## 4 MEMAC Algorithm Design
### Design and Implementation of Clustering Protocol
It is observed that the network lifetime of the MEMAC algorithm, irrespective of the proportion of remaining live nodes, exceeds that of the other three protocols. Consequently, it is determined that the MEMAC algorithm clearly outperforms the other three algorithms in prolonging the overall average network lifetime. Figure 4: Simulation rounds corresponding to different node death ratios. Figure 3: Performance comparison of four algorithms in terms of network lifetime. ### Comparison of Average Residual Energy at Node

From Figure 5, it is evident that a slower decline in the average residual energy consumption is exhibited by the MEMAC algorithm proposed in this paper when compared to the other three algorithms. Moreover, the nodes undergo a significantly greater number of rounds when the energy is ultimately depleted, indicating that the longest energy usage time is also shown via this algorithm. This is due to the incorporation of multipath channel information of underwater nodes into the model of inter-node competitive games via the MEMAC algorithm, which enhances the adaptability of the nodes during the clustering process in complex underwater environments. The average energy consumption curve of the CCCS and GTC algorithm has little difference in the decline rate, which is because CHs nodes of CCCS and GTC adopt the practice of centralized control of the data transmission of network nodes in their clusters. And the curve decline rate of these two algorithms is slower than that of the classical LEACH protocol, which is due to the randomness of CHs' selection in the LEACH algorithm. In the initial 150 rounds of simulation, the average energy consumption of the GTC algorithm decreased at a slower rate than that of the CCCS algorithm because the centralized control overhead of CCCS was greater than that of GTC, thus consuming more energy. However, with the increase in the number of simulation rounds, CCCS consumed less energy than GTC. CCCS reduces the communication costs required for centralized control. However, a faster average energy consumption decline rate is shown for the CCCS protocol than the MEMAC algorithm proposed in this paper within the underwater model. ### Comparison of Total Data Received via Central Node

As shown in Figure 6, with the increase in network load, a higher total data reception at the central node is achieved using the MEMAC algorithm compared to the other three algorithms. This is due to the incorporation of the traffic weight of nodes as a reward value into the model for selecting the PCH strategy via the algorithm, which accelerates its convergence to the Nash equilibrium. This enables more data to be received within the same time frame. Figure 5: Comparison of average residual energy in nodes. Additionally, the total remaining energy in the network is taken as a constraint in the CH competition for the algorithm, which factors this into the strategy for CH election. This approach leads to an increase in the overall network lifespan and the time for receiving data packets, thereby further increasing the total amount of data that can be received via the central node. ## 6 Conclusions

In the field of real-time marine environment monitoring, mobile node monitoring based on UWSNs is recognized as an efficient monitoring method today. However, the user nodes in UWSNs, being powered via batteries, lead to significant energy consumption and difficulty in replacement. Hence, the MEMAC algorithm is proposed in this paper. Firstly, a method is designed via the algorithm to place the power distribution system of underwater data warehouses on the floating surface platforms, allowing the power system to provide continuous electricity to underwater central nodes while being easy to maintain. This enables the uninterrupted acquisition of marine information collected via other mobile underwater nodes through clustering and better aggregation of this information.