## 4 Comparison
### Comparison of Security Algorithms in Drone Routing
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Innovative Features** & **Performance Metrics** & **Optimization Constraints** \\ \hline \multirow{3}{*}{CBPTCS [11]} & Jointly considers collaboration & \multirow{3}{*}{Average delivery time,} & Limited number of CSs, threshold \\  & between public transport, drones, & number of required CSs, and & distance between CSs according to \\  & and CSs to maximize drones flight & average drone flight time & drone flight time, and connectivity \\  & time to server rural areas customer. & & between CSs and all customers. \\ \hline \multirow{3}{*}{DDC [17]} & Utilizes \(k\)-means algorithm to & \multirow{3}{*}{\begin{tabular}{c} Coverage ratio, and number \\ of CSs \\ \end{tabular} } & \multirow{3}{*}{Limited number of CSs.} \\  & cluster the frequently delivery & & \\  & locations and placing the CSs. & & \\ \hline \multirow{3}{*}{ODCS [46]} & Utilizes the MST to find the optimal & Delivery coverage ratio, and & \multirow{3}{*}{\begin{tabular}{c} Limited number of CSs. \\ number of required CSs. \\ \end{tabular} } \\  & flight path and placement of CSs to & & \\  & extend the drone flight time. & & \\ \hline \multirow{3}{*}{CDNC [51]} & A cloud-based multiple drones & \multirow{3}{*}{\
\begin{tabular}{c} Number of CSs, average \\ drone flight time, and average \\ utilization of CSs \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} Central cloud server must have \\ connectivity with all drones and \\ CSs to obtain the global knowledge. \\ \end{tabular} } \\  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of charging algorithms in drone routing in terms of innovative features, performance metrics, and optimization constraints. Table 7 summarizes the existing techniques in terms of their performance-based features, such as evaluated metrics and innovative ideas. For ensuring security issues via blockchain technology, the security performance of the proposed model was evaluated using several crucial metrics have been considered, such as mining and transaction times, storage cost, and latency of blockchain consensus. We observed that in addition to these parameters, other metrics such as communication and computation overhead, end-to-end delay, and network throughput have also been considered to minimize the overall energy cost to ensure reliable and effective delivery performance. This is mainly because adding an extra payload on the drone causes the battery to drain much faster, which directly affects the overall flight time. Furthermore, energy consumption varies continuously with the dynamic payload weight. Weather uncertainty and customer demand are some of the major issues that remain underexplored in the existing study. For example, heavy wind and rain can harm the aircraft and would risk the lives of the citizens. This would incur immense penalties on the service providers. Furthermore, in urban areas, the amount of package delivery is extremely high and drones from other service providers as well as the high-rise buildings make it challenging to design routes with minimum flight time. Thus, uncertainty-aware route planning is still an open issue in the field of drone-based logistics. To design an effective and reliable drone-based delivery system, various design aspects need to be considered, which was the goal of this study. Thus, although the existing studies present promising development in this area, further research is needed to address the security concerns in large-scale delivery scenarios and in uncertain conditions.