or suboptimal performance [2; 3]. These data-acquisition systems help minimize prolonged wind farm downtime and maintain peak efficiency in wind farms [4]. Embracing the digital era, the energy sector is undergoing a significant transformation that will see next-generation offshore wind farms leverage the power of technologies from the fourth industrial revolution (Industry 4.0) such as Industrial Internet of Things (IIoT), edge computing, and virtualization to improve their data-acquisition systems. IIoT devices installed on wind turbines and offshore digital substations monitor critical infrastructure in real time. These sensors record wind speed, turbine rotation, temperature, vibration, and electrical output. The data are then transmitted to control and protection systems, enabling automatic adjustments such as altering turbine blade pitch for optimal wind capture or shutting down turbines during severe weather conditions such as storms [5; 6]. These large-scale, complex offshore wind farms generate big data from IIoT devices, often managed by cloud services in an IoT/Cloud-based model, as described in [7]. The traditional IoT/Cloud model encounters challenges such as high latency, substantial data transfer, storage subscription expenses, intricate scaling processes, bandwidth limitations, and restricted connectivity [8]. To address this, edge computing is integrated into the design of offshore wind farm data-acquisition systems, bringing storage and computing resources nearer to the assets to improve efficiency [9; 10]. Furthermore, software-based protection and control systems are set to replace conventional hardware to centralize management and enable remote monitoring, streamlining fault response, and maintaining wind farm stability [11]. These virtual solutions allow for swift adaptations, upgrades, and new features with minimal physical intervention, cutting down on wind farm downtime and costs [12]. A high-performance, secure, reliable, and scalable communication network is needed to facilitate critical, latency-sensitive data transfer between communicating nodes in the data-acquisition system [13; 14]. This communication network is implemented between the wind turbines and an offshore platform (which houses the digital substation). High performance ensures swift data transfer with minimal latency, crucial for real-time wind farm monitoring [15]. Security is upheld through robust encryption and vigilant network monitoring, protecting against unauthorized access and cyber threats. Reliability is achieved with failover systems and durable infrastructure, enabling consistent operation under adverse conditions [16]. Scalability allows for future expansion, accommodating more turbines or increased data loads without compromising network integrity. Together, these attributes ensure that the network can effectively support critical operations of the offshore wind farm. Over the years, communication networks have significantly improved, adopting new network and service management strategies. Figure 1 shows the evolution of network automation for network and service management from 1960, the launch year of the first network, to 2050. Conventional communication networks have a distributed architecture where each device is managed separately in a concept known as \"_siloed networking_\". However, configuring, managing, and troubleshooting have become increasingly challenging as communication networks expand. To address these challenges, researchers continue to explore the concept of _software-defined networking_, where the control plane of the network is decoupled from the data plane [17; 18]. This transforms conventional communication networks into programmable, software-defined, and centrally managed architectures, improving network management and agility, streamlining the deployment of new services, and reducing reliance on vendors [19; 20].