# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 5 Comparison of Routing Protocols  
In this section, the performance of existing routing protocols for UAWSNs is qualitatively compared in terms of routing policies and metrics, outstanding features, and characteristics. The routing policies and metrics of the existing protocols are summarized in Table 2.  
Routing policies and metrics have a vital effect on the performance of routing protocols. Generally, WSNs have a large number of nodes with sensing, communication, and computation capabilities. The energy, bandwidth, and computational capacity are limited in sensor nodes. To achieve high-energy efficiency and a better lifetime, different routing protocols can be applied to different applications based on the operational environment. Routing policies and metrics for UAWSNs are summarized for each routing protocol in Table 2. From our study, it was found that the policies of optimized link routing and shortest-path routing are widely used in routing protocols for UAWSNs. In position-based routing protocols, however, the policies of distance-estimation routing and position computation-based routing are used. Major advantages and limitations of existing routing protocols in UAWSN are summarized in Table 3. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Protocol**} & \multicolumn{1}{c}{**Routing Policies and Metrics**} \\ \hline HHA & Optimized link routing \\ \hline SN-UAV & Optimized link routing + shortest path routing \\ \hline UAV-WSN & Traffic allocation-based data driven \\ \hline UAV-AS-MS & Optimal link state routing \\ \hline ULSN & Liner sensor cluster routing + traffic allocation-based routing \\ \hline URP & Dynamic cluster-based routing + node estimating and data-driven routing \\ \hline C-UAV-WSN & Distributed cluster-based routing + freshest path routing \\ \hline rHEED & Dynamic and distributed cluster routing + dynamic path routing \\ \hline UADG & Data-driven routing + minimum-cost path routing \\ \hline DPBA & Traffic allocation-based routing \\ \hline EEDGF & Short distance-based routing \\ \hline PCDG & Optimized and shortest path routing \\ \hline ADCP & Dijkstra shortest-path routing \\ \hline H-UAV-WSN & Deterministic clustering mechanism \\ \hline TADA & Topology-aware multi-path routing \\ \hline UAV-CDG & Projection-based CDG data collection + shortest UAV trajectory \\ \hline EEJLS-WSN-UAV & Distance-estimation routing + position computation-based routing \\ \hline LS-UAV-WSN & Shortest-path routing + data-driven routing \\ \hline PSO-WSN-UAV & Optimized link routing + shortest path-based routing \\ \hline FSRP & Shortest-path routing + optimized link routing \\ \hline EFUR-WSN & Shortest UAV route with modified Voronoi diagram and optimal UAV hovering locations. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Routing policies and metrics for UAWSNs. In Table 4, the 21 existing routing protocols in UAWSNs are compared with respect to various features, protocol operations, characteristics, and distinct performance factors. It should be noted that GPS and RSSI are widely used in most of the routing protocols to obtain the location information of UAVs. The design of a proper routing protocol in UAWSNs is dependent on the requirement, network scenario, and application of the target UAWSN. From our study, most of the routing protocols consider only a single UAV for collecting data from sensor nodes. On the other hand, a multi-UAV-oriented routing protocol can show a robust network lifetime, less energy consumption, and less end-to-end delay. It is generally known that sensor nodes are power-limited; therefore, energy efficiency is the most significant objective in the design of a routing protocol. As compared in Table 4, most of the routing protocols show better performance in terms of energy consumption by adopting UAV-based data