## 2 Related Works
In K-LEACH, the K-means method was used for uniform clustering during network initialization, and cluster-heads were selected based on the maximum residual energy of nodes and the nearest distance between the nodes and the cluster centers. As a result, the nodes with more uniform membership and larger residual energy were more likely to be selected as cluster-heads. Generally, the K-means clustering algorithm uses the distance formula to calculate whether two nodes belong to the same cluster. The nodes within the threshold of distances join in the same cluster, and the nodes out of this scope are divided into other clusters. Therefore, this method makes the distance between the cluster-head and the member nodes of the cluster in a reasonable range. In [24], a Q-learning routing algorithm based on the K-means algorithm was proposed for optimal clustering and energy load balancing in wireless sensor networks. Firstly, the Q-learning algorithm is used for training to obtain the optimal initial parameters of the K-means algorithm, which enables the K-means algorithm to obtain better clustering results. Secondly, it also considers the area of clusters and the number of sensors in the clusters, where nodes with large residual energy and a small distance to the receiving node are selected as cluster-heads. This effectively extends the network lifetime. However, it is not ready-to-use and cannot be deployed in various scenarios because the Q-learning algorithm needs be trained in advance. In [25], an energy aware distance-based cluster-head selection and routing (EADCR) protocol was proposed, which used the fuzzy C-mean (FCM) algorithm, residual energy of the nodes, their relative Euclidean distances from the base station (BS), and cluster centroid to extend the network lifetime. It used the shortest path algorithm among nodes to reduce the energy consumption of cluster-heads via multi-hop routing. In [26], a new adaptive and hybrid clustering scheme was proposed that used the Euclidean distance, the fuzzy C-mean (FCM) algorithm, the position of the BS, and the residual energy of the nodes to cluster the nodes, and therefore to minimize the energy consumption of nodes. Clustering was only performed in a few rounds, which significantly reduced the energy consumption. The scheme used an energy-efficient fitness function to select cluster-heads in the network. This effectively extended the network lifetime. The above research were mainly focused on routing algorithms on static base stations (sinks), which have unlimited energy and powerful computing power. It aimed to minimize energy loss and extend the network lifetime where the sensor's energy cannot be replenished. On the other hand, some researchers work on mobile sinks, where the static base station is replaced by an unmanned aerial vehicle (i.e., drones) [27; 28], and tackle the Traveling Salesman Problem (TSP) between the sink and cluster-heads. In [27; 28], the authors proposed two sustainable and quality-aware data collection algorithms (QEADCS, GAMEDFS) in the UAV-assisted manner to reduce energy consumption in robotic wireless sensor networks. They consider the problem under the constraint on the limited-capacity battery of UAV, so the UAV would not access all CH robots. In each cluster, a cluster-head (CH) robot assigns a collaborative task to cluster member (CM) robots and collects data from the CMs, while an unmanned aerial vehicle (UAV) collects data from the CHs robot by accessing a subset of them. The total cost in routing is minimized by optimizing the UAV path planning to minimize its data quality and energy consumption. It used the shortest path TSP algorithm among UAV and CH nodes to reduce the energy consumption of cluster-heads via multi-hop routing.