# Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs
## 3 Mobility Models  
Several research papers have suggested algorithms or models to support mobility in W.S.N.s. According to [22], the capacity of nodes in W.S.N.s to relocate themselves upon deployment is referred to as node mobility. As a result, there are two primary categories of sensor node mobility algorithms or models. The first is the usage of mobile sinks or sinks that can receive data from stationary sensor nodes while moving, whereas the second is based on providing all of the sensor nodes with the capacity to move. Because it is more pertinent to the proposed mobility model, we will focus our review in this paper on studies that supply a single mobile sink that moves to collect data from stationary sensor nodes and sends them to the base station. The study proposed in [23] introduced a data-gathering method based on path planning to reduce distances traveled by the mobile sink and reduce the communication range. An inner center path planning algorithm was used to shorten the distance a mobile sink had to travel. The movement path back propagation problem was also considered by presenting a back-routing algorithm. Therefore, the proposed scheme must enable the mobile sink to make adaptive judgments to move adequately. In addition, the research presented in [24] suggested addressing the relay selection problem to decrease energy use and increase the lifespan of W.S.N.s. As a result, the k-means method was used in the proposed study to partition the network into clusters. A movable sink-based cluster head selection technique was suggested to increase energy usage inside the cluster. The mobile sink node will function as a cluster head close to stationary sensor nodes to gather data and reduce energy consumption for static sensor nodes and the cluster head. The research authors presented in [25] were written by writers who developed a sink mobility model derived from Korhonen's self-organization map (S.O.M.). Their study establishes the mobile sink's movement path using Korhonen S.O.M. The mobile sink thus moves during movement times and stops during pause periods. The mobile sink will remain in its present location during the stop periods for a fixed period, after which it will begin migrating to a new site determined using the Korhonen S.O.M., and so on, until topology changes brought on by energy depletion take place. As a result, a new Korhonen S.O.M. calculation of the mobility path will be performed. To further improve the operation of environmental monitoring and anomaly search in W.S.N.s, a collaborative approach was presented in [26]. The proposed method is composed mostly of two components. Based on a weighted Gaussian coverage strategy, the first focus is collaboratively installing the static sensor nodes. The second section, on the other hand, focuses on path planning for the mobile sink and is based on modifying an active monitoring and anomaly search system that uses a Markov decision process model. The major objective is quickly identifying environmental irregularities so that the mobile mode can respond appropriately based on a cumulative reward function. Another study that divides the network into zones to enhance wireless sensor network performance and extend their lifetime is proposed in [27]. The mobile sink will travel close to the heavily loaded zone. It is important to note that a fuzzy logic system is used to select the strongly loaded zone to address any uncertainties that may arise when making this choice. Similarly, an adaptive mobile routing method concerned with identifying burst traffic is proposed in [28]. Additionally, the algorithm relies on splitting the network's clustersinto two groups, and the proposed network architecture is based on having two mobile sinks. After that, each sensor node will be in charge of one group and communicate with the cluster heads. Additionally, the mobile sink will visit the cluster heads within the group specifically.