## 2 Problem Background
### Existing OppNet Routing Protocols for Sensed Data Collection
#### 2.2.2 OppNet Routing Protocols in Sensed Data Collection Scenarios
The initial energy for nodes is 4800 Joules, while energy consumption for receiving or sending messages is set to 0.08 mW/s. Since our interest is only on energy consumed due to forwarding decisions made by different replication techniques, we consider receiving and sending messages as the only sources of energy consumption. Hence, scan energy (i.e., energy consumed from device discovery), scan response energy (i.e., energy consumed from device discovery response), and base energy (i.e., energy consumed in idle state) are all set to 0. Enough initial energy is provided to ensure that nodes do not fail as a result of battery depletion during the simulation. The parameters used for the simulation setup are shown in Table 1. Unless stated otherwise, experiments for each  
Figure 1: Movement scenarios used for evaluating unfairness in opportunistic network (OppNet) routing protocols (partitioned by a grid, the small, medium and large scenarios consist of 1 region, 4 regions and 16 regions, respectively). result are conducted 10 times with different random seeds, in order to present the average with a confidence interval of 95%. For the routing protocols, SnF uses binary spraying, and the value of \(L\) is taken as 4, 6 and 8, for the small, medium, and large scenario, respectively. This choice is based on values that yield best overall performance from several trials. Community formation and centrality computation for Bubble Rap is in accordance with the K-Clique and cumulative window algorithms given by [PERSON] et al. [45], while dLife considers 24 daily samples (i.e., 1 h each) as mentioned by [PERSON] et al. [57]. HERO maintains a routing table of 7 days with 24 periods each. Experimenting with the routing protocols, we observe that they all subject a few set of nodes to most of the routing duties. The results in Figure 2 show that only about 2% to 10% of the total node population partake in forwarding up to 25% of the entire number of messages generated in the network. For the purpose of evaluation, these nodes are termed \"central nodes\", while the remaining nodes are termed \"other nodes\". \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value(s)** \\ \hline Total simulation time (days) & 5 \\ Warm-up period (days) & 1 \\ Cool-down period (days) & 1 \\ Wireless communication interface & Bluetooth \\ Transmission range (m) & 10 \\ Transmission rate (MBps) & 2 \\ Buffer size (MB) & 10 \\ Message size (KB) & 10 to 15 \\ Message TTL (days) & 1 \\ Number of scenarios & 3 \\ Simulation area (km\({}^{2}\)) & 4.5 \(\times\) 3.4, 9 \(\times\) 6.8 and 18 \(\times\) 13.6 \\ Number of nodes & 19, 76 and 304 \\ Ave. message generation rate/node & 1 message every 10 to 15 min \\ Battery capacity (Joules) & 4800 \\ Receive/transmit energy (mW/s) & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters for the small, medium and large scenarios. Figure 2: The percentage of nodes that partake in forwarding up to 25% of the total number of messages in the network under low, medium, and high degrees of spatial locality. Fairness, on its own, would not be worth the effort (considering the adverse effect the introduction of fairness may have on throughput [19]), unless it reflects on the expenditure of some resource, in this case energy. Since popular nodes are more involved in routing, they also consume more energy. The idea is that, reducing the significant gap between the energy consumption on these set of nodes will increase user satisfaction [19]. So that for fair routing, the difference in energy consumption on neighbouring nodes should not be too large. Hence, we study the impact of unfairness on popular nodes from the perspective of energy consumed--which is the percentage of initial energy expended by the end of the simulation.