## 4 Evaluation
### Quantifying the Burden on Nodes
#### 4.2.2 Performance Evaluation in the Helsinki Scenario  
The Helsinki scenario is simulated under five different rates of message generation in order to analyse how the routing proposals respond to changes in data traffic. The scenario consists of 600 nodes, which include 80 source nodes and 36 destination nodes that are placed in popular locations to represent sensors and gateways, respectively. Each source node generates messages to a randomly chosen destination node at the following rates: (i) 1 message every 3 h; (ii) 1 message every 2 h; (iii) 1 message every hour; (iv) 2 messages every hour; and (v) 3 messages every hour. Figure 22 shows that FLARoute achieves the highest energy distribution and fairness under the different rates of message generation. Figures 23-25 show how the routing proposals perform under increasing data traffic in terms of energy distribution, routing fairness, throughput, average delivery delay, transmission overhead, average energy consumption, and the energy consumption on the 5 most popular nodes. Finally, Figures 26 and 27 show results of performance evaluations under dynamic user behaviour and heterogeneous buffer capacity, respectively, with source nodes generating 1 message every hour. Figure 21: ReBurD’s relative burden against time for Node 114 and 177. Figure 22: Energy distribution and routing fairness under increasing data traffic in the Helsinki scenario. Figure 23: Throughput and average delivery delay under increasing data traffic in the Helsinki scenario. Figure 24: Transmission overhead and average energy consumption under increasing data traffic in the Helsinki scenario. Figure 25: Energy consumption (in Joules) on the 5 most popular nodes under increasing data traffic in the Helsinki scenario. Figure 26: Throughput and routing fairness with and without users withdrawing participation from the network. A. Throughput and Average Delivery Delay under Changing Data Traffic  
As shown in Figure 23, throughput drops with increasing data traffic. With PRoPHET, throughput drops by 18.4% from the least to highest data traffic. This goes as low as 27.7% with CCAF--the lowest drop in throughput as data traffic increases. This is due to the adopted routing approach, which retains messages in buffers until relays presenting much higher encounter-based forwarding utilities are encountered. Under high data traffic, messages are lost during the wait period due to TTL exhaustion on less popular nodes or buffer overflows on popular nodes. Throughput drops by only 3.6% with FLARoute, which is able to maintain a more stable throughput under increasing data traffic by utilizing a forwarding utility that allows more number of nodes to participate and contribute their resources to routing. FLARoute also achieves the highest throughput under the different data traffic. Figure 23 also shows that average delivery delay increases with data traffic. Since PRoPHET does not consider fairness, messages are forwarded to relay nodes with higher forwarding utilities irrespective of their burden. This way, messages are delivered through shorter routes and minimum delay is incurred, especially when data traffic is low. Under higher data traffic, however, more number of messages are introduced in the network, thereby resulting in higher buffer occupancy. Hence, more messages are delivered through longer routes due to message loss at popular nodes. Fair routing with CCAF spends more time waiting to encounter nodes with much higher forwarding utilities. Also, additional waiting time is incurred under higher data traffic since short-lived encounters may not be sufficient for popular nodes to complete their forwarding transactions. As compared with CCAF, FLARoute records less average delivery delay since a wider range of relay nodes are utilized. This shows that FLARoute recorded higher average delivery delay (and throughput) in the Skudai scenario due to its awareness of spatial locality (i.e., its ability to retain and deliver messages traversing multiple geographic regions).