## 2 Materials and Methods  
In [6], the selection of the cluster head is based on traffic priority data and energy levels, followed by an optimal route for data transmission. The authors in Ref. [7] proposed a novel algorithm called Power-Efficient Cluster-based Routing (PECR), which uses K-Means clustering, optimal route choice, communication based on energy use, cluster head and primary cluster head alternation for energy efficiency and increased network lifespan. To evaluate the performance of EEDLABA, an energy-consumption model and path loss are proposed, with nine sensor nodes deployed on a human body [8]. There are many research studies on routing protocols for WSN with energy efficiency, security, and cluster-based routing [9; 10; 11; 12; 13; 14]. In this direction, the authors in Ref. [15] proposed a Feedback Routing to Multiple Sinks (FROMS), with a multicast routing protocol based on reinforcement learning and modified transmission back-offs and acknowledgments. With the proposal, the authors have shown that machine-learning algorithms can be successfully applied to devices with limited constraints. [PERSON] et al. [16], overcame the problems of propagation delay and power consumption using a machine-learning approach for routing wireless sensor nodes underwater. This method proposed a balanced routing protocol called the Q-learning-based Energy-Efficient and Lifetime-Aware Routing Protocol (QELAR). It distributes routing across all sensor nodes. Hierarchical routing protocols were used to exploit the energy of the sensor nodes. Based on the above concept, an energy-efficient shortest-path Q-Routing algorithm was proposed in [17]. This algorithm is based on reinforcement learning, which extends the network lifetime. Dynamic topology, one of the characteristics of WSN networks, has complicated the routing mechanism. Another challenge is to minimize resource consumption during routing. A Support Vector Machine-based clustering method was proposed in [18]. To reduce energy consumption, the proposed method allocates the nodes to the closest cluster head. A Naive Bayesian-based classification method was suggested in [19] to predict traffic load and energy in the selected path. Estimating the link cost of nodes is one of the key tasks in routing. To calculate the link cost, the authors in [20] proposed an approach based on machine learning. They evaluated the performance of the algorithms using various machine-learning techniques. To enhance network lifetime, a Hybrid Clustering Routing Protocol-Hole Detection (HCRP-HD) was proposed by [PERSON] et al. [21] by detecting holes and edge nodes. The sink node is responsible for hole and edge detection, reducingthe energy consumption of the sensor node. The network is transformed into several rings to reduce energy consumption due to direct transmission. In [22], the author proposed a machine-learning-based User-Specific Optimal Capacity Shortest-Path (US-OCSP) routing to find the shortest path. Here, they have considered the available capacity in the nodes and distance to determine the optimal path between the source and destination. The congested nodes are avoided using the Q-learning algorithm to improve the throughput and bitrate of the network. Most of the routing algorithms require high bandwidth to manage the routing table entries or avoid suffering from high delay. To overcome this, the route discovery is done on demand, and the update to the route is done proactively. A novel Q-learning-based algorithm was proposed by the authors in [23] by incorporating QoS in the existing reinforcement-learning-based algorithms. The packets in different traffic classes will be routed via different routes; therefore, this method provides high priority for important packets. An Energy-Centric Route-Planning (ECRP) method was introduced in [24] to balance the sensor node lifetime. It also ensures security along with the routing. To maintain a balanced communication channel, the energy requirements of individual and cooperative nodes were taken into consideration.