ProTopv1 by Hyper Hyve
ProTopv1 is an amazing way to convert your hive to the science-proven insulated hive top and condensing hive. Plus, it houses all the sensors and their power-management supply, which communicates over a cellular network that covers 99% of the US.
Each sensor, powered by solar, feeds its readings and edge compute into our secure servers that then analyze that data based on our machine learning algorithm that considers your hive's location (GPS sensors) and cross-references millions of data points to give you a picture of what is going on in the hive and what that data means. It's like having a master beekeeper in the hive telling you what to do and when.
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Core Concept-ProTop v1 replaces a standard Langstroth hive lid with an insulated, sensor-equipped top. It converts any conventional hive into a condensing hive while continuously collecting environmental, acoustic, chemical, and motion data. The system runs on cellular connectivity so it works in remote apiaries without Wi-Fi or Bluetooth range limitations. Data is processed with machine-learning models that learn each hive’s normal baseline and surface early warnings for issues such as swarm preparation, queen problems, disease markers, theft, or environmental stress.
Physical Design-
- Insulated sloped-roof form factor that fits standard Langstroth equipment.
- Approximately ~R-20.4, and insulated with 3" of rigid foam insulation that turns the hive into a condensing hive.
- Playing-card-sized plastic mesh vent for the sensors centered on the underside, recessed to reduce propolis buildup.
- Dual solar panels on the roof so the unit can face any direction and keep the battery charged.
- Weatherproof electronics enclosure, NFC logo badge, and durable metal serial tag.
- Fits any Langstroth like hive no larger than 17" Wide X 22" Deep exterior hive body.
- Fits FlowHive, Standard Langstroth, HiveIQ, and similar.
Sensor Suite (Version 1)
| Sensor | Purpose |
|---|---|
| Temp/ Hum/ VOC/ ENV | Temperature, humidity, barometric pressure, and VOC/gas signatures |
| Microphone | Colony acoustic signatures (swarm tones, queen piping, distress, robbing, defensive behavior) |
| LIS3DH accelerometer | Vibration, tipping, theft attempts, bear activity |
| Onboard GPS (cellular module) | Location tracking and geo-fence alerts |
These sensors feed both real-time dashboards and predictive models for:
- Swarm preparation (48–72 hour acoustic + temperature signals)
- Queen loss or queenlessness
- Honey-flow and harvest-readiness indicators
- Winter cluster and starvation risk
- Disease markers (including AFB-related VOC signatures)
- Theft or physical disturbance alerts
- Optimal inspection timing
A user-selectable Condensing Mode toggle adjusts the model baselines so humidity and temperature interpretations remain accurate whether the top is run as a closed condensing system or with traditional upper ventilation.
Connectivity & Power
- Cellular LTE-M / NB-IoT with onboard GPS.
- Solar charging + 18650 battery for continuous outdoor operation.
- Data plan is designed to be a one-time lifetime allowance (e.g., 1NCE-style) so there is no ongoing cellular fee for the user.
- Firmware supports over-the-air updates.
Identity, Activation & Anti-Theft
Every unit ships with two permanent identifiers that are factory-linked:
- Cryptographic NFC tag sealed inside a UV-stable ASA 3D-printed Hyper Hyve logo enclosure. Designed to be readable through gloves and bee suits.
- Human-readable stamped metal serial number in the format HH-XXXXXX on the opposite side of the lid.
Activation is simple:
- Mobile: open the app → “Add Hive” → tap the logo.
- Web: enter the serial number at the dashboard.
Ownership transfer requires an authenticated action by the current owner plus a time-limited token. A stolen flag can lock the device from re-registration. All ownership changes are append-only audit logs. Board serial, SIM ICCID, and NFC UID are bound at manufacturing so simple tag swaps do not work.
User Experience
- Web dashboard and companion mobile app.
- Automatic sensor readings every few minutes.
- Structured digital inspection logging (temperament, brood pattern, queen status, disease signs, stores, notes) that feeds the same data model as the sensors.
- Lock-screen NFC “tap-to-check” flow: walk up, tap through gloves, see current status and answer one or two quick confirmation questions. This both verifies the automated record and creates labeled training data for the models.
- Natural-language style alerts (e.g., “acoustic + temperature patterns consistent with swarm prep — inspect within 24–48 hours”).
Privacy & Security Posture
Because this is a commercial IoT product that will live outdoors and transmit colony and location data:
- Device identity is cryptographically bound at the factory.
- Sensor payloads are sent over cellular with server-side validation.
- The device uses hardware-level firmware encryption and secure boot — your credentials and API keys are protected at the silicon level and can't be extracted even with physical access to the unit.API credentials are stored encrypted on-device; the server validates every request.
- Core ML models, prediction logic, and the growing training dataset remain server-side — the hardware alone has limited value without the cloud service.
- Ownership and transfer events are fully audited; a stolen flag prevents unauthorized re-registration.
- Subscription and account management occur on the web, not through in-app purchases.
Business Model Context
Hardware is sold outright with 6 months of cloud service and advanced analytics are offered via a simple web-based monthly subscription (target $10/device) after. The same subscription covers future modular add-ons (weight, CO₂, etc.).
What Makes ProTop v1 Distinct
It is currently the only solution in its price band that combines cellular connectivity, multi-gas VOC sensing, high-quality acoustic monitoring suitable for bee-noise analysis, accelerometer event detection, GPS geo-fencing, and insulated condensing-hive functionality in a single drop-in top. The system is built so that every hive improves the fleet-wide models while still learning its own normal baseline, reducing false alerts over time.
