# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 4 Evaluation and Analysis
### Results and Analyses  
The performance of our proposed strategies, viz., Threshold, RRE and ORRE are evaluated with four DTN routing protocols: Epidemic, Prophet, Spray and Wait, and Direct Delivery. The statistics based on three randomized trials are calculated and we show the average results for both flying foxes and pigeon scenarios.  
Figure 4 shows the daily packet delivery rates of DTN routing protocols using Threshold, RRE and ORRE strategies for the flying fox scenario. In this figure, a threshold of zero corresponds to the original DTN algorithms and a threshold of one corresponds to direct packet deliveries to the base station similar to the Direct Delivery protocol.  
Figure 4 shows that the performance of energy-unaware algorithms (zero threshold) can be improved for the four DTN routing protocols at the optimal threshold. Similarly, all protocols perform the same as _Direct Delivery_ when the threshold reaches one. The optimal threshold for all protocols is 0.5 and improves the original _Epidemic_, _Prophet_, _Spray and Wait_ protocols by 12%, 10%, 10%, respectively.  
_Prophet_ protocol performs the best in Threshold strategy as _Prophet_ is able to best make use of the regular daily patterns and periodic contacts between nodes.  
The results of RRE strategy are slightly worse than the optimal threshold in the Threshold algorithm. However, the ORRE strategy improves the RRE performance up to 4% and closes the gap to the optimal Threshold strategy.  
Figure 5 shows the packet delivery rates of DTN algorithms for three strategies with the pigeon scenario. It is clear that once we enable energy awareness, the Threshold strategy can improve  
Figure 4: Packets delivered per day for Threshold, RRE and ORRE strategies for flying foxes.  
the energy-unaware version of all algorithms except _Direct Delivery_ by up to 13%. In this scenario, the optimal threshold is 0.7 and the four DTN protocols perform similarly to each other.  
We note that all protocols perform better than the _Direct Delivery_ protocol for a threshold close to one. This is because the battery capacity of the node is greater than the daily harvested energy. Consequently, per Equation (1) of the Threshold strategy, nodes can forward packets if they store more energy than the daily harvested energy. In this scenario, nodes harvest energy during the day and forward packets to other nodes while they meet and commute within foraging areas. Similar to the flying fox scenario, ORRE improves the RRE strategy by up to 4%.  
We now evaluate latency and routing overheads of the Threshold strategy to provide more details on the performance of the algorithms in different scenarios.  
Figure 6 shows the _Average latency_ for the Threshold strategy in _Pigeon_ and _Flying fox_ scenarios. In pigeon scenario (a), the average latency increases as the nodes limit their participation in data forwarding, due to reserving more energy for communication with the base station. Therefore, larger thresholds lead to routes that have fewer hops to the base station. However, in the flying fox scenario, increasing the threshold past the optimum leads a fall in the average latency as the nodes generated many packets and consume all allocated energy before entering the foraging area.  
Figure 5: Packets delivered per day for Threshold, RRE and ORRE strategies in pigeon scenario.  
Figure 6: \(Cont.\)  
We also present the overhead ratio of algorithms as shown in Figure 7. The figure indicates that in both scenarios, when threshold increases, the overhead ratio decreases. This is because participation of nodes in data forwarding is limited which leads to less communication and forwarding few packets to other nodes.  
Figure 6: Average latency for Threshold, RRE, ORRE for (**a**) pigeon and (**b**) flying fox scenarios.  
Figure 7: Overhead ratio for Threshold, RRE, ORRE for (**a**) pigeon and (**b**) flying fox scenarios.