## 4 Results
### Simulation Results  
In this section, we illustrate the results from simulations. All results are obtained by averaging over 100 simulation times.  
(1) Value of InformationAs can be seen in Figure 4, the value of information acquired by sink nodes in the scenario of 10 sensor nodes is presented. Our method gains the highest VoI, 14.63% and 51.61% higher than QELAR and DBR, respectively. QELAR comes in the second place while DBR obtains the lowest VoI among the three methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline \(CR\) & 300 m \\ \(pl\) & 1000 bit \\ \(E_{ini}\) & 100 J \\ \(e_{s}\) & \(5\times 10^{-8}\) J/bit \\ \(e_{r}\) & \(10^{-8}\) J/(bit \(\cdot\) m\({}^{2}\)) \\ \(SP\) & 100 m per time slot \\ \(TTL\) & 10 time slots \\ \(\alpha\) & 0.5 \\ \(k\) & 1 \\ \(\gamma\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 4: Value of Information obtained by sink nodes (10 sensor nodes).  
Moreover, as shown in Figure 5, the VoI acquired by our method performs better as the network size increases, which is 43.48% and 83.35% higher than QELAR and DBR, respectively. When forwarding data, QELAR and DBR choose the earliest packet in the cache. Not surprisingly, DBR achieves the lowest VoI because the forwarding decision of DBR depends on the accessibility of neighbors with smaller depths. Specifically, compared with QELAR and our method, sensor nodes have to wait longer for the qualified neighbors, which leads to more decay of the VoI of packets. Our proposed method performs the highest VoI, because our method explicitly takes VoI into account in its reward function (Section 4), which leads to the choice of the packet with largest VoI in the sensor cache.  
(2) _Residual Energy_ The results of residual energy of QELAR, DBR and our method with 10 sensor nodes is indicated in Figure 6. The residual energy of QELAR is the lowest while our method consumes the smallest energy among the three methods. More specifically, our method consumes 31.21% and 37.26% of the energy consumed by QELAR and DBR, respectively.  
As shown in Figure 7, our method still consumes the least energy among the three methods when the network size increases, only 24.79% and 31.43% of the energy consumption of QELAR and DBR, respectively. That is mainly because by choosing packets and relay nodes smartly, our method achieves excellent performance in energy consumption. Our method always selects the latest packets in the  
Figure 5: Value of Information obtained by sink nodes (50 sensor nodes).  
Figure 6: Residual energy of sensor nodes (10 sensor nodes).  
cache while QELAR always selects the earliest packets. Moreover, in our method, earlier packets may have been discarded due to TTL constraint when the latter packets are forwarded, which leads to the avoidance of forwarding too many early packets in the cache, compared with QELAR. Therefore, the energy consumption of our method is much lower than that of QELAR.  
(3) _Packet Delivery Ratio_ The packet delivery ratio (PDR) of QELAR, DBR and our method can be seen in Table 2. DBR achieves higher PDR than other two methods in both scenarios. Because packets are forwarded towards sensor nodes with less depths, the packets are either staying in a sensor node or approaching the water surface, which prevents the packets from being forwarded repeatedly between several sensor nodes and trapped in a certain region. Therefore, DBR decreases the repeating forwarding between sensor nodes and increases the PDR. The PDR to sink nodes of our method in scenarios of 10 and 50 sensor nodes are 66.36% and 71.64%, respectively. Our method achieves a PDR slightly lower than QELAR does, mainly because more packets with earlier generation time in the cache are discarded due to the maximum living duration.