# UAV Charging Station Placement in Opportunistic Networks
## 7 Routing Protocols
### State-Based Campus Routing (SCR) Protocol
It is worth noting that message delay and success rate are not dependent, meaning that a scheme that produces low message delay values does not necessarily guarantee a high success rate. For example, if a message is transmitted to only one node during its lifetime, it will have a low message delay and a low success rate as well. #### 8.3.3 Number of UAV Tours

Figures 12 and 13 shows the number of tours made by the UAVs when each of the nine schemes is used in the NCSU and KAIST datasets, respectively. A tour begins when a UAV takes off from the charging station until it returns and lands at the charging station again. Meaning that every time a UAV takes off it starts a new tour. The figures show that the minimum number of UAV tours is obtained when K-Means clustering is used, the three routing protocols (Epidemic, SCR, and Spray and Wait) yield similar performance. Our simulation shows that the total distances traveled by the nine schemes are similar which is around (16,266 km). This means that a UAV travels the same distance no matter which duet of clustering approach and routing algorithm is used, however, it made a fewer number of tours when K-Means is in effect. The lower number of tours means there were fewer numbers of landings and take-offs, which concludes that K-Means Clustering is the most energy-efficient approach as UAV land whenever it needs charging. Figure 11: Box plot distribution results of Success Rate on KAIST Dataset. Figure 12: Number of UAV Tours in NCSU Dataset. Figure 10: Box plot distribution results of Success Rate on NCSU Dataset. #### 8.3.4 Number of Messages Sent

Figures 14 and 15 shows the number of messages sent by the nine schemes in the NCSU and KAIST datasets, respectively. It is interesting to note that when Epidemic routing is used with any clustering algorithm, it gives the highest total number of messages sent, which is an indication that it is a less energy-efficient routing protocol. This observation conceptually matches the fact that Epidemic routing consumes network resources at a high rate. From the metrics introduced in this section, it can be inferred that SCR manages to balance the load and energy more efficiently than the other protocols. Figure 14: Number of UAV Tours in NCSU Dataset. Figure 13: Number of UAV Tours in KAIST Dataset. Figure 15: Number of UAV Tours in KAIST Dataset. #### 8.3.5 Number of Messages Added to Buffer

When an encounter session is initiated, the nodes and UAVs exchange protocol messages, as well as actual messages. The more actual messages exchanged between nodes, the more efficient the communication becomes in an opportunistic network. If the number of messages added to buffers is small, it can be interpreted as the messages are not being distributed efficiently. For both datasets, K-Means shows a higher number of messages added to buffer comparing to DBSCAN and randomly places charging stations. Figures 16 and 17 shows the number of messages added to buffer. ## 9 Conclusions

UAVs are becoming more widely used in many military and civilian applications. On the other hand, the limited battery life of UAVs creates challenges that need practical solutions. In this paper, we proposed a framework to solve the problem of charging station placement in a UAV-aided opportunistic network. The framework consists of three main parts which together form the proposed solution: (i) the way UAVs scan the area of interest; (ii) the use of three routing protocols to forward messages during UAV-to-UAV, UAV-to-Node, and Node-to-Node communication; and (iii) the use of clustering approaches to find the best spot to place the charging stations. Different network performance metrics for all combinations between routing protocols and clustering approaches have been closely investigated to give the best understanding and assessment for the proposed framework. The simulation study shows that K-Means clustering gives better results when compared to the DBSCAN and Random clustering approaches.