# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 5 Evaluation
### Impact of Contact Information Size on Message Forwarding
#### 5.2.2 Routing Performance of PoiFord  
With less contact information maintained on nodes, PoiFord is able to improve overall network performance significantly. To investigate how PoiFord improves routing performance, we run two versions of the simulation for PRoPHET and PoiFord. The first version of the simulation namely \"without SV\" did not implement summary vectors, so that whenever two nodes encounter each other, they obtain the necessary encounter-based knowledge directly from the simulator's internal memory. This is how the current version of PRoPHET and most encounter-based routing protocols are implemented in ONE simulator--they abstract the process of exchanging summary vectors so a node can access the routing information stored in another node without receiving or sending summary vectors. For the second version of the simulation, namely \"with SV\", we implement the process of exchanging summary vectors, so that whenever two nodes encounter each other, they actually generate and exchange new messages to which they attach a message vector and a contact vector (as stated earlier in Section 2.1). Hence, message forwarding takes place only after a summary vector has been successfully received. The results are recorded in Figure 10. \"No SV\" indicates that HERO does not require summary vectors to be exchanged between encountered nodes. Without implementing summary vectors, incorporating PoiFord into PRoPHET increases throughput by about 16.9% and reduces average delivery delay by about 16.4%. PoiFord is aware of a major and often overlooked factor, which is the adverse effects of spatial locality. Spatial locality increases with the geographical area of the network in the sense that most nodes travel relatively short trips and have high preference to a local area. This reduces the encounter opportunities between sets of nodes that reside in different geographical areas. In that case, finding suitable relays with forwarding utilities based on only direct encounter information may be challenging. Unfortunately, as mentioned earlier, the solution provided by transitive properties (such as PRoPHET's) may result in excess transmissions. With locality awareness from utilizing location-based knowledge, PoiFord is able to find more suitable relay nodes even in high degrees of spatial locality, thereby delivering more messages in less time. HERO, on the other hand, records the least throughput and highest average delivery delay due to the absence of any encounter-based or social-based knowledge. With the implementation of summary vectors, PoiFord further increases throughput by 23.9%. This indicates that in addition to locality awareness, PoiFord's ability to reduce contact information  
Figure 9: Percentage of contact information transfer attempts that were successful and energy consumed for transferring contact information in a day. overhead also contributes to increased throughput when summary vectors are considered--which is a more realistic case. With smaller summary vectors, nodes running on PoiFord are able to forward more messages during each encounter opportunity. As a result, less messages are lost and throughput is increased at the cost of slightly (3.7%) higher average delivery delay, since the metric accounts for only delivered messages. It can also be observed from Figure 10 that HERO records a higher throughput than PRoPHET in this scenario. This shows that results obtained without actually implementing summary vector exchange could be exaggerating--this is also observed in Figure 11. This practice may provide misleading results especially when the performance of different routing protocols is being compared. Neglecting summary vector exchange does not only affect the comparison with location-based forwarding techniques (e.g., comparing the performance of PRoPHET and HERO), but also the comparison with other types of forwarding approaches (e.g., PRoPHET and PoiFord).