Industrial Internet of Things (IIoT) era: RFID how to become the “nerve endings” of data collection?
In the architecture of the Industrial Internet of Things (IIoT), data is the “blood” that drives intelligent decision-making, and RFID (Radio Frequency Identification) technology, with its ubiquitous sensing, passive and long-lasting, non-contact identification characteristics, is becoming the “nerve endings” of the IIoT data collection layer. “.
First, RFID: IIoT data collection “capillary”.
Industrial Internet of Things (IIoT) relies on the real-time collection of massive heterogeneous data, while traditional sensors have problems such as complex wiring, limited power supply, high cost, etc. RFID fills this gap through the following characteristics:
Passive sensing: Tags do not need batteries, activated by reader RF energy, suitable for high temperature, high humidity, strong interference and other industrial environments;
Wide-area coverage: UHF RFID can read and write up to 30 meters away, supporting batch identification in dynamic scenarios (e.g. forklifts, conveyor belts);
Low-cost deployment: basic tags cost less than $1 per unit and can be rewritten to adapt to flexible production needs.
According to IoT Analytics data, in 2025 the global industrial Internet of Things RFID data collection accounted for 37%, becoming the second largest perception technology after the industrial camera.
Second, RFID in IIoT core application scenarios
- All-chain material tracking
Technology realization: RFID tags are embedded in raw materials, work-in-progress and finished products, and combined with 5G network to realize real-time positioning across workshops and factories;
Case : Haier’s smart factory tracked the whole process of refrigerator production through RFID, shortening the order delivery cycle from 14 days to 5 days and increasing inventory turnover by 40%.
- Equipment health monitoring
Technology realization: Metal-resistant RFID tags integrate temperature and vibration sensors to collect real-time equipment operating parameters;
Case: ThyssenKrupp deployed RFID sensing tags on elevator ropes, combined with AI to predict the risk of breakage, reducing maintenance costs by 28%.
- Man-machine collaboration safety
Technology realization: Wearing RFID tags for operators, triggering safety interlocks when approaching dangerous equipment;
Case: Bosch Suzhou plant reduces human-machine collisions by 90% through RFID+UWB positioning technology.
- Energy dynamic optimization
Technology realization: RFID tags are linked with smart meters to correlate equipment status and energy consumption data;
Case: Schneider Electric optimized the start-stop strategy of injection molding machines through RFID in its French factory, reducing the daily power consumption of a single machine by 22%.
Third, technology integration: RFID and IIoT ecological depth of synergy
- RFID × 5G: low-latency wide-area interconnection
5G URLLC (ultra-reliable low-latency communication) supports millisecond uploading of RFID data to meet the needs of AGV scheduling, emergency shutdown, and other scenarios;
Case: Huawei’s manufacturing park deployed a 5G+RFID network to achieve second-level synchronization of status data for 1,000 devices.
- RFID × Edge Computing: Localized Intelligent Decision Making
Edge nodes clean RFID data, extract features, and upload only key events to the cloud;
Case: Siemens Industry Software uses edge computing to analyze machine tool data collected by RFID, compressing the abnormal response time from 10 seconds to 200 milliseconds.
- RFID × digital twin: building virtual mapping
RFID data drives digital twin models of equipment and production lines in real time, supporting simulation optimization and predictive maintenance;
Case : BMW through RFID data to build the entire vehicle manufacturing digital twin, the new model introduction time reduced by 35%.
Fourth, low-cost universalization of the landing path
Phased deployment strategy
Initial stage: focus on high-value scenarios (such as key equipment monitoring), using “handheld terminals + detachable tags” to reduce risk;
Mid-term: Expand to full production flow, deploy fixed readers and metal-resistant tags;
Long-term: Build RFID data ecosystem with supply chain partners to realize end-to-end visualization.