Semantic based networking of information in vehicular clouds based on dimensionality reduction

Transport of information in vehicular clouds faces challenges due to intermittent connectivity and the fact that the already existing Internet protocol based transport solutions do not exploit the semantics of information to utilize the available contextual information. With the advent of Internet of Things and Machine to Machine communications, availability of contextual information through the wisdom of the crowd and ubiquity of sensors and devices calls for a shift towards networking of information beyond Internet Protocol (IP) level connectivity. We propose a novel approach for forwarding and discarding policy that can be utilized by content aware network elements. The proposed method makes use of multidimensional scaling techniques that leverages the spectral characteristics of information predicates. By evaluations and analysis we show that by considering the networking of information paradigm for vehicular clouds our proposed clustering technique yields a lower processing cost and complexity as the system scales.