# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 4 Routing Protocols for UAWSNs
### Tree-Based Routing  
In tree-based routing, a tree is formed among the all sensor nodes and a sink is the root of the tree. In UAWSNs, the UAV functions as a sink node. All leaf nodes send their data to the respective parent. Each parent node is responsible for aggregating the received data and forwarding the data to the next-level parent node toward the sink. Aerial-based data collection (ADCP): Data collection is a crucial factor for WSNs. Traditional WSNs use sink nodes to collect the sensed data, but this technique is not feasible for mobile sensors deployed for some specific applications such as wildlife monitoring, tracking in smart cities, and monitoring sports events. To address this issue, [PERSON] et al. proposed a data collection and tracking mechanism using a fleet of flying devices (drones) [47]. The main objective of the scheme is to deploy a set of UAVs in a three-dimensional space to cover and collect data from all the mobile sensor nodes using ground-to-air communication. The gathered data are transmitted to the central base station using multi-hop air-to-air communication through UAVs. The optimization of special and temporal coverage with mobile flying drones is formulated as the aerial data collection problem (ADCP) as shown in Figure 6. The authors attempted to fill the gap between the practical networking approach and the theoretical approach through the evaluation and comparison of data collection and tracking algorithms. Hybrid UAV-aided WSN routing (H-UAV-WSN): When a WSN is formed with thousands of sensor nodes and a traditional immobile sink node, this may result in inefficient area coverage and decreased network performance. To deal with this issue, [PERSON] et al. proposed a hybrid UAV-WSN (H-UAV-WSN) network that can be self-configured for the improvement of data gathering across large  
Figure 6: Multi-tier UAWSN network. areas [48]. The routing scheme firstly establishes a decentralized multi-level architecture. The UAV is fitted with a sink node that acts as a data collector. In addition, the UAV can behave as a relay to connect WSNs to a remote base station depending on the application requirement. Such a feature enhances the connection between ground WSNs and the base station. Based on the trajectory, two stages are considered. Firstly, a discovery trajectory is planned. Secondly, a trajectory is planned for data acquisition, which passes through the neighborhoods of CHs. The trajectory is planned in a way that guarantees communication time and obstacle avoidance. However, any collaboration between ground WSNs and UAVs is not taken into account in forming clusters. Topology-aware data aggregation (TADA): In Reference [49], UAV-based data aggregation of WSNs was reported. Traffic volume can be significantly reduced using comprehensive sensing (CS). The existing approaches based on CS have limitations such as excessive overhead in broadcasting and high errors in data reconstruction process. Addressing these issues, [PERSON] et al. proposed a topology-aware data aggregation (TADA) protocol that can sustain the advantages of CS-based schemes while alleviating the previously mentioned issues. One of the main features of TADA is its ability to utilize the topology information to rebuild the raw data with higher precision. The mechanism of weight coding is identical to CS in TADA, but TADA can successfully achieve a short weight vector which results in lower energy consumption. The construction of a measurement matrix representing the scaling of WSN makes TADA more adaptive to dynamic changes in WSN topology. At the beginning of the proposed protocol, a balanced tree-based topology is constructed. The simulation outcomes demonstrate that the TADA scheme shows better performance in comparison to two typical CS-based protocols in terms of the energy efficiency of data aggregation, data reconstruction error rate, and storage requirement. However, the protocol ignores the effect of link transmission failure. In addition, packet loss could severely downgrade the performance of data gathering.