## 6 Numerical Results  
In this section, we compare the efficiency of our transmission slot scheduling method with the traditional TDMA approach. Additionally, the contribution of energy harvesting to the lifetime of a clustered underwater sensor network is also denoted in our Python simulations. We used the piezoelectric energy harvesting model [11] with the underwater communication channel model [34] and round-based data collection in clustered networks is adopted from [42]. We assume data is backlogged, i.e., nodes always have data to transmit to their cluster heads. Data packets are fixed in size, and transmission power is adjusted according to the channel conditions and distance of cluster members to their respective cluster heads. We present a performance comparison of 100 different network topology settings in this section. Simulation parameters are defined in Table 1.  
In TDMA-based medium access methods, the transmission slot of each node in the cluster is organized by the cluster head, and cluster members are notified about their scheduled slots for communication. We investigate the performance of TDMA and the effect of piezoelectric energy harvesting on the clustered underwater sensor network. The TDMA-none curve indicates 100 nodes without energy harvesting equipment and using TDMA slot allocation for in-cluster communication. In TDMA-none, the curve cluster head schedules the transmission slots for cluster members. The TDMA-EH curve presents 100 nodes with piezoelectric harvesting equipment and the same transmission slot scheduling method as TDMA-none. We present the performance of our method in the TDMA-RL curve, the transmission slot scheduling with cooperative learning, and compare its efficiency to the traditional TDMA method. Figure 4 depicts that our proposed method improves energy utilization as more data signals are received by the gateway station. In the TDMA-none curve, nodes perform transmissions in predetermined slots scheduled by cluster heads without energy harvesting equipment. The TDMA-EH curve incorporates energy harvesting. It is observed that nodes collect more energy as in consecutive rounds. Therefore, compared to the TDMA-none curve, the TDMA-EH curve sends 20% more data signals to the gateway. The TDMA-RL curve denotes the performance of our approach. As available energy changes during idle-tx states in frames, each node experiences varying levels of energy within its surrounding environment. As the selection of transmission slots adapts to the harvesting conditions, the potential for collecting more energy during each frame increases. It is observed that 15% more data signals can be received by gateway in the TDMA-RL curve compared to the TDMA-EH curve. However, in the traditional TDMA slot scheduling, nodes are unaware of their ambient conditions, and energy is harvested in pre-established slots during frames; compared to the TDMA-none curve, it is observed that \(\sim\)35% more data packets can be transmitted by exploiting the available energy underwater. Figure 3: Example self scheduling of transmission slots representing high/low harvesting conditions. Figure 5 represents the performance of the previously mentioned methods in terms of throughput. It is observed that at the late stages of simulation in the TDMA-RL method, more data signals are received per unit of time compared to the TDMA-none and TDMA-EH methods due to better utilization of available energy. It is also observed that all curves are identical in the early stages of the simulation. In the forthcoming rounds, more energy