### Industry 4.0 IIoT, Cloud, and Edge Computing for Next-Generation Offshore Wind Farms
The offshore wind farm's data-acquisition system's attributes are variety, which _\"refers to the different types of structured and unstructured data such as text, sensor data, audio, video, and graph from heterogeneous data sources\"_[39]; velocity, which _\"refers to how quickly the data can be analyzed to make decisions or  
Figure 2: The seven-layer Internet of Things World Forum reference model, adapted from [34] and classified into Data-in-Motion (Layer 1 to Layer 3) and Data-at-rest (Layer 5 to Layer 7). to obtain useful analytic information in real-time_\" [40]; veracity, which refers to _\"the reliability and insightfulness of data\"_[41], and volume, which _\"refers to the large amount of data that grows continuously as more data is obtained from the IIoT devices mounted on the offshore wind farm's critical infrastructure\"_[40]. **Data-at-Rest (DAR) Module:** The data-at-rest (DAR) module comprises Layer 5 to Layer 7. Layer 5 (Data Abstraction): This layer aggregates the data and simplifies how the underlying data are represented, abstracting the end users from unnecessary background details of the heterogeneous data using object-oriented programming and other database management tools [40; 41]. Layer 6 (Application): This layer comprises software applications built on the microservices architecture that performs monitoring, visualization, predictive analytics, forecasting, trends, and control logic using application programming interfaces to interact with the data stored in Layer 7 [39]. Layer 7 (Collaboration and Processes): This is the business layer, where the business objectives, key performance indicators (KPIs), and other requirements are defined [34]. Contemporary IIoT systems use the IoT-Cloud model, where IIoT devices collect data from the critical infrastructure under observation and send them through a wide area network (or the Internet) to the cloud instances [42]. Then, using query-based approaches, the data stored in the cloud storage are accessed by the O&M team for visualization, forecasting, pattern recognition, and predictive analytics. Regrettably, this IoT-Cloud model faces several technical, operational, and regulation challenges [43]. The continued proliferation of these IIoT devices creates a huge data influx, testing the resilience of the communication infrastructure by increasing the bandwidth requirements [44]. As such, the IoT-cloud model experiences a significant response time lag between the IIoT devices sending data to the cloud instance, the cloud processing the data, and the response being sent to the wind farm. This response time lag is further aggravated when the data are sent over the Internet, which is a best-effort network [8; 45]. Further, adding more devices increases the task load at the central cloud infrastructure, causing scalability concerns [46]. Additionally, many cloud servers are located in different regions and adhere to diverse data protection and privacy regulations [47]. As such, the model faces data sovereignty and compliance issues. Conversely, the IIoT-Edge model enables processing data near the IIoT devices, thereby reducing latency, conserving bandwidth, bolstering reliability, and significantly strengthening security. [PERSON] et al. [2] conducted an experimental simulation to demonstrate how leveraging edge computing technology in wind farm data-acquisition systems design eases acquisition and makes its management more efficient and accurate. Further, [PERSON] et al. [9] performed structural health monitoring on offshore wind turbine towers by measuring the acceleration response, incorporating an edge-computing framework in its data-acquisition system. [PERSON] et al. [10] designed a multi-sensor edge computing architecture to identify incipient short-circuits in wind turbine electrical generators. The results from these empirical studies quantitatively demonstrate how processing data close to the wind farm improves offshore wind farm operations.