, and the need to turn readings into decisions.
01 · The problem
Lab-clean data rarely survives the field.
Commercial hive systems can be expensive and rigid. DIY systems can fail under moisture, power loss, signal blockage, and noisy sensor output. The project’s goal was not to collect the most data; it was to produce reliable signals that could support less invasive hive stewardship.
02 · My contribution and ownership
Technical implementation inside an active field context.
At Capitol Bee Care, I supported sensor and camera deployment, data logging, and monitoring-workflow design, working alongside people with beekeeping experience to connect each technical choice to real conditions in the hive.
Domain collaboration
Placement, intervention thresholds, and reliability decisions were informed by beekeeping practice and real deployment constraints.
03 · Architecture
Every component had to earn its place.
Each part below traces back to a specific field requirement — power budget, weather exposure, or data reliability — rather than a spec sheet chosen for its own sake.
Enlarge image ↗
Enlarge image ↗04 · From readings to action
Filter at the edge. Interpret in context.
A raw sensor reading becomes a usable signal only after it passes through four layers of processing, each one removing a different kind of noise.
Level 1
Edge filtering
The ESP32 takes sequential readings and uses the median to reduce transient spikes from brief disturbances.
Level 2
Transmission
The device wakes on an interval, connects, transmits a processed packet, and returns to low-power operation.
Level 3
Trend analysis
Temperature and humidity are evaluated over time so normal daily fluctuation is not confused with an intervention signal.
Decision layer
Health classification
Conditions are translated into stable, stress, or critical states with a corresponding beekeeper action.
Enlarge image ↗05 · Field iteration
Reliability emerged from the system as a whole.
No single fix solved the reliability problem. Placement, power, moisture, and signal integrity each needed their own answer, and the four had to hold up together in the field.
Placement
Sensors were positioned near the brood-box thermal core while avoiding the entrance, airflow blockage, and colony disruption.
Power
Sampling intervals and deep-sleep behavior were used to extend feasible deployment time between maintenance visits.
Moisture
Coating, sealing, and an IP67 enclosure addressed corrosion and water ingress.
Signal
External antenna placement and filtered readings addressed hive-material interference and transient noise.
06 · Reported pilot outcomes
What the pilot report recorded.
The 2025 project report records these pilot results. A public raw dataset and independent validation are not available.
98%reported system uptime across varying weather conditions
80%reported reduction in physical hive inspections
3 weeks → 3 monthsreported battery-life change after sampling and deep-sleep optimization
5+sensor/camera systems supported in my field work
07 · Limits and next work
What needs more field testing.
- The portfolio does not include a public raw pilot dataset or independent validation study.
- Thresholds may need adjustment for colony, season, climate, and sensor placement.
- Connectivity and long-term enclosure performance require broader deployment testing.
- Future work in the report includes solar autonomy, LoRaWAN, acoustic sensing, and a simplified mobile dashboard.
08 · Artifacts