# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 2 Problem Background  
Most pioneering work on OppNets focused on the point-to-point (or destination-based) communication model, in which endpoints are identified by their ID. Basically, message headers include destination identifiers in order to ensure delivery through suitable relays. In recent years, however, the increasing use of the Internet for sharing information has directed research efforts towards disseminating and retrieving content, rather than connecting node pairs. In information (or content) centric communication [30], the content, rather than the nodes involved, are named. Interested parties (or subscribers) request content by their names, and the network is tasked with locating the sources (or publishers) and routing the content to the receivers. Contributions have also emerged in the aspect of location-based information sharing, e.g., floating content [31], where content is associated with a particular geographic area. However, this destination-less communication model is more suitable for content-centric applications where data is disseminated based on user interest (or feedback as in ODD [32]). In order to be a feasible backhaul solution for UANs, available resources need to be utilized sparingly while guaranteeing acceptable throughput. In other words, OppNets require means of determining suitable relays (among the multitude of nodes in Smart City scenarios) that can route messages to their respective gateways within a given time-to-live (TTL), thereby making the destination-based approach a more suitable communication model. Different routing strategies following the destination-based communication model have been proposed over the years. There are routing strategies that do not require knowledge about the network (e.g., Epidemic [33]), thereby making them easy to implement. However, the tendency to consume excessive resources makes them unsuitable for sensed data collection. Utility-based protocols on the other hand, require knowledge about the network to select suitable relay nodes during message forwarding. The ability for a node to deliver a message is usually determined by computing its \"forwarding utility\" for the destination, such that a higher value indicates a higher contribution towards message delivery (i.e., more chances of encountering better relay nodes or the destination itself). Researchers adopt different strategies for computing forwarding utilities. There are strategies that predict future encounters between two nodes from the similarity of their movement patterns (e.g., MobySpace [34]). However, in sensed data collection, gateways nodes are static and do not exhibit similar movement patterns with suitable relay nodes. The delivery ability of nodes can also be determined from social characteristics (e.g., PeopleRank [35], Bubble Rap [36] and dLife [37]). Some protocols (such as CiPRO [38]) go a step further by utilizing contextual information--user information (e.g., email address, work and home address, occupation, mobility patterns, and communities they belong to) and device information (e.g., battery level and storage capacity)--to make forwarding decisions based on a profile match. However, the fact that gateway nodes neither portray social characteristics (e.g., belonging to social communities) nor possess enough contextual information limits the applicability of these strategies. Forwarding strategies can also be realized from encounter-based properties. Some strategies derive forwarding utilities from the number of encounters between nodes, so that messages are forwarded to nodes that encounter the destination (or its neighbors) more frequently (e.g., PRoPHET [39], I-PRoPHET [40] and ISW [41]). Others maintain a timer for each encountered node and determine the forwarding utility based on how recently the destination was encountered (e.g., Spray and Focus [42], TMS [43] and OPF [44]).