## 2 Related Work  
Many approaches for WSNs have been carried out to achieve low energy usage and increase the network lifespan. Because the BS cannot be close to all SNs, SNs that are away from the BS might consume a significant amount of energy to deliver their collected data. Therefore, moving the BS in the sensor deployment area in such a way that the overall average distance is reduced, maybe a successful strategy for extending the lifetime and enhancing WSNs' effectiveness [20]. This Section describes the recent related work on BS location optimization, the research on BS mobility and the use of relays in communication networks. Several strategies have been put forth to address the BS optimal location, including [PERSON] et al. [21] who propose a method for changing the position of the sink node to correspond to the network architecture when a WSN is expanded. The two components of the suggested location update scheme are separated, locating the appropriate location and creating the path-finding algorithm. The best location of the BS in prolonged circumstances is demonstrated by simulations, as well as how to instruct the BS on how to find the desired location. Numerous outcomes of the simulation show the dependability and effectiveness of the suggested scheme regarding the path-finding algorithm. Conversely, [PERSON] et al. [22] suggested a useful virtual grid-based hierarchical routing method that is appropriate for setback applications and meticulously chooses a mobile sink's path while accounting for the hop numbers and the sensor nodes' gathering ratios. Using this method for multi-hop data communication reduces the total utilization of energy. Simulations based on different parameters are used to assess the proposed protocol's quality and compare it to a current routing protocol. The results demonstrate that it outperforms the current one while preserving program latency constraints. In addition, [PERSON] et al. [23] propose a BS placement algorithm that uses computation geometry to choose the best place for the BS based on the relative relationship between the SNs. A breadth-first search spanning tree is created. Data sent by SNs to the BS travel over the spanning tree path from node to root to the BS. Consequently, each SN uses the least amount of energy overall to relay data to the BS. Furthermore, [PERSON] et al. [24] present a method based on the weights of CHs to find the optimum location for the BS. The CH with the least amount of weight will be chosen as the best BS location. Finding the optimal BS location has resulted in lower energy consumption and significantly longer network duration. Also, it controls the traveling path while accommodating dynamic position recognition, in which the network device can only reside in a given sensor node, referred to as the primary node (PSN). [PERSON] et al. [25] indicated a Genetic Algorithm-based strategy for dynamically changing the locations of BSs over time. The optimal locations for BSs, as well as the optimal number of sink nodes, were calculated using this strategy, which considered the remaining energy on each SN and probable routing topologies. Finally, simulation outcomes indicate that the suggested strategy would extend the duration of WSNs. [PERSON] et al. propose to maximize logistics vehicle monitoring efficiency through the deployment of a WSN that has been upgraded by an improved bat algorithm. To create models for node deployment optimization, it examines the system architecture and operational theory of WSN for logistics monitoring. According to the study, the suggested WSN achieves optimal deployment efficiency and greater coverage ratios, which results in shorter communication times and more effective monitoring. The results show that WSN technology is a viable way to improve supervisory systems for logistics carriages [26]. The study of applying dynamic clustering techniques to the problem of maximizing the lifetime of heterogeneous WSNs is proposed in [27].