IoT, Automation & Robotics

Every device must have an identity from the first moment. Without a verified identity, every device is a potential attacker. Zero-touch provisioning means: the device powers on, registers automatically, receives its certificate and configuration — without any manual technician intervention.

X.509 certificates: Every device has a unique client certificate. Mutual TLS authentication — the backend verifies the device, the device verifies the backend. Certificate rotation without downtime. Real-time revocation of compromised certificates via CRL or OCSP.

Fleet management: Centralised management of thousands of devices. Device twin / device shadow for desired vs. reported state. Grouping by location, type, firmware version. Bulk operations — update configuration for an entire group with a single command. Lifecycle management: provisioning → active → maintenance → decommissioning.

Enrolment flows: Manufacturing provisioning (certificate on the production line), field provisioning (QR code + activation), self-provisioning (device registers itself via enrolment service). We choose based on device type and deployment model.

Azure IoT Hub DPS / AWS IoT Core: Managed provisioning services for cloud-native IoT. Custom provisioning for on-premise or hybrid scenarios. Terraform for infrastructure, Ansible for device configuration.

Telemetry is the lifeblood of an IoT system. Without a reliable data pipeline, sensors are just expensive hardware. We build telemetry chains that deliver data from devices to the backend reliably, quickly and with guaranteed ordering.

MQTT as transport: Lightweight protocol designed for unreliable networks. QoS levels (0 = fire-and-forget, 1 = at least once, 2 = exactly once) based on data criticality. Persistent sessions for devices with intermittent connectivity. MQTT 5.0 with topic aliases and shared subscriptions for more efficient routing.

Kafka for stream processing: MQTT broker delivers data to Apache Kafka. Kafka as a central event store — retention policy as required (hours to years). Stream processing via Kafka Streams or Apache Flink: aggregation, windowing, real-time anomaly detection. Consumer groups for parallel processing.

Latency and throughput: End-to-end latency from sensor to backend under 100ms on LAN, under 500ms over mobile network. Throughput: thousands of messages per second per broker instance. Horizontal scaling — adding brokers without downtime.

Data pipeline: Raw telemetry → validation and enrichment → stream processing → time-series database (InfluxDB, TimescaleDB) + event store (Kafka) + alerting engine. Each step is independently scalable and monitored. Dead letter queue for messages that fail validation.

Compression and batching: Protocol Buffers or MessagePack for efficient serialisation. Batching on the device to reduce network overhead. Adaptive sampling — 1/min in normal operation, 10/s during an anomaly.

Not everything belongs in the cloud. When a sorting line needs a decision in 10ms, you cannot wait for a round-trip to Azure. Edge computing processes data where it is generated — on the device or in the local network. The cloud gets results, not raw data.

Computer vision on the edge: NVIDIA Jetson, Intel NCS, Coral TPU. Models optimised for edge inference — TensorRT, OpenVINO, TensorFlow Lite. Quality inspection on production lines (defect detection), OCR on labels, people counting, safety zone monitoring. Inference under 50ms per frame.

Anomaly detection: Statistical models and ML models running locally. Vibration analysis for predictive maintenance, temperature drift detection, power consumption anomalies. The edge decides “something is wrong” and sends an alert — it does not send gigabytes of raw data to the cloud.

Edge orchestration: K3s (lightweight Kubernetes) or Docker on edge devices. GitOps for deployment — same workflow as for cloud. Centralised management via Azure IoT Edge, AWS Greengrass or a custom orchestrator. Rolling updates, health checks, automatic restart.

Offline resilience: Edge node operates autonomously even during connectivity outage. Local cache, store-and-forward for telemetry, local decision engine. After connectivity is restored, sync with cloud. Critical decisions (stop line, safety alert) never depend on cloud connectivity.

A bad OTA update can brick thousands of devices at once. And unlike a server, you cannot SSH-restart IoT devices in the field. OTA updates for IoT require a paranoid approach to rollout strategy, verification and rollback.

Staged rollout: We never update the entire fleet at once. Canary group (1-5% of devices) → early adopters (10-20%) → general availability. Automatic validation between each phase: crash rate, telemetry health, business KPI. If metrics degrade, the rollout stops.

A/B firmware: Dual-partition schema (A/B). The active partition runs the current firmware; the new one is written to the second. After a successful boot the new partition becomes active. On failure, automatic fallback to the previous version. No bricked devices.

Differential updates: We don’t send the full firmware — only the diff from the current version. For embedded Linux (Yocto, Buildroot) we use Mender or RAUC. Update size reduced by 60-80%, faster deployment, lower data costs over mobile connectivity.

Security: Firmware images are digitally signed. The device verifies the signature before installation. Secure boot chain — from bootloader to application. Anti-rollback protection — an older (potentially vulnerable) version cannot be installed.

Scheduling and constraints: Updates outside peak hours. Respecting battery level (no update below 30%). Connectivity requirements (Wi-Fi preferred over cellular). User confirmation for consumer devices, automatic updates for industrial devices.

Automation is not about robots — it is about orchestration. A robot that sorts parcels is just an actuator. The value lies in the system that knows where each parcel belongs, coordinates dozens of robots simultaneously and responds to exceptions in real time.

Sorting lines: Integration with PLCs (Siemens, Rockwell, Beckhoff) via OPC-UA. Control logic for sorting by destination, priority, dimensions. Real-time throughput monitoring, jam detection, automatic recovery. Tied to WMS — the sorting line knows what it is sorting through integration with the warehouse system.

AMR (Autonomous Mobile Robots): Coordinating a robot fleet in a depot or warehouse. Traffic management — no collisions, optimal routing. Integration with WMS for task assignment (pick, transport, replenishment). Fleet management: charging, maintenance, performance metrics per robot.

IT/OT convergence: The OT world (PLC, SCADA, sensors) speaks different protocols to IT (REST, Kafka, SQL). We build an integration layer that bridges both worlds securely. OPC-UA as the standard, custom adapters for legacy protocols (Modbus, PROFINET). DMZ between IT and OT networks.

Digital twin: A virtual model of the physical process. Simulate changes before deploying them to the real line. What-if analyses — what happens if we increase throughput by 20%? Where is the bottleneck? A training environment for new operators without the risk of damaging hardware.

Reactive maintenance costs 3-10× more than predictive maintenance. An unplanned production line outage costs tens of thousands per hour — lost production, overtime, express parts. Predictive maintenance identifies the problem days or weeks in advance.

Condition monitoring: Continuous data collection from sensors: vibration, temperature, current, pressure, acoustic emission. Baseline for normal operation, alerting when thresholds are exceeded. Trend analysis — slowly increasing vibration = bearing nearing end of life.

ML models for prediction: Supervised models trained on historical data (failure history + sensor data). Remaining Useful Life (RUL) prediction — “the motor will last approximately 14 more days”. Unsupervised anomaly detection for unknown failure modes. Models run on the edge (fast decisions) and in the cloud (complex analyses).

IoT dashboards: Grafana with real-time data from InfluxDB/TimescaleDB. Full fleet overview on a single screen. Drill-down to individual devices. Historical trends, device-to-device comparison, shift-over-shift analysis. Mobile access for the maintenance team in the field.

Maintenance workflow: Alert from monitoring system → automatic work order creation in CMMS → assignment to technician → repair confirmation → closure. Integration with ERP for spare parts ordering. KPIs: MTBF (Mean Time Between Failures), MTTR, Overall Equipment Effectiveness (OEE).

Predictive maintenance ROI: Typically 25-30% reduction in maintenance costs, 70-75% reduction in unplanned outages, 20-25% extension of equipment lifetime. Payback period 6-12 months.