# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 3 A Model for Contact Information Overhead
### Need for Algorithms that Reduce Contact Information Overhead  
One of the challenges facing OppNet research for emerging IoT scenarios is the difficulty in obtaining rich datasets that portray human movement in urban scenarios. For instance, a campus scenario may include thousands of portable handheld devices on people (e.g., students, lecturers and buses) and endpoints (e.g., vehicles and access points) more densely packed and moving into transmission range more often than depicted in existing datasets. The impact of certain overheads may go unnoticed in a small and highly dense population--for instance, the overhead each node incurs due to flooding messages in a small population may not be significant when compared with a larger population. This is also the case for a large population with low density, since nodes may not encounter each other frequently enough. Hence, for the purpose of evaluating OppNet solutions in such scenarios, it may be more realistic to study human movement properties in real-world traces and reproduce them in synthetic movement models, until rich city-wide datasets are available. At the same time, researchers need to have this in mind while setting up synthetic movement scenarios.  
The need to reduce contact information overhead is acknowledged only when the node population and dynamicity of the utilized movement scenario begins to resemble that of real-world urban environments. Contact information size raises two main issues with regard to routing performance. The first issue is regarding the portion of the encounter duration the process of exchanging summary vectors occupies. Considering the operations involved in neighbor discovery and link set up, contact information needs to be small enough to fully utilize the typically short encounter durations. The second issue is that of energy consumed in exchanging contact information. Since there is no guarantee that users will always be willing to shed all the available energy on their devices for the sake of the technology, the logical direction for OppNets is to minimize energy consumption.  
Clearly, the size of contact information in summary vectors needs to be minimized as much as possible. The optimization provided by the Delay Tolerant Networking Research Group in the latest version of PRoPHET's specification [58] suggests that contact information for nodes with delivery predictabilities less than a certain threshold be removed. However, the threshold requires careful selection for each node such that it has to be less than delivery predictability values usually present in the network for destinations for which the node is a forwarder. The authors also suggest that the threshold could be calculated based on delivery predictability ranges and the amount they change historically. Certainly, determining this threshold introduces additional complexities whose impact on performance the authors are yet to investigate. Until then, forwarding algorithms that can guarantee equal or acceptable throughput with less contact information are key, and are the subject of the next section.