## 4 Simulator Environments  
The first step in evaluating the performance of an OppNet protocol is the selection of the simulation platform to use. In general, there are two main options: custom built simulators and \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Protocol** &
\begin{tabular}{} \end{tabular} & **Concise Description** \\ \hline Multi-S\&W & 2017 & Spray and wait routing with next hop selection based on weighted sum of betweenness centrality, friendship index and similarity index. \\ \hline FGAR [54] & 2017 & Adaptive replication and forwarding based on contact prediction and success probability of delivery between contacts in a given time interval. \\ \hline IBR [55] & 2017 & Effects of interactions between nodes in terms of popularity without detection of communities is the forwarding strategy. \\ \hline FC-DFCM [56] & 2017 & Relationship strength of node pairs and contact durations are used in selection of forwarders. \\ \hline EIMCT [57] & 2017 & Social network based contact durations are considered for forwarding decisions. \\ \hline SAPR [58] & 2017 & Social characteristics of nodes and their mobility are the factors for making forwarding decisions. \\ \hline CPR [59] & 2017 & Prediction-based routing decision considering statistical contact information, contact transitivity and instant contact information. \\ \hline TCCB [60] & 2017 & Temporal social contact patterns and temporal centrality prediction are used to select forwarders. \\ \hline FARS [61] & 2018 & Fairness based routing involving weighted factors of contact duration, residual buffer and historical amount of delivered data. \\ \hline ELPFR-MC [62] & 2018 & Energy-aware routing based on node’s residual energy, location prediction and delivery probability. \\ \hline Predict and Forward [63] & 2018 & Next hop selection based on node profiles and attributes along with historical encounters to calculate delivery probability. \\ \hline IoR [64] & 2018 & Average time since the message generation, average distance travelled in hops and delivery predictability of ProPhet are used to select forwarders. \\ \hline CAOF [65] & 2018 & Relay selection is based on node’s activeness to meet nodes in its own community and different communities within a bounded time. \\ \hline CAF [66] & 2018 & Adaptive weighted combination of friendship index, similarity index, centrality, contact strength and trust to find the suitable relays. \\ \hline CbR [67] & 2018 & Nodes belonging to clusters with good delivery capability are preferred than just the node’s utility value. \\ \hline RBES [68] & 2018 & Congestion level at a node and contact history of nodes are together used for forwarding strategy. \\ \hline kROp [69] & 2018 & Partition of neighbors to clusters and next hop is determined by an evaluation function to find the cluster with optimal delivery capability. \\ \hline PBQ [70] & 2018 & Delivery probability is calculated by Poisson based distribution along with consideration of node’s daily routines and mobility patterns. \\ \hline MLProph [71] & 2018 & Enhanced delivery predictability of ProPhet with machine learning by considering node’s popularity, power consumption, speed, location and frequently encountered nodes. \\ \hline EPSoc [72] & 2018 & Social-based epidemic routing where degree centrality of nodes is used for next hop selection. \\ \hline CoSim [73] & 2018 & Cosine similarity of the data packets between nodes are used to find the similarity of the nodes which in turn are used to forward messages. \\ \hline CGR [74] & 2018 & Replication based on scheduled contact patterns with predetermined network prediction. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._standard simulators. The standard simulators usually used for OppNets are ONE [75], OMNeT++ [76], ns-2/ns-3 [77], MobEmu [78], and Adyton [79]. For both custom-built and standard simulators, there is also the option of providing the original code for reproducibility purposes or not.