## 4 Evaluation
### Results and Discussion
#### 4.2.1 Performance Evaluation in the Skudai Scenario  
Figure 8 records the difference between the average estimated maximum burden on neighbouring nodes and the actual maximum burden in the network. The results show that the average estimate converges to about 20% to 50% of the actual maximum burden in the second day of message generation. We do not expect a precise estimate, and consider this outcome satisfactory for the following reasons: (i) it is only an average of different local estimates, and the error bars suggest that estimates may vary significantly among nodes; and (ii) each node has its own set of neighbours, and the maximum burden in the network may vary significantly from the maximum among each node's neighbours. The next set of results show the performance of CCAF and FLARoute incorporated into PRoPHET routing protocol, as well as the other selected routing proposals in the Skudai simulation scenario. The fairness of the routing techniques is shown in Figure 9. FLARoute records the highest fairness of 97.9%. SnF and CCAF record a fairness of 97% and 96.2%, respectively. dLife and PRoPHET are closely tied at 95.9% and 95.8%, respectively. Bubble Rap shows a 95.3% fairness, and HERO records the least fairness at 93.3%. Although the difference in fairness may seem small, we observe that a slight change in fairness leaves a noticeable impact on the energy consumption differences  
Figure 8: The difference between average maximum burden among neighbouring nodes estimated by each node with MaxBE, and the actual maximum burden in the network at different times during the simulation. between the central nodes and the other nodes. Note that only two proposals, CCAF and FLARoute, were incorporated into PRoPHET. Bubble Rap, dLife, HERO and SnF were only included in the results to show how the existing routing approaches perform under high degrees of spatial locality. Hence, in order to capture the impact of CCAF and FLARoute, their performance should be compared with that of PRoPHET alone. As shown in Figure 10, FLARoute records the highest energy distribution at 43.3%. This means that the average energy expended on the other nodes is 43.3% of that expended on the central nodes. This is followed by SnF and PRoPHET at 24.2% and 17.1%, respectively. According to the pattern of routing fairness in Figure 9, CCAF falls behind PRoPHET with an energy distribution of 11.3%. This is because with CCAF, the difference in the burden between members of the other nodes ranked according to their contribution in routing (i.e., from the most burdened to the least burdened) is more subtle as compared with PRoPHET. Normal order resumes with dLife, Bubble Rap and HERO, recording an energy distribution of 11.2%, 6.9% and 4.3%, respectively. FLARoute's performance as compared with PRoPHET's shows that the impact of an additional 2.1% fairness could be as much as 2.5 times increase in energy distribution. The throughput of the routing techniques is shown in Figure 11. FLARoute records the highest throughput at 85%, followed by CCAF at 76.2%. PRoPHET and SnF are closely tied at 65.3% and 64.9%, respectively. Next is dLife and HERO, recording a throughput of 49.3% and 49%, respectively. Bubble Rap records the least throughput at 42.7%. FLARoute's improvement in fairness and throughput comes at a cost. As shown in Figure 12, FLARoute incurs 8.2 h of average delivery delay, which is a 49.1% increment of PRoPHET's. CCAF, on the other hand, is able to improve PRoPHET's delay by 9.1%. Figure 10: Energy distribution (in %, i.e., \(\alpha\times 100\)) of different routing techniques. Figure 9: Routing fairness (in %) of different routing techniques. As shown in Figure 13, FLARoute is able to reduce the transmission overhead of PRoPHET by 73.5%. This results in a 65.6% reduction of average energy consumption. A 90.7% reduction in transmission overhead and 89.2% reduction in average energy consumption is achieved with CCAF.