## 5 Building Resilience in Software-Defined IIoT-Edge Networks
### Autonomous Networks: Self-X Network Management  
Given the growing complexity of offshore wind farm networks, strict security and performance demands, and the difficult accessibility of the offshore environment, it is essential for network engineers to adopt automation and intelligence for network and service management. This shift from manual, reactive resilience measures to proactive, automated strategies will significantly improve operational resilience, performance, and scalability. According to the ETSI ZSM standard group, _\"An autonomous network (AN) is a network that self-operates according to the business goals with no human intervention beyond the initial supply of input (e.g., intent, goals, policies, certain configuration data) by the human operator\"_[155; 156]. The ultimate goal in network automation is to develop networks that can operate independently, guided by high-level policies set by the provider or user. These autonomous networks can configure, monitor, heal, and optimize themselves without human input [157; 158; 159]. Achieving this necessitates the creation of a new architectural framework that supports closed-loop control and is tailored for applying data-driven AI algorithms [160]. These autonomous networks define self-X network and service management properties such as self-configuration, \"this property relates to the autonomic capability of the AN to configure and govern any parameters or settings which relate to functions, services, or assets that make up or relate to that autonomous network\"; self-healing, and self-repair, _AN's ability to fix anomalies after detecting them and to get back to normal fully operational mode through a process of automated steps\"_; and self-awareness, _\"cognitive-like awareness regarding several dimensions such as context information, time, SLAs, KPIs, environments\"_[161; 23; 162]. Figure 10 illustrates an autonomous software-defined IIoT-Edge network for an offshore wind farm application. In the design and implementation of the network, high-level policies such as specific performance metrics, security policies, and traffic engineering rules are defined as business goals [157; 163]. These business goals, formulated by the O&M team, are translated into intents that guide the autonomous operation of the network using an abstraction module [158]. The network implements a monitoring feature at the application plane to collect data on network performance traffic flows, device status, and link status. The network uses a custom-based machine learning (ML) model to recommend configuration changes or take corrective actions in the _\"observe-orient-decide-act\"_ cycle (see the application plane in Figure 10) [164; 165; 166; 167]. In this _\"observe-orient-decide-act\"_ cycle, the network-monitoring module reads the network state and stores it in a knowledge base (such as Redis or Hadoop). Further, it forwards the network state to the predictive analytics module for visualization and forecasting purposes. The recommender module accesses the network state stored in the knowledge base, which formulates traffic engineering functions to manage resources on the network, guaranteeing network performance and availability. To make the network fully autonomous, the AI/ML decision-maker module uses model-based and model-free reinforcement learning approaches to assist the recommender module in optimizing traffic routing and anomaly detection to respond to security threats or failures. Further, the AI/ML decision-maker module updates the knowledge base with data for future use [168; 169]. These modules provide feedback to the network administrators on the impact of the high-level policies on the network, enabling them to decide how to effect the changes. Several studies have explored reinforcement learning approaches to realize autonomous software-defined networks across different use cases. These RL strategies deploy single or multi-agents at the application plane to interact directly with the system, learn, and create optimal policies to meet the stipulated intents.