著者：ジェリー・ファン

Node-RED PLC data collection helps industrial teams turn selected PLC values into usable edge data without rewriting control logic. A practical flow should define 4 items first: PLC source, register map, polling interval, and destination format. Robustel EG5100 edge computing gateway is a practical reference for PLC data collection because it provides 2× software-configurable RS-232/RS-485…

An eSIM/eUICC router lets industrial IoT teams download, activate, switch, or retire cellular operator profiles remotely instead of relying only on physical SIM replacement. Robustel R1520e Global addresses this deployment problem with a hybrid design combining 1× eSIM/eUICC and 1× 2FF physical SIM. With support for up to 8 eSIM profiles, industrial interfaces, RCMS remote…

Node-RED industrial IoT workflows help project teams build selected data flows near equipment instead of sending every raw value directly upstream. The best fit is a 3-step edge workflow: collect field data, transform it locally, and forward structured messages to MQTT, APIs, or cloud systems. Robustel EG5120 is a practical reference for this architecture because…

Edge computing applications in industrial IoT are not all the same. A factory may use an edge gateway to collect PLC data for production visibility. A remote utility site may use edge processing to reduce unnecessary site visits. A BESS project may need selected equipment data from multiple site-side systems. An EV charging location may…

Edge computing vs cloud computing is often framed as a technology comparison. In industrial IoT projects, that framing is usually too broad. Most deployment teams are not choosing between edge and cloud as two competing architectures. They are deciding which task belongs close to the equipment and which task belongs in a cloud platform, remote…

An industrial data collection project can look successful on paper and still fail in practice. The gateway is online. The cloud connection works. Data appears on a dashboard. But the values are hard to interpret, tags are unclear, units are inconsistent, timestamps are unreliable, or no one knows who should maintain the data mapping after…

Choosing an industrial edge gateway is often harder than comparing datasheets. A project team may know that it needs remote monitoring, cellular connectivity, industrial data access, and cloud forwarding. But those requirements are still too broad for a reliable gateway decision. A simple utility cabinet, a factory PLC data collection project, a BESS site, and…

AI inference at the edge is often discussed as if every industrial IoT project should move intelligence as close to the machine as possible. In practice, the decision is more specific. Local AI inference makes sense when field-side data needs to be interpreted near the equipment before selected results are sent upstream. That may involve…

Edge computing vs cloud computing is often presented as a choice between two competing architectures. In industrial IoT, that is usually not the most useful way to look at it. Most industrial projects do not need to decide whether edge computing is “better” than cloud computing. They need to decide which data should be handled…

5G edge computing in industrial IoT is not simply about adding a faster cellular connection to a gateway. A 5G connection can make remote connectivity more flexible, but the real value appears when cellular backhaul is combined with local processing, protocol handling, selected data forwarding, and remote device management. In many industrial projects, raw data…