## 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}{p{113.8 pt}} **Year of** \\ **Publication** \\ \end{tabular} & **Concise Description** \\ \hline Prophet v2 [31] & 2011 & Fine-grained encounter rates by taking into account the unsuccessful intermittent connections, thus increasing the reliability of delivery predictability. \\ \hline BubbleRap [32] & 2011 & Social and structural properties such as centrality and community metrics are used to select forwarders. \\ \hline R3 [33] & 2011 & Estimates end-to-end delays of different paths to the destination and selects the path with best replication gain, uses adaptive replication. \\ \hline
3R [34] & 2011 & Estimates encounter probability by prediction of fine-grained regular encounters pertaining to a time window in a given day. \\ \hline dLife [35] & 2012 & Daily routines of users are used to increase the accuracy of predicting future social contacts. \\ \hline Sprint [36] & 2013 & Predicting the future contacts over a given time by analyzing the mobility patterns and using additional information on social contacts. \\ \hline SGBR [37] & 2013 & Identifying social groups based on frequent and longer contact durations, social properties used to route packets between different communities. \\ \hline Scorp [38] & 2013 & Prediction of probability of encounters having content-specific interests among the neighbors with similar social interests and daily routines. \\ \hline HBPR [39] & 2013 & Exchange of location updates between nodes and predicting the direction of future movements to find shortest paths to the destination. \\ \hline Onside [40] & 2014 & Community identification based on similar social interests and exchange of interests table between nodes to chose forwarders. \\ \hline JDER [41] & 2014 & Targets high reachability by giving preference to selecting nodes connected to multiple communities (cut-nodes) as forwarders. \\ \hline GAER [42] & 2014 & Genetic algorithm based next hop selection, distance between mean of home locations of neighbors to destination along with a fitness function is used to select forwarders. \\ \hline PRoWait [43] & 2015 & Uses delivery predictability of Prophet to select forwarders combined with spray and wait routing. \\ \hline Pathsampling [44] & 2016 & Learns network topology by using probes sent along with the beacons and selects forwarders based on estimated end-to-end delivery probability. \\ \hline CGrAnt [45] & 2016 & Local information, situational information and domain information is used to select forwarders. \\ \hline ABCON [46] & 2016 & Relay nodes are selected by the number of encountered neighbors. \\ \hline EER [47] & 2016 & Calculates expected encounter value for every neighbor by using contact durations and frequent contacts which determines the forwarders and the number of copies. \\ \hline RPC [48] & 2016 & Estimates reachability probability computed considering centrality measure and encounters rates, which is used in selecting forwarders. \\ \hline EDR [49] & 2016 & Encounter rates with the destination and estimated distance to the destination are used to select forwarders. \\ \hline GSAF [50] & 2016 & Destination dependent identifier is used to spray messages to forwarders. After the message is in the locality of the destination, it is flooded to all neighbors. \\ \hline HPR [51] & 2016 & Delivery predictability of nodes with spraying is the forwarding strategy. \\ \hline E2 FA [52] & 2016 & Delivery predictability of nodes and buffer utility are used for choosing forwarders. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._