## 3 Methodology of the Survey
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Protocol** & \begin{tabular}{c} **Year of** \\ **Publication** \\ \end{tabular} & **Concise Description** \\ \hline Epidemic & 2000 & Flooding of all the messages to all the neighbors. \\ \hline Fresh [12] & 2003 & Nodes having the most recent encounters with destination are chosen as forwarders. \\ \hline SEPR [13] & 2003 & Nodes with the shortest expected path length to the destination are selected as forwarders. \\ \hline Seek and Focus [14] & 2004 & Randomized forwarding and Utility-based forwarding are used based on encounter rates. \\ \hline Spray and Wait [15] & 2005 & Controlled-replication. Spraying \(L\) copies of message \(M\) to relays and holding the final copy until the destination node is met. \\ \hline MobySpace [16] & 2005 & Nodes having similar mobility pattern to the destination node. \\ \hline Maxprop [17] & 2006 & \begin{tabular}{c} Estimating highest delivery likelihood of neighbors to the destination based on frequency of encounters with the destination. \\ \end{tabular} \\ \hline Spray and Focus [18] & 2007 & Spraying message copies to the neighbors and utility forwarding \\ \hline Prioritized ER [19] & 2007 & \begin{tabular}{c} Estimation of routing cost to destination and prioritization of packet bundles to transmit based on routing cost. \\ \end{tabular} \\ \hline HiBop [20] & 2007 & \begin{tabular}{c} Contact history and stored context information about neighbors are used for selection of forwarders. \\ \end{tabular} \\ \hline Geo-Opps [21] & 2007 & \begin{tabular}{c} Using geographic location of the destination and prior known route schedules of destination and encountered neighbors. \\ \end{tabular} \\ \hline Propicman [22] & 2007 & \begin{tabular}{c} Estimating the probability of meeting the destination by sending probes to two hop neighbors. \\ \end{tabular} \\ \hline Utility-based & 2007 & \begin{tabular}{c} Nodes choose between different utilities such as most mobile first, most social first, etc. for selection of relays. \\ \end{tabular} \\ \hline Rapid [24] & 2007 & \begin{tabular}{c} Adaptive replication of copies based on a chosen utility metric such as minimizing average delivery latency, minimizing maximum latency, etc. \\ \end{tabular} \\ \hline ORWAR [25] & 2008 & \begin{tabular}{c} Controlled-replication such as spray and wait routing combined with prioritization of messages by choosing required utility metrics. \\ \end{tabular} \\ \hline EBR [26] & 2009 & \begin{tabular}{c} Replication-based and selection of forwarders based on the number of encounters with the destination. \\ \end{tabular} \\ \hline CAR [27] & 2009 & \begin{tabular}{c} Evaluating and predicting context information such as mobility locally and by sending updates to neighborhood, using delivery predictability for relay selection. \\ \end{tabular} \\ \hline SimBetTs [28] & 2009 & \begin{tabular}{c} Relay selection based on social analysis on betweenness centrality, similarity index and strength of ties between nodes. \\ \end{tabular} \\ \hline FairRoute [29] & 2009 & \
\begin{tabular}{c} Tie strength and social status of nodes to assist in forwarding decisions. \\ \end{tabular} \\ \hline Prophet+ [30] & 2010 &
\begin{tabular}{c} Enhancements to delivery predictability by considering node’s buffer, power, location, popularity along with the delivery predictability obtained from prophet. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: OppNet protocols involved in this study.