# A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks
## 2 Related Research
The method determines the energy-saving threshold distance for multi-hop communication by analyzing the energy consumption model of WSNs and proposes an optimal relay node selection strategy based on distance and residual energy. However, the existing non-uniform clustering protocols lack the theoretical basis of multi-hop threshold distance, have insufficient load balancing during data transmission, and perform poorly in the face of complex and variable networks. [PERSON] et al. [13] proposed a chaos particle swarm optimization (CPSO)-based cluster routing algorithm for a wireless sensor network (WSN). The method optimizes network performance through clustering, cluster head selection, and energy management. However, the performance of the algorithm is greatly affected by the parameters, and the adaptability and scalability of the algorithm still need to be improved in large-scale and dynamically changing network environments. [PERSON] et al. [14] proposed a multi-intelligent deep reinforcement learning algorithm (EMADRL). By defining the multi-agent reinforcement learning form of MDVRP, a policy network model based on the encoder-decoder structure is designed, and it is trained using the policy gradient algorithm, and local search strategies such as 2-opt search and sampling search are introduced to optimize the solution. However, the EMADRL algorithm has a low accuracy of Q-value estimation at the early stage of training, resulting in large fluctuations in the reward curve. Its convergence time is longer than some of the compared algorithms, and the convergence speed still needs to be improved, even though it can produce high-quality solutions quickly in practical high-computing power platforms. [PERSON] et al. [15] proposed a Grid Based Reliable Routing (GBRR) algorithm for Wireless Sensor Networks (WSN) that combines virtual grid cluster construction and energy efficient next hop selection strategy. The main approach is to improve the reliability and energy efficiency of data transmission through mesh partitioning and intra/inter-cluster communication optimization. [PERSON] et al. [16] proposed a task offloading scheme (TOS-P4) based on the P4 programming language for task allocation and resource management in fog computing networks. This method is able to improve efficiency and reduce the latency of task processing by performing task type identification and dynamic adjustment of load state in the data plane through P4 switches. However, in traditional fog computing network task offloading, it is difficult for tasks to be accurately assigned to the right server based on type, and SDN controllers have limitations in network traffic management and programmability. To sum up, although the existing routing algorithms for QoS and energy saving can achieve the optimization goals set by themselves, they still have a single consideration factor, and it is difficult to take into account multiple optimization goals such as QoS and energy saving at the same time. Moreover, the convergence efficiency of existing routing algorithms and the effectiveness of algorithm models still need to be improved, which makes QoS-oriented energy-saving routing optimization of great research significance. Therefore, based on the efficient routing scheduling mechanism of SDN, this paper further studies how to provide reasonable allocation of network resources through intelligent routing scheduling, realize differentiated services for QoS requirements, and take into account network energy consumption optimization.