Deploy & Operate
The Deploy & Operate phase is where a completed Robora build goes from “box” into “action.” Here’s what happens step-by-step once a robot or IoT device leaves the assembly line:
Onboarding via the dApp
Device Registration: The owner scans a QR code (or enters a device ID) in the Robora mobile or web dApp. This links the physical unit’s hardware PKI key to their blockchain identity.
Firmware & Agent Assignment: The dApp shows a menu of available AI agents (vision, navigation, inspection, analytics, etc.). The owner selects which agents to deploy, and the smart contract sends encrypted payloads to the device.
Network Handshake & Authentication
Mutual TLS / PKI Verification: The robot initiates a secure handshake with Robora’s edge-and-cloud gateway. Both sides verify their certificates (device vs. platform), preventing any man-in-the-middle or unauthorized access.
Compute Node Discovery: The device queries on-chain registries to find nearby “Edge-Accelerated” Coral TPU nodes or “Cloud-Scale” GPU farms, based on latency, cost, and trust ratings.
Compute Job Submission
Task Packaging: Sensor data (camera feeds, LiDAR scans, telemetry) is packaged into discrete compute jobs. For example, every 500 ms the device may batch up a 3D point-cloud chunk for SLAM mapping.
Smart-Contract Escrow: The owner’s $RBR tokens are staked in escrow against each compute job. Node operators see the incoming job request and its attached payment offer.
Real-Time Inference & Control Loops
Edge Inference: Low-latency tasks (object detection, obstacle avoidance) run on Coral TPUs co-located with the robot. Results (e.g., bounding boxes, safe-path vectors) are streamed back over a lightweight gRPC channel.
Cloud Training / Orchestration: Longer-running jobs (multi-robot coordination, large-model retraining) are dispatched to cloud GPUs. When complete, updated weights or trajectory plans are sent back and automatically integrated into the device’s local firmware.
Monitoring & Feedback
Live Dashboard: In the dApp, the owner monitors CPU/GPU usage, agent performance metrics (latency, accuracy), and token-spend in real time. Alerts trigger if SLAM drift exceeds thresholds or network latency spikes.
Auto-Scaling & Redeployment: If compute demand spikes (e.g., more robots are activated), the system can automatically spin up additional GPU nodes or switch more jobs to edge devices, all governed by on-chain service-level policies.
On-Chain Logging & Settlement
Proof-of-Task Receipts: Each compute segment produces a cryptographic receipt that’s hashed on-chain. This proves the work was completed, unlocking token disbursement to the node operator.
Transparent Billing: The owner sees a breakdown of edge vs. cloud costs, with charges settled automatically from their wallet. Historical logs let them audit every inference or training cycle.
Over-the-Air Updates & Maintenance
Secure OTA Pipeline: New agent versions, firmware patches, or security fixes are published via the same smart-contract–managed pipeline, signed with Robora’s hardware PKI and verified on-device before installation.
Health Checks & Diagnostics: Periodic self-tests (battery status, sensor calibration) run locally, with results anchored on-chain so maintenance teams can pre-emptively address wear-and-tear.
By threading together on-chain identity, token-escrow, decentralized compute discovery, and secure OTA updates, the Deploy & Operate stage ensures each Robora unit stays connected, up-to-date, and optimally performing, without manual infrastructure management on the user’s part.
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