moai-domain-iot

MQTT-based telemetry requires a robust client with auto-reconnect, message buffering, and edge aggregation logic. This Skill provides production-ready patterns for connecting devices to MQTT brokers, processing sensor data locally at edge gateways, and forwarding filtered results to cloud services, eliminating redundant transmissions.

Device management security involves credential onboarding, secure MQTT authentication, and policy enforcement across sensor networks. This Skill covers enterprise patterns for secure device provisioning, connection authentication, and scalable administration of distributed IoT fleets without manual per-device configuration.

Edge ML inference processes telemetry locally before transmission, reducing latency and bandwidth. This Skill provides containerized deployment patterns with Docker for running inference models on edge gateways, enabling real-time decision-making with only aggregated results sent upstream.

MQTT scales well for sensor networks, and this Skill supports multi-protocol architectures including MQTT, CoAP, and LoRaWAN across device, edge, and cloud layers. You can mix protocol handlers at different network tiers and consolidate telemetry through a unified MQTT backbone.

Message buffering during disconnection prevents data loss. This Skill implements auto-reconnect with local queuing, so edge gateways accumulate telemetry while offline and replay it once the broker connection restores, guaranteeing no sensor readings are discarded.

Docker containerization packages your MQTT client, data aggregation logic, and inference models into portable, reproducible images. This Skill provides deployment patterns for running containerized edge gateways across heterogeneous hardware, simplifying rollout and scaling.