# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 4 Routing Protocols for UAWSNs
### Network Structure-Based Routing
#### 4.1.2 Hierarchical Routing
One is the inclusion of WSN operation for updating the UAV flight plan, and the other is the consideration of UAV trajectory to facilitate data acquisition. In the network model of Reference [41], the WSN is assumed to be deployed on the ground and the nodes are supposed to organize themselves into clusters as shown in Figure 4. CMs collect data and send them to the CHs periodically. The CHs must aggregate the data before delivery to a UAV. The role of the CH is rotated within the network, and the transmission range of the new CHs fixes the flying zones for the UAV. The energy consumption and network lifetime were remarkably improved in Reference [41]. The main drawback of this system was its inability to perform better in large-scale sensor networks. Hybrid and energy-efficient distributed (rHEED): When a large number of sensor nodes are deployed in a geographical area, the energy-hole problem can be an issue that can cause nodes closer to the static sink node to die faster. A received signal-strength indication (RSSI)-based hybrid and energy-efficient distributed (rHEED) protocol [42] is a clustering scheme in WSNs that incorporates  
Figure 4: UAV-aided data gathering system. Figure 3: Data collection method in UAV-assisted routing protocol (URP). UAV-based mobile sinks to overcome the energy-hole problem. Its network model consists of sensor nodes that collect data and transmit them to CHs. Then, all the CHs forward their data to the mobile sink node, which is the UAV. It is assumed that the UAV can move in three dimensions with variable speeds. The protocol can successfully prevent unnecessary cluster formation using the UAV path as a parameter to form the clusters. rHEED does not consider wind effects and UAV travel time over a large-area network, which are major drawbacks of this protocol. UAV-assisted data gathering (UADG): In UADG [43], the issue of concurrent data transmission between densely populated sensor nodes and high-speed UAVs working as sink nodes is exploited. In UADG, a data-acquisition algorithm is introduced, which involves the UAV and mobile agents (MAs) for autonomous data collection and processing. Sensor nodes are grouped in UADG, and a node with the highest residual energy is elected as a leader node among the nodes in a group. MAs also collaborate with one another to avoid redundant visits to the leading nodes. MAs must finally return to the UAV with the integrated data. Even though UADG is energy-efficient, it does not consider parallel processing of the mobile nodes, which could further increase the performance. Dynamic programming-based algorithm (DPBA): Because of limited resources in WSNs, it is quite challenging to allocate them wisely to increase the data transmission rate. In the dynamic programming-based algorithm (DPBA) [44], bandwidth and energy allocation are incorporated to increase the total transmission rate. A two-layer paradigm is taken under consideration to design the network model of the protocol. In the first layer, sensor nodes sense data from the environment and transfer them to UAVs. In another layer, UAVs send the gathered data to the outside world. Only the first layer was considered in Reference [44]. The whole system is time-slotted. UAVs move along their trajectories over a sensor field to gather the data from the sensor nodes. The protocol can increase the overall data transmission rate. The protocol, however, does not allow for the prospect of multiple UAVs collecting data from the sensor field. Energy-efficient data gathering framework (EEDGF): In Reference [45], the authors proposed a multi-UAV network-oriented energy-efficient data gathering framework (EEDGF) in deadline-based WSNs, which can be used in various UAV-based applications. In a large WSN, the sensed data can be easily collected in saved time with UAVs, as shown in Figure 5.  
This work gives a framework for deadline-based multi-UAV WSNs by formulating an integer linear programming model.