Cold Chain Monitoring IoT: Data That Passes Inspections

Articles on the Internet of Things: connected devices, sensors and platforms that collect and use data from machines, vehicles and infrastructure.
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The Investimenti Sostenibili 4.0 grant, opening on 6 October 2026, funds up to 75% of eligible costs for southern Italian SMEs (35% non-repayable grant and 40% zero-interest loan) for projects that combine Industry 4.0 technologies with at least a 5% annual energy saving. Because 4.0-enabling technologies must be preponderant (over 50% of eligible costs) and the saving must be documented, the IoT layer of sensors, gateways, cloud and analytics is the core of the application.

The SOS Panic Button is Mative's onboard emergency system that lets drivers and operators request immediate assistance with a dedicated button: once activated, it can call predefined emergency contacts, transmit the real-time GPS location, identify the vehicle and notify the operations center. It can interoperate with the infrastructures of the European Emergency Number 112 through "Where ARE U" services, extends to wearable Lone Worker devices, and has been adopted by AMAT Palermo, the public transport operator of Palermo.

On-board load weight measurement tells a fleet manager how much each truck is carrying in real time, without installing load cells in the suspension. Mative's solution fits an angular sensor on the chassis, linked by a rod to the axle: the heavier the load, the more the suspension deflects and the wider the angle. After a calibration at a weighbridge, the cloud converts that angle into weight with an accuracy of 3–5% of the maximum payload.

Mative's depth measurement system for excavators continuously detects the position of the arm's moving elements and, with a proprietary geometric algorithm, computes in real time the excavation depth with centimeter-level precision. The data are sent automatically to the cloud, where site and fleet managers can monitor, archive and analyze them, and the system installs quickly without modifying the machine structure.

RFID monitoring on garbage collection trucks identifies every bin automatically: a passive UHF tag on the container is powered and read by an antenna near the lifting mechanism, and the onboard computer confirms the emptying with the arm sensors. Each collection event is enriched with timestamp and GPS coordinates and sent over 4G/5G to Mative's back-office, where it supports Pay-As-You-Throw billing, route optimization and SLA monitoring.

Advanced telematics for agriculture means reading CAN bus and GPS data from tractors, harvesters and other farm vehicles to know in real time where they are, how they work and how much fuel they use. Mative's Synapsis FMX Smart CAN Control does this with the Teltonika FMB140 device and the Synapsis IoT platform, adding alerts, geofencing and maintenance scheduling based on actual use.

Industrial IoT helps metalworking SMEs, squeezed by rising energy costs, foreign competition and average operating margins below 10%, by giving them real-time visibility on machine status, production times, process efficiency and energy consumption. With the Mative Cloud and Mative Synapsis Industrial Edge platforms they can find bottlenecks, move to predictive maintenance and cut energy waste: SMEs that took this path report up to 30% less machine downtime and 15-20% higher production efficiency.

Industrial IoT (IIoT) connects machines, sensors and management systems in a continuous, real-time data flow that companies use to monitor performance, detect imminent failures and reduce downtime. Mative Cloud is Mative's IIoT platform: independent of hardware, it collects and stores device data in the cloud, runs remote commands and over-the-air firmware updates, creates intelligent alerts and integrates PLC plants through protocols such as MQTT, Modbus, OPC UA and CANopen.

IoT and machine learning help an agricultural company save water, energy and money: soil moisture sensors allow irrigation only when needed, temperature and humidity sensors cut unnecessary heating and ventilation, and ML models support weather forecasting, crop management and production planning. For a medium-sized agricultural company, Mative's forecast analysis after one year points to 30% less water consumption, 25% lower energy costs, 20% more production and 15% savings in operating costs.