# A Novel Cooperative Opportunistic Routing Scheme for Underwater Sensor Networks
## 5 Experimental Results
### Results and Analysis
However, the VBF and HHVBF routing protocols are confined to their pipelines and are not flexible enough to find a path with a minimal number of hops to the sink. Furthermore, the discovered path by VAPR has more hop counts compared to OVAR, because it is only performed based on the directional trails and not on the basis of the hop count value. Figure 13: Average end-to-end delay _vs._ node density. Propagation deviation factorThis is a normalized value for showing the total traversed distance by each packet from the source to the sink and can be calculated using [22]:  
\[Pdf=\frac{TD-SD}{SD} \tag{24}\]  
where TD and SD are total travelled distance and the straight line distance from the source to the sink node, respectively. The propagation deviation factor is increased if packets are delivered to the destination through a longer distance. An efficient routing protocol selects the best path, or one very close to the best one, with the lowest propagation deviation value out of many available routes to the destination. The propagation deviation factor of routing protocols is shown in Figure 15. By increasing the number of nodes, the shorter routing paths can be established by the routing protocols. As can be seen, OVAR has obtained the lowest value in comparison to other routing protocols. This indicates that hop count and depth are adequately combined to reduce the traversed distance. The advantage of our method is that it can reduce the traversed distance without any knowledge about the node coordinates. On the other hand, the paths discovered by VBF and HHVBF are far away from the optimal path in sparse networks. Nonetheless, VBF can obtain a good propagation deviation factor in a dense network when its constant pipeline includes the sufficient number of nodes close to the straight line between the source and sink. Although VAPR avoids all kinds of voids in its routing path, it is not always interested in the forwarding nodes closer to the sink (direct line between the forwarding node and sink), but the nodes with more advancement toward up or down, depending on the directional trails. Impact of beaconing intervalIn order to evaluate the impact of beacon intervals on OVAR performance, we conduct extensive simulations at varied beacon intervals of 30 s, 90 s and 150 s under the same operational condition as before. The impacts of different beacon intervals on the packet delivery ratio, average end-to-end delay and energy consumption are shown in Figures 16-18, respectively. Figure 14: Average hop count _vs._ node density. Figure 16: Impact of different beacon intervals on the packet delivery ratio. Figure 15: Propagation deviation factor _vs_. node density. As can be seen in Figure 16, by increasing the beacon interval, the packet delivery ratio is decreased because the routing and neighbourhood information gradually become outdated with the passing of time. Furthermore, by considering the longer intervals, all,of the estimates about the packet delivery probabilities can become obsolete due to node movement. Thus, the beacon interval should be set in a way that the packet delivery ratio reaches the highest possible value without imposing high overhead to the network. On the other hand, late updating can potentially increase the latency of received packets. This is due to the fact that packets are relayed over the non-optimal paths because the forwarding decisions are partially based on the outdated information. As depicted in Figure 18, energy consumption is further decreased by postponing the beaconing at the cost of deteriorating other performance metrics. Figure 11:0 Impact of different beacon intervals on the average end-to-end delay. Figure 11:1 Impact of different beacon intervals on the energy consumption.