## 4 Evaluation
### Lessons Learned  
It is important to note that simply involving more nodes in the routing process does not achieve fairness. The idea is not only to allow a higher number of less popular nodes to participate in routing, but to also determine a means for them to reduce the burden on popular nodes and contribute towards improving throughput. Simply involving more relay nodes would be Epidemic routing, and this approach does not guarantee improved fairness. Since Epidemic forwards messages to nodes irrespective of their delivery ability, fairness is determined by the distribution of encounter opportunities among nodes--note that popular nodes are likely to experience more encounters. In other words, the rate at which a node running on Epidemic receives messages is determined by its level of popularity. Hence, simply involving more nodes in routing without providing them with a means to reduce the burden on popular nodes degrades fairness and throughput instead. An important lesson learned is that metrics such as transmission overhead and average energy consumption tend to increase when more nodes contribute in routing. This can be misleading during performance evaluation if not properly understood. By increasing the number of central nodes (i.e., shifting excess burden from popular nodes to less popular nodes so that the later become more popular), nodes that would have been idle--in terms of contribution--become more active, hence carry out more transmissions and consume more energy. Since less popular nodes are significantly higher in population, the average energy consumption is increased when they are entrusted with more messages. Furthermore, a considerable portion of their incurred burden is also lost to failed transmission attempts, just as the case with popular nodes. Hence, considering every node in the network, the transmission overhead and energy consumption increases. This is only noticeable when comparing FLARoute with CCAF, as FLARoute reduces these metrics significantly when incorporated into PRoPHET. A similar behaviour was observed with [PERSON] and [PERSON]'s [19] proposal, where PFA-PeopleRank and MCFA-PeopleRank--their fair extensions of PeopleRank--incurred more cost (i.e., transmission overhead) than the original version of PeopleRank. According to the authors, the cost increase per node is not significant even though the total number of message replicas in the network is increased, and that the increase in cost becomes significantly smaller when the number of nodes in the network grow to reflect a more realistic deployment. Another lesson learned is that, in the presence of strong spatial locality, allowing more number of less popular nodes to contribute in forwarding increases the number of hops required to deliver messages as well as delivery delay. Popular nodes often happen to be the ones that either travel longer distances, visit more locations or encounter more number of nodes within shorter periods. Thus, evading them, especially in environments characterised by strong spatial locality, inevitably incurs additional delivery delay. With respect to non-delay sensitive applications in such environments, what is more important is the ability to deliver messages within their lifetime. In order words, it is more important to maximize throughput than minimize delivery delay. Hence, incurring lower delivery delay that will lead to reduced throughput is not a preferable trade-off for delay-tolerant applications. It is also important to note that changes in fairness tend to occur in slight percentages, especially under higher node population. An example is [PERSON] et al. 's [76] fairness comparison of their fair versions of existing routing protocols on the MITreality data set using [PERSON] et al. 's [77] fairness index.