## 2 Related Work
The article presents a novel method for accomplishing energy-efficient clustering in the presence of dynamic network characteristics and node behaviors by combining Genetic Algorithm with bacterial conjugation. The results demonstrate the effectiveness of thesuggested algorithm in improving WSN performance, outperforming LEACH-M and EEC-PSO by 12.3% and 10.6%, respectively, in terms of average energy consumption and network lifetime. In the literature, there are many BS mobility techniques proposed to extend the lifespan of WSNs. For example, [PERSON] et al. [28] used a Lagrangian method to tackle the optimal trajectory problem for UAV BSs. Hamilton-Jacobi equations are used to obtain closed-form formulas for the trajectory and speed when the traffic intensity shows just one phase. [PERSON] et al. [29] proposed a hybrid approach to optimize the mobile sink tour route rather than traveling to all possible nodes in the WSN. Through the identification of an effective routing strategy for dynamic mobile BS visitation and gathering of data, the power efficiency of this work is enhanced. [PERSON] et al. [30] additionally suggest a hybrid technique for a novel arrangement of movements in which the mobile BS checks just rendezvous points (RPs) instead of every SN in the WSN. Multi-hopping is used to relay data from all other SNs that are not RPs to the closest RP. Two fundamental problems arise: computing movable sink travel pathways that can reach all RPs within a particular temporal latency and reducing BS energy usage to increase the lifespan of the WSN. A mechanism known as adaptive rendezvous planning, which prioritizes each SN based on the hop distance from the BS path followed and the traffic flow transmitted to the nearest resource provider is proposed to address the first issue. Local routing that is energy-conscious is employed. Finally, a thorough network simulation is used to test the proposed RP model for a mobile sink. The outcomes of the simulation indicate that the suggested RP model enables the BS to gather all the data in a predetermined length of time. [PERSON], [PERSON] et al. suggested using multi-verse optimization to find unknown nodes. The proposed method is compared with firefly algorithms and particle swarm optimization. The results of the experiment show that the recommended method produces better results than other methods [31]. [PERSON] et al. [32] suggest an improved energy-efficient clustering protocol (IEECP) to improve the longevity of the Internet of Things based on WSN. Three elements make up the proposed IEECP. First, the ideal number of clusters is chosen. Static and balanced clusters emerge after that. Finally, optimal locations for the CHs are chosen. As a result, by optimizing the clustering structure, the suggested protocol minimizes and balances SN energy usage. [PERSON] et al. provide a thorough overview of the developed methods to extend the life of mobile WSNs by using the mobility of SNs and/or sink(s) (MWSN). Depending on whether the MWSN has mobile SNs, a single BS, or several BSs, the survey categorizes the algorithms into different groups. To increase the energy efficiency of networks, the mechanism for driving the BSs is also carefully investigated and reported [33]. [PERSON] et al. [34] aims to solve the issue of WSN lifetime. It provided a theoretical framework for examining how BS placement affected the volume of broadcasts and network lifespan. The significance of the BS mobility and BS position effect on network performance are discussed. The effectiveness of the suggested method is supported by numerical findings for the number of transmissions, energy usage, energy usage variation, and network lifespan. [PERSON] et al. [35] estimate the number and optimal location for wireless BSs. A method based on k-means clustering is provided for estimating not only the optimal location but also the optimal number of BSs. The simulation result showed that increasing the number of BSs would improve the communication connection efficiency in industrial environments.