# IoT OS Platform: Software Infrastructure for Next-Gen Industrial IoT
## 3 Theoretical Framework of the IoT OS Platform
### Core Design of the IoT Operating System Platform
#### 3.2.3 Intelligence  
Currently, industrial intelligence is a hot research topic. However, deploying artificial intelligence algorithms into practical scenarios still faces significant obstacles. Compared to executing algorithms in test environments and obtaining results, packaging algorithms into applications usable in IoT scenarios still requires considerable effort. Especially after a phase of system completion, integrating new algorithms often becomes difficult, making it hard for IIoT systems to keep up with the latest developments in the rapidly evolving field of artificial intelligence. In the previously mentioned five-layer architecture for IIoT, the platform layer actually takes on the responsibility of collecting data downwards and supporting applications upwards. Considering the demands of popular applications such as federated learning [52] for intelligent algorithm scheduling and operational support platforms, based on the platform layer's capabilities, it is possible to solve the data access issues from various sources and allow algorithm output results to directly control devices, thereby converging the problem of IIoT intelligence implementation to how to support algorithm development and operation on the platform layer. We believe that constructing management and support for algorithms in the IoT OS platform should be a key capability of the next generation of IIoT infrastructure. To build support for algorithms on the IoT OS platform, we propose life cycle management for artificial intelligence algorithms applicable to the IoT OS platform, along with a unified algorithm interface standard. By implementing flexible, low-cost algorithm integration, it is possible to improve the current stagnation in intelligent construction of IIoT and apply excellent algorithms from various scenarios to practical applications. In summary, the IoT OS platform adheres to the following principles, making the development, deployment, and use of algorithms in IIoT scenarios more flexible and effective. **Life cycle Management. ** We define the life cycle of an algorithm as the five stages shown in Figure 7: analysis, development, verification, operation, and maintenance. In the analysis stage, by visualizing and initially processing data collected in the IoT system, it is possible to assist in discovering the feasibility and design direction of algorithm construction, clarify the goals and scope of the algorithm, and further define the performance indicators of the algorithm. Since the IoT OS platform has the data collection capability mentioned earlier, it can use the collected data to choose an appropriate algorithm architecture to build a viable artificial intelligence model. Further, in the verification stage, by adjusting parameters and observing trial run results and fixing possible errors and issues, the development effect of the algorithm is confirmed to ensure normal operation under various conditions. In the operation stage, most current IoT platforms usually embed algorithms, only allowing core developers to adjust and modify algorithms. However, we  
Figure 6: Task offloading. believe that the capability to deploy and run algorithms should be opened to users. By building a series of operational and maintenance feature sets, continuous evaluation and adjustment of the algorithm's performance are still possible after deployment. Constructing a complete life cycle process can improve the current lack of flexibility in existing IIoT applications for algorithms and ensure the high quality and reliability of algorithms. **Unified Interface Standard. ** In building the interface standard, we abstracted each algorithm as a stateful executor, inputting data in the required format of the algorithm and outputting usable inference results.