A Semantic Geo-Tagged Multimedia-Based Routing in a Crowdsourced Big Data Environment

Traditional routing algorithms for calculating the fastest or shortest path become ineffective or difficult to use when both source and destination are dynamic or unknown. To solve the problem, we propose a novel semantic routing system that leverages geo-tagged rich crowdsourced multimedia information such as images, audio, video and text to add semantics to the conventional routing. Our proposed system includes a Semantic Multimedia Routing Algorithm (SMRA) that uses an indexed spatial big data environment to answer multimedia spatio-temporal queries in real-time. The results are customized to the users' smartphone bandwidth and resolution requirements. The system has been designed to be able to handle a very large number of multimedia spatio-temporal requests at any given moment. A proof of concept of the system will be demonstrated through two scenarios. These are 1) multimedia enhanced routing and 2) finding lost individuals in a large crowd using multimedia. We plan to test the system's performance and usability during Hajj 2015, where over four million pilgrims from all over the world gather to perform their rituals.