Computer and Information Science and Engineering 

Many future mobile services and applications will center on the social and community aspects of mobile societies. Interactions and connections between users in mobile networks are usually subject to the strength of the connections between the nodes, informed by historical events. This study proposes, implements and evaluates novel methods to dynamically measure the strength of social connections and similarity based on historical mobility behavior and encounter information. Through our protocol and application we investigate the feasibility of discovering known encountered devices, in addition to the opportunistic identification of potential-strong new connections. We introduce a family of encounter-based trust adviser filters that processes encounter statistics and location preferences. The evaluation is conducted over three phases. First, through statistical, stability and graph analyses we find that several filters possess high stability, and that trust forms a small world among trusting users. Resilience to attacks using anomaly detection achieves less than 10% false positives and 7% false negatives. Second, through selfishness analysis using trusted epidemic routing, we find that it is possible to efficiently use meaningful, stable trust routing without sacrificing network performance in DTNs. Third, through a series of surveys and participatory experiments, we find users' willingness to trust other devices to be highly correlated with behavioral similarity. User feedback from using extit{ConnectEnc} app, shows that statistically strong correlation exists between the similarity scores generated by the filters and the selection of trusted devices by the user. Proposed filters are able to capture 80% of the already known user within top 25% of the encountered users. Thus extit{ConnectEnc} is able to capture socially similar users with good success. With this trust, several potential applications can be enabled including mobile social networking, building groups and communities of interest, localized alert and emergency notification, context-aware and similarity-based networking.