## 3 Related Work  
The rapid developments in Industry 4.0 and the IIoT have exponentially increased the need for developing efficient network solutions in terms of energy consumption and delay. As introduced in Section 1, the authors of [11] stated that power consumption can be improved with minor delays, thus solving both issues by controlling power consumption. In this line, several studies have been conducted in the field toward reaching energy-efficient consumption within the IIoT and Industry 4.0 frameworks. [PERSON] et al. [38] presented a novel energy-harvested and cooperative-enabled efficient routing protocol for an IoT wireless body area network. Multiple parameters were considered in [38] to improve the routing including the number of hops from the sensor to the sink, the node traffic level, the signal-to-noise ratio, and the residual energy. Then, the forward nodes were selected based on the calculation of the path's cost. The results in [38] showed that the proposed parameters' cost-path calculation increases the lifetime of the sensor and decreases the end-to-end delay. In [39; 40], traditionally used routing protocols were modified to reduce their energy consumption. The authors of [39] improved the clustering protocol, which is mainly focused on reducing the end-to-end delay, to save energy and prolong the WNS's lifetime. The proposed approach reduced and balanced the energy consumption of nodes by improving the clustering structure based on a fuzzy c-means algorithm. The results in [39] proved that the modified clustering approach is suitable for long-life systems outperforming the existing clustering-based protocols. In [40], a priority-based and efficient routing protocol was proposed based on the well-known low-power and lossy (RPL) network model. In the proposed approach, each network sensor used timing patterns while sending data to the destination and tackling the traffic along the path. The experimental results of [40] showed that the proposed approach increases the routing's robustness, thus reducing delay and energy consumption. In addition, it outperforms the QoS-based RPL, which is one of the most widely used routing strategies for IoT applications. In recent years, AI methods, especially reinforcement learning (RL) methods, have gained great popularity within the field of WSNs, IIoT and Industry 4.0 [14; 41; 42]. In particular, intelligent routing algorithms have demonstrated that they can enable the change of the routing path according to the crowd or the complexity of data, thus improving the energetic performance of WSNs [43]. In [14], an extensive review of the most relevant AI methods used in the literature to optimize the energy consumption and the network's lifetime in WSNs was conducted. Different routing protocols based on RL, ant colony  
Figure 4: Software-Defined Network (SDN) architecture. optimization, fuzzy logic, genetic algorithms, and neural networks were studied. The results in [14] confirmed that RL, which is flexible, fully distributed, and robust against node failures, is the best-suited technique for WSN applications. In this line, in [42], an intelligent method based on a distributed Q-learning-aided power allocation algorithm for heterogeneous IIoT network layers was proposed to meet the QoS requirements for Industry 4.0. The performance of the proposed approach was evaluated for independent, docitive, and cooperative learning. The simulation results in [42] showed that the proposed approach is effective regarding power consumption while having low computational complexity and fast convergence. [PERSON] et al. [41] introduced a secure and energy-aware heuristic-based routing protocol for WSN. The proposed approach used AI-based heuristic analysis to accomplish reliability and security with the least complexity. The results in [41] revealed that the proposed protocol improves end-to-end delay, power consumption, and network dynamics. Software-defined networks have also been recently used for energy optimization purposes [18; 19].