SoftOne IIoT is an open-source Industrial IoT platform which serves as a control hub for such connected production facilities. Being hardware- and transport-agnostic, SoftOne is easily integrated with a broad variety of Industrial sensors, controllers, machines, and device gateways, enabling many-to-many interoperability between them.
On the application and data processing levels, SoftOne IIOT offers a rich feature set for cloud enablement and generic data treatment, which can be used to rapidly assemble end-to-end IoT applications for industrial systems automation, predictive maintenance, and remote monitoring.
SoftOne Industrial IoT Architecture
- Security – Security is a key concern for an IoT system, especially for Industry 4.0 implementations. Remote network access to factory equipment introduces safety and privacy concerns. Therefore, security features like device authentication, role-based access control, encryption of data and software updates need to be considered.
- Device Management – Remote device management is required for any large-scale implementation. Relying upon manual updates on the factory floor is prone to errors, time consuming and costly. Device management for industrial IoT devices should include initial setup and configuration, health check of device, software update and deactivation.
- Data Aggregation
- Digitalization of analog data from the factory floor and communicating this data to the network using standard protocols like MQTT, OPC UA or HTTP(S). This can be accomplished via PLCs that are network enabled, or by adding IoT gateways that connect the PLCs to the network.
- Formatting the data using open standards, like OPC UA, PPMP, PackML.
- Defining and sharing of semantic information to allow for easier analysis across different systems.
- Filtering data at the edge so only relevant information is sent to the network. This can be accomplished through programmatic PLC or IoT gateways.
- Data storage at the edge and in the cloud.
- Ability to store and forward data as well as compression and schedule transfers to enable OT data will eventually be made available to IT and to the enterprise and data scientists for longer term analysis including training predictive models.
- Event Management and Data Analysis – Industry 4.0 implementations will generate a tremendous amount of data and events. For data management, software will be required to filter this data at the edge, provide real-time analytics at the edge and cloud, provide batch-oriented analytics and data storage. For event management, software will be required for event routing, processing and handling at the edge.
- Digital Twin Management – Creating a digital representation of a physical asset is often referred to as a ‘digital twin’. Creating a digital twin allows for easier integration of data analysis, machine learning and monitoring that can be directly tied to the physical asset. Digital twins also enable simulation of future scenarios that can help with planning and preventive maintenance.
The software required to manage digital twins includes the tools required to create and model a twin, APIs and runtimes to interact with a digital twin, and administration consoles to manage the lifecycle of a digital twin collection.