# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 6 Performance Comparison and Analysis
### Simulation Results in the Dolphin Pods Routing Scenario
Therefore, the dolphin pods scheme selects the path where the forwarder of the source node has a higher value of weighting sum of depth difference of the hops (H) [3], average number of forwarding nodes, very small number of suppressed nodes and is close to the sink, which means that the fitness function (HH) for that path is greater, which reduces the collision probability at the receiver. As a result, the PDR value is increased. Next, we investigate the energy tax comparison of a proposed dolphin pod with WDFAD-DBR protocol. When compared to idle listening, packet reception, sensing and processing of operations, in underwater acoustic networks, transmission of a packets is the most energy consuming operation. Transmission of data packets accounts for most of the energy consumed due to their large size when compared to control packets. The above argument is already validated through experimental measurement by the authors in [39]. Energy cost for transmitting a single data bit is roughly equal to the energy consumption for processing thousands of operations [40]; however, complexity of the algorithm may increase energy cost. The algorithm we proposed considers all of the above-mentioned energy usage parameters. However, only considering the receiving and transmitting energies will also not affect the general trend. DBR and WDFAD-DBR do not exploit certain energy efficient parameters, due to which our proposed scheme (DOW-PR) outperformed the two in terms of energy conservation. The simulation results are drawn for energy tax against the nodes number in the network (refer to Figure 11 for mapping into arbitrary values in SET3). The similar trend found for energy tax in all protocols DBR, WDFAD-DBR, and dolphin pod i.e., energy consumption reduces when the nodesnumber increases. This is due to the fact that increasing nodes in the network causes the increase of energy resource and also the probability of successful packet delivery being improved. Therefore, it reduces the retransmissions of the packets as nodes number increases and will significantly reduce the energy wastage. Secondly, our scheme exploited the adaptive nature of data transmission power, which depends upon the maximum displaced node within the transmission range (refer to Figure 5). Consequently, a significant amount of energy saving resulted, in contrast to WDFAD-DBR in which a fixed amount of energy was utilized at each hop regardless of the node displacement. In sparse networks, the transmission power adjustment is not very effective due to the nodes being widely dispersed, and there will be low probability of a nearby potential forwarder for lesser transmission energy usage. On the other hand, in dense networks, there will more likely be the presence of nodes in the maximum transmission range and therefore more options of ranges can be investigated. Generally, source nodes need to transmit with maximum power in case there is a forwarding node in its farthest transmission zone. Consequently, a greater number of nodes will receive the packet due to maximum transmission, but this is a rare occurrence. However, in WDFAD-DBR, the source node will transmit with a fixed maximum power level, no matter if the farthest forwarding node is even lying in a close proximity transmission zone. The above procedure adopted in our proposed scheme significantly reduces the energy consumption without compromising other performance parameters. Therefore,  
Figure 12: (**a**) PDR vs. number of nodes; (**b**) energy tax vs. number of nodes; (**c**) end-to-end delay vs. number of nodes; (**d**) APD vs. number of nodes. Simulation results using arbitrary values in SET3. either being sparse or dense, the proposed protocol (DOW-PR) convincingly beat both DBR and WDFAD-DBR in terms of efficient energy utilization.