## 4 Multicast Routing Approaches in DTN
### Geographical Multicast Routing in DTN
#### 4.4.3 Discussion  
Ferry/mul-based routing is a type of routing with infrastructure assistance that adopts additional mobile nodes to bridge the communication between disconnected networks. The Message Ferrying (MF) concept is adopted in Reference [73] for sparse MANETs to relay messages using ferries to reach a specific region. Ferries travel around the specific region to forward and collect data. The authors in Reference [73] extended their work for DTN communications using multiple ferries and different route designs [74] via four approaches: Single Route Algorithm (SIRA), Multi-Route Algorithm (MURA), Node Relaying Algorithm (NRA), and Ferry Relaying Algorithm (FRA) approaches. Another work in Reference [75] categorizes ferries into two types: Regional ferries belonging to the source region, and independent ferries, which do not stick to any region. However, none of the proposals address multicast routing in DTN except FBIMR [67] and NewVDTN [68]. FBIMR forwards multicast messages between different domains with the help of message ferries to achieve inter-domain multicasting. NewVDTN selects the data mule based on the congestion level of the network for multicast message delivery. Table 4 shows the performance comparison of geographical multicast routing in DTN. In FBIMR, the increased number of ferries improves the overall performance more than increasing the speed of the ferry in a single ferry deployment. NewVDTN is well suited for sparse networks, but its performance decreases when the network density is higher. The deployment of data MULEs requires careful consideration of the network density and the number of MULEs; even the trajectory of the MULEs plays an important role. For location-based schemes, CTMR [72] limits the communication attempts by the implementation of a time contrast to better conserve energy and results in higher delivery delay.  
The ferry/mul-based approach is a promising approach to improve the performance of DTN multicasting. Unmanned Aerial Vehicles (UAVs) or drones are being used as ferries for data dissemination and collection. For example, the authors in [76] used a drone in DTN to lower the cost of implementing smart cities monitoring. In Geographic Spray and Wait (GeoSaW) [77],  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multirow{2}{*}{**Key Solution**} & **Delivery** & **Delivery** & **Delivery** & **Energy** & **Mobility** & **Compared** \\  & & **Ratio** & **Cost** & **Delay** & & **Algorithm** & **Algorithm** \\ \hline \hline \multirow{2}{*}{FBIMR} & Message Ferry & High & Low & Low & - & \begin{tabular}{c} VANET \\ and RFGM \\ \end{tabular} & - \\ \hline \multirow{2}{*}{NewVDTN} & Node Density & \multirow{2}{*}{Low} & \multirow{2}{*}{Lower} & \multirow{2}{*}{Higher} & - & \begin{tabular}{c} Map Based \\ Movement \\ and Static \\ \end{tabular} & Epidemic \\  & & & & & and Static \\ \hline \multirow{2}{*}{GeoSDFA} & Social & \multirow{2}{*}{High} & \multirow{2}{*}{Lower} & \begin{tabular}{c} Slightly \\ Lower \\ \end{tabular} & - & \
\begin{tabular}{c} Real \\ Traces \\ \end{tabular} & GeoMob \\  &
\begin{tabular}{c} Delivery \\ Probability \\ with Queue \\ \end{tabular} & High & - & Moderate & Reduce & High & DTBR \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of geographical multicast routing in DTN.  
the current location and scheduled path information of UAVs are utilized to determine the most suitable UAV to relay messages to the destination. The reliability of message delivery can be further enhanced by location-aware algorithms using geographical information gathered from GPS equipment or localization techniques.