## 6 Results and Analysis
### Results and Analysis  
The simulation experiments involve randomly generated topologies with varying actor counts and communication ranges. The number of actors has been set to 20, 40, 60, 80 and 100. The communication range of actors is changed among 50, 100, 150 and 200. When changing the node count, \"_r_\" is fixed at 100 m; and \"_N_\" is set to 60 while varying the communication range. Like baseline approaches, PCR is meant to recover from single actor failure. PCR may handle multiple node failures if they are far apart from each other and cause no conflicts during recovery. However, PCR and baseline approaches for a single node failure are not guaranteed to converge. Simultaneous failure of multiple nodes may cause conflicting conditions for PCR to converge successfully. Generally, the possibility of multiple simultaneous actor failure is exceptional; however, it may precipitated by disastrous events such as explosions in a battlefield. In such a case, failed actors are usually co-located. Recovery from such failures is very challenging and requires careful consideration. To handle a special case of multi-node failure, we have presented RAM in [27]. In the experiments, we identify critical actors and choose a cut-vertex at random to be failed. The results of individual experiments are averaged over 30 trials to ensure statistically stable results. All results are subject to 90% confidence interval analysis and stays within 10% the sample mean. _Total distance moved_: Figure 8(a,b) show the total distance traveled by nodes involved in recovery. PCR significantly outperforms reactive schemes, _i.e._, DARA and RIM, because it strives to move non-critical nodes in order to prevent successive relocations. Both the graphs suggest that the performance advantage of PCR remains almost consistent while varying \(N\) and \(r\). This is because PCR strives to avoid moving critical nodes that causes further partitioning and requires cascaded relocations. Furthermore, PCR only performs cascaded relocations in case non-critical backup in the neighborhood is not available. Figure 8(a) indicates that the performance of PCR scales very well and is not affected by the node density because of choosing non-critical nodes as backup. Similar observation can be made for the communication range as in Figure 8(b), where the movement overhead is significantly less as compared to the reactive schemes. However, PCR slightly performs worse than DCR. This is because DCR prefers to engage strongly connected nodes as backup. The rationale is that there is more probability to have non-critical nodes in the neighborhood of highly connected nodes. _Number of moved nodes_: Figure 8(c,d) report the scope of recovery when PCR and the baseline approaches are applied. The graphs confirm the performance advantage of PCR which moves fewer actors than reactive schemes. This is because PCR limits the scope of recovery and avoids successive cascaded relocations by choosing non-critical nodes as backup. Furthermore, the performance of PCR remains almost constant while varying \(N\) and \(r\), which indicates great scalability. However, again DCR slightly moves less number of nodes than PCR due to the same reason mentioned above. _Number of messages exchanged_: Figure 8(e,f) show the coordination messaging overhead as a function of the \(N\) and \(r\). As expected, hybrid schemes (PCR and DCR) cause far less coordination overhead than DARA and RIM. This is because they limit message exchange only between a pair of primary and backup instead of all 1-hop and 2-hop neighbors as is the case in RIM and DARA respectively. In addition, unlike reactive schemes, PCR and DCR strive to engage non-critical nodes in the recovery which does not require coordination messages. Figure 8(e) shows that the messaging overhead of reactive schemes increases with the high node density. This is because the number of 1-hop and 2-hop neighbors increases.