## 2 Materials and Methods
It is very difficult to identify non-trusted nodes in wireless networks dynamically. A reinforcement learning-based scheme combined with Blockchain technology to enhance efficiency and security during routing was proposed in [25]. The routing information is stored in the Blockchain; therefore, this information is immutable. Reinforcement learning is used to select more trusted and efficient links dynamically. A novel load-balanced routing protocol based on machine learning was proposed by the authors of [26]. To make intelligent routing decisions, the adjacency matrix of the network topology is first reduced using Principal Component Analysis, and the status of the network queue is predicted using neural networks for intelligent routing decisions. The load-balancing routing algorithm is implemented considering the Queue Use. The shortest path and link stability should be considered to decrease the end-to-end delay. To predict the behavior pattern of the network nodes, a method based on Q-learning was proposed in [27]. The authors in [28] suggested a genetic algorithm-based routing protocol for efficient routing. The unhealthy nodes were identified, and energy use and computational time were reduced. To improve network lifetime, a clustering-based routing protocol was proposed by the authors in [29]. The entire network is segmented into clusters, and each cluster has a designated cluster head through which all the network nodes transport the data. The authors in [30] presented a Multi-Objective Multi-Hop Routing (MOMHR) protocol to improve network lifetime by ensuring optimal routing. This protocol works in two phases. In the first phase, K-Means clustering is used to cluster the entire network. To identify the best cluster head in each cluster, an artificial bee-colony optimization algorithm is used in the second phase. Then, the multi-objective function is used to find a low-cost route from a node to a base station. This approach has been further enhanced by the authors in [31] by introducing a trust-based routing for secured routing. An optimization algorithm called Chicken-Dragonfly (CHicDra) was proposed to find the optimal cluster head. The finalization of the trusted nodes is undertaken with the inclusion of parameters such as integrity factors, consistency factors, forwarding rate factors, and availability factors. The authors in [32] proposed a Residual Energy-based Cluster-Head selection along with the LEACH algorithm (RCH-LEACH) by taking into account parameters such as threshold energy, residual node energy, and the optimal number of clusters. Relative inter-cluster and intra-cluster costs are presented in [33], and they proposed a multi-hop unequal clustering scheme based on fuzzy logic that reduces the energy requirement of cluster members. A node may decide on its own, based on the probability mechanism. They also introduced a self-adaptive rotation mechanism that reduces re-clustering. Enhanced Hybrid Multipath Routing (EHMR) was proposed by the authors in [34], based on the hierarchical clustering technique. To route a packet, the next hop is identified by minimum hop count and maximum residual energy. Load-balancing and fast failure recovery mechanisms were also proposed. Due to node replication, node attacks are inevitable in WBAN. A new type of walk called Solo Stage Random Walk Memory with a Distributed Network was proposed by [PERSON] et al. [35] to address this issue; it ensures a node's security while maintaining a reasonable memory and communication overhead. Data communication is not secure in both directions, because nodes are vulnerable to various attacks. Even though data communication is guaranteed, higher network densities are said to impair attack detection. Several types of sinkhole nodes have been identified to address this issue by the authors in [36] based on numerous disjoint clusters. Several solutions are provided to secure WBAN from black-hole attacks.