# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 3 Geo-DMP Mobile System
### The Routing Scheme Based on Map Segmentation and Movement History  
Based on the map segmentation presented in the previous section, we designed a straightforward routing scheme, yet it is adequate for validation and testing purposes. The design is partly inherited from our former work [109], which takes advantages of square regions to capture the movement patterns of people and take them as a basis to predict contact chances in the near future. Since we primarily focus on the retrieval of spatial-related contents, which are commonly location-dependent, a node that is more probable to pass the target location can be regarded as a better carrier, so we will keep going along this direction. Compared with [109], while preserving the basic idea, we make major changes on three aspects:  
* We use realistic administrative regions originated from map segmentation instead of simple square regions to characterize the movement of nodes. * The emphasis of routing design is transited from EID-based (identifier of nodes) to region-based as we do not intend to contact certain nodes, but to expect to acquire demanded contents along the path to certain locations. * Besides regular bundles, we also support BPQ-enabled request and response bundles to achieve bi-directional, name-based, and on-demand content retrieval. Accompanying the movement, the node obtains its current GPS position at a periodic interval of 5 s. By querying its position through the map interface, regions where the node lies are obtained. Thereby, we project the detailed trajectory onto a sequence of subregions, which is more friendly for comparison and maintenance. The local recorded history of passed regions is not merely for self use. Upon a link establishment, two nodes will exchange this information in the form of a normal bundle. A node can only learn the history regions of its direct one-hop neighbors, and such knowledge of a certain neighbor will be updated to the latest state only when that neighbor is encountered again. Correspondingly, we add new fields indicating the source and target regions of the bundle in the bundle header. As we partition the map into multiple layers with different granularities, the region on every layer that encompasses the given location is recorded. The source regions are those from which the bundle originates. To be more specific, for query bundles, they are where the request is issued, while for a response bundle, they are where the successful content matching happens. Beyond use in forwarding, this information can reflect the spatial distribution of content supply and demand to some degree. On the other side, regions for which the bundle is destined are target regions. Potential candidates of target regions include, but are not limited to: publicly known locations of road side unit (RSU), possible regions where contents could be found indicated by received response bundles, or locations that are highly correlated with the demanded contents (e.g., updates on traffic or weather condition, or photographs and short videos from the scene of an incident). With designated target regions and collected regional movement history, the routing strategy will make efforts to take bundles to gradually approach their destinations by selecting the next hops that have get closer to the finer target regions. With the movement trajectories represented by a sequence of regions, the mobility pattern can be learned. However, how to accurately identify the regularity of mobile nodes is also a sophisticated research problem. There are numerous works that investigate this problem with respect to frequency, recency, or optimality, etc. Due to its complexity, this problem is beyond the scope of this paper. Here we put an emphasis on showing the ability of Geo-DMP on the support of region-based routing strategy. If a better mobility pattern can be derived via analysis of history regions, it can be applied to Geo-DMP as well.