Industrial IoT Systems: Machine Monitoring and Smart Control
We design industrial IoT for production, warehouse, and field: machine monitoring, telemetry, and control on a Raspberry Pi edge, connected to ERP/WMS.
Free architecture consultation — no commitment, we reply within 24 h.
Control layer
Raspberry Pi CM4 · edge
Device signal
Line 03 needs intervention
Operational problem
Device data exists, but the process can still stay blind
Many companies already have sensors, controllers, and production systems generating data. The biggest loss appears when a signal does not immediately become a business response.
Before
Delayed response and manual exception handling
- Operators learn about failures too late or through a phone call.
- Data from PLCs, RFID, or sensors does not reach ERP, WMS, or dashboards.
- Connectivity loss stops reporting and makes event analysis harder.
- Alerts are scattered, while escalation depends on people remembering the process.
With Foxnet
A device signal triggers the right action
- Telemetry and control run on a Raspberry Pi edge close to the process, also with limited connectivity.
- The edge node filters noise, detects thresholds, and sends only valuable events.
- The system updates WMS/ERP statuses, creates tickets, and sends alerts.
- Event history becomes a data source for analytics, prediction, and AI.
Foxnet IoT architecture
From device signal to a business system decision
We connect the physical layer with backend, integrations, and operational interfaces, with an industrial Raspberry Pi edge at the heart of the architecture. IoT does not stop at a dashboard; it controls and updates the process.
- 01
Devices and sensors
PLCs, RFID, sensors, meters, gateways, and controllers collect signals from the floor, warehouse, or field.
PLC / RFID / RS485 / CAN
- 02
Industrial edge (Raspberry Pi)
An industrial-grade Raspberry Pi / Compute Module gateway filters noise, runs local control rules, and buffers events when the network is weak.
Raspberry Pi / CM4 / RS485 / DIN-rail
- 03
Backend and queues
Events flow into a resilient backend, queues, and APIs that organize the data stream.
.NET / RabbitMQ / REST
- 04
Business operations
Dashboards, alerts, ERP, WMS, and workflows receive current process status without manual copying.
ERP / WMS / dashboards
- 05
Analytics and AI
Events create structured data for predictions, anomaly detection, and decision-support models.
AI / anomalies / prediction
The key transition is from raw data to action: an alert, status update, service ticket, or automatic decision.
Implementation scenarios
IoT designed around the process, not a hardware catalog
We choose hardware, protocols, and integrations around the operational goal. Every scenario is described through signal source, detection, response, and business impact.
Warehouse and RFID
-
Signal source
RFID gates, readers, scales, and scanners at inbound points or transfer zones.
-
System detects
Goods movement, mismatch, delay, wrong location, or an incomplete batch.
-
Response
The Raspberry Pi edge updates WMS/ERP, sets pallet status, alerts the coordinator, and records an event trace.
Business impact
Fewer manual corrections, faster exception detection, and better visibility of goods flow.
Production line monitoring and control
-
Signal source
PLCs, temperature sensors, cycle counters, machine states, and quality signals.
-
System detects
Parameter drift, downtime risk, threshold crossing, or performance drop.
-
Response
The Raspberry Pi edge raises an alert, escalates to maintenance, logs a history entry, and updates the production dashboard.
Business impact
Faster downtime response and fewer decisions made only after the fact.
Field monitoring
-
Signal source
Distributed devices, meters, environmental sensors, GPS, and Raspberry Pi gateways with a local buffer.
-
System detects
State change, lack of activity, deviation from norm, or connectivity loss.
-
Response
Local logging, delayed synchronization, alert, and event replay when the network returns.
Business impact
The process remains under control even when devices operate outside stable infrastructure.
Alerts and automatic actions
-
Signal source
Events from sensors and the Raspberry Pi edge, from backend, dashboards, and business systems.
-
System detects
Threshold crossing, event sequence, anomaly, or missing expected activity.
-
Response
SMS, email, webhook, service task, status update, and escalation path.
Business impact
Less waiting for an operator and a shorter path from signal to action.
Edge continuity
The system still behaves sensibly when connectivity is limited
We do not design IoT as if the network were always perfect. The Raspberry Pi edge layer keeps local logic running, stores events, and synchronizes history once connectivity returns.
-
01 Local decision
The Raspberry Pi gateway recognizes an event and executes a critical response without waiting for the central system.
-
02 Safe buffer
Events, measurements, and statuses are stored locally with order and timestamps.
-
03 Replay after sync
When the network returns, the backend receives full history and can reconstruct process state.
- 0 lost events
- during short connectivity gaps
- local first
- for critical control rules
Offline buffer
Limited connectivity
Sensor
Edge
Queue
Backend
ERP/WMS
Event queue
42 entries
Replay to backend
100% process history
Operations integration
Device data reaches the place where decisions are made
IoT creates the most value when it becomes part of daily operations: planning, service, logistics, reporting, and process automation.
The Raspberry Pi edge normalizes device data, and we design integrations through APIs, webhooks, queues, and custom adapters so the physical process works inside the same data model as the business.
Raspberry Pi
edge
-
ERP
Updates for statuses, usage, downtime, batches, and cost signals in one financial and operational system.
-
WMS
Goods movement, locations, exceptions, and warehouse operation confirmations without manual copying.
-
Service desk
Automatic tickets with device context, measurement history, and response priority.
-
AI and BI
Structured data streams for anomaly detection, prediction, and executive dashboards.
Delivery model
From a small pilot to a production control system
We help you start with one process, validate value, and then scale the architecture across more operational areas.
- 01
Process discovery
We map devices, signals, exceptions, current systems, and decisions that are still handled manually.
- 02
PoC in one area
We build a fast prototype with a real device, a Raspberry Pi edge node, and the first system response.
- 03
Rollout and integrations
We add devices, stabilize the backend, and connect ERP, WMS, alerts, and dashboards.
- 04
Monitoring and growth
We maintain observability, event history, and a foundation for analytics, automation, and AI.
FAQ
FAQ: IoT, edge computing, and smart control
Short answers to the questions that usually appear before the first business IoT pilot.
Can IoT be implemented without replacing existing systems?
Yes. We usually add an integration layer that connects devices, PLCs, sensors, or gateways with the current ERP, WMS, CRM, or production system. This lets an IoT project start with one process instead of replacing the whole environment.
What happens when a device loses connection?
The edge layer can keep running local rules, store events, and synchronize data when the network returns. This limits the risk of losing process history and keeps operational response available during unstable connectivity.
How do you connect IoT with ERP or WMS?
We design API adapters, webhooks, event queues, or dedicated integrations. Device data is normalized, enriched with business context, and only then used to update statuses, documents, tasks, or dashboards.
Can IoT become a data source for AI?
Yes, but the data must first be reliable, structured, and described with process context. We build pipelines that turn raw telemetry into data for anomaly detection, failure prediction, event classification, and reporting.
How do we start with a small pilot?
Choose one process where delayed response or manual checks create a visible cost. In the pilot, we connect selected devices, an edge node, one data flow, and a measurable system response.
Do you build industrial edge on Raspberry Pi and Compute Module?
Yes. We routinely build the edge layer on industrial-grade Raspberry Pi and Compute Module: DIN-rail mounting, 24 V power, isolated I/O, and RS485, CAN, and Modbus modules. It is not a prototype board but a control node hardened for the production floor and field operation.
Which device protocols and interfaces do you support?
We most often work with MQTT, Modbus (RTU and TCP), RS485, CAN, and OPC UA, plus 4–20 mA and digital inputs from PLCs, sensors, and meters. We match the protocol and modules to the specific device and operational goal.
IoT workshop
Show us the process that is still too slow, manual, or invisible
During a short consultation we will identify which device signals are worth collecting, where edge logic should run on Raspberry Pi, and how to connect IoT with your company systems.
The consultation is free and does not commit you to anything further.