GAnGS: gather, authenticate 'n group securely
Proceedings of the 14th ACM international conference on Mobile computing and networking
Proceedings of the 6th ACM conference on Embedded network sensor systems
Seeing-Is-Believing: using camera phones for human-verifiable authentication
International Journal of Security and Networks
SPATE: small-group PKI-less authenticated trust establishment
Proceedings of the 7th international conference on Mobile systems, applications, and services
PROTECT: proximity-based trust-advisor using encounters for mobile societies
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
EmotionSense: a mobile phones based adaptive platform for experimental social psychology research
Proceedings of the 12th ACM international conference on Ubiquitous computing
A survey of mobile phone sensing
IEEE Communications Magazine
Non-cryptographic authentication and identification in wireless networks
IEEE Wireless Communications
Self-policing mobile ad hoc networks by reputation systems
IEEE Communications Magazine
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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 potentially-strong new connections. We propose a set of 4 filters to rate and rank mobile encounters identifying users with similar behavior. We have developed and deployed ConnectEnc application on Android and Nokia N810 platform to measure the link between the scores of proposed filters and the existence (or lack) of social relationship with the rated devices. We find that a statistically strong relationship exists between our recommendation and social relationship with the devices rated by the users (for LVC, r=0.84, p With this similarity based trustworthy node discovery, 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.