## 2 Problem Background
### Existing OppNet Routing Protocols for Sensed Data Collection
#### 2.2.2 OppNet Routing Protocols in Sensed Data Collection Scenarios  
In this section, we perform simulation experiments with different routing protocols in order to investigate how existing routing techniques perform under different degrees of spatial locality. The protocols are Epidemic, PRoPHET, dLife, Bubble Rap, SnF and the Home Based Routing (HERO) algorithm [52], which divides the geographical area of the network into regions, and forwards messages based on the history of region visits. Our method of obtaining different degrees of spatial locality is by generating 3 different scales of the Helsinki scenario to depict relatively low, medium and high degrees of spatial locality. In order to achieve this, it is important that the resulting 3 scenarios are formed without changing essential encounter characteristics. Therefore, related factors such as node population and geographic area need to be varied proportionally. Another related factor is the distance between a source-destination pair. This is because the placement of the source and destination nodes impacts on network performance, especially when the distance between them is longer. Since the distance between source-destination pairs would be different for each of the 3 scenarios, it is very unlikely to maintain the same proportion across the 3 scenarios through manual selection and placement. In order to avoid bias, we utilize the \"MessageEventGenerator\" class in the Opportunistic Network Environment (ONE) simulator [53], which randomly selects the source and destination of each message based on a given creation interval, message size range and range of source/destination addresses. To generate the three scenarios, the Helsinki simulation area in the ONE simulator is modified to obtain the three movement scenarios shown in Figure 1. The Helsinki simulation area is used to represent the medium scenario, the small scenario is represented by one of the four main districts in the medium scenario and the large scenario is artificially generated from the combination of 4 copies of the medium scenario. The scenarios, namely small, medium and large, correspond to low, medium and high degree of spatial locality, respectively. We vary the number of nodes for each scenario proportionally in order to leave the essential encounter characteristics unchanged. We assume an OppNet formed by mobile users in a city using smartphones or similar handheld devices equipped with GPS and a Bluetooth interface with transmission speed and range of 2 MBps and 10 meters, respectively. The free buffer space for routing-related tasks is limited to 10 MB, as users may not be willing to spend most of their storage space on behalf of the network. Users move according to the Working Day Movement (WDM) model [54]. Since WDM models daily movement routines in working days, a total simulation duration of 5 days is chosen, i.e., 1 day for warm-up (to ensure a complete generation of encounter and location history and community formation), 3 days for message generation and routing, and 1 day for cool-down (to allow every message delivery attempt). A message is generated every 10 to 15 min from a randomly chosen node (the source) to another randomly chosen node (the destination). The TTL parameter for each message is set to 24 h, as we intend for messages to be delivered during a 1-day period. Sensor data size and generation rates used in related studies (e.g., 36 bytes every 100 s, i.e., 1.296 KB per hour [16] and 30 bytes every 5 s, i.e., 21.6 KB per hour [55]) indicate that this choice depends on the sensor application. Our goal is not to account for the exact amount of burden, but to show that unfairness exists and how fair routing proposals address it. Hence, for simulating sensor applications involving delay-tolerant data, the message size is uniformly distributed between 10 KB and 15 KB. Energy consumption for Bluetooth is according to the configuration settings in the module proposed by [PERSON] et al. [56].