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DeepOpp: Context-aware Mobile Access to Social Media Content on Underground Metro Systems
Di Wu, Dmitri Arkhipov, Thomas Przepiorka, Qiang Liu, Julie McCann and Amelia Regan
Hunan University & Imperial College London, University of California Irvine, Imperial College London, Dartmouth College, Imperial College London, University of California Irvine

Accessing online social media content on underground metro systems is a challenge due to the fact that passengers often lose connectivity for large parts of their commute. As the oldest metro system in the world, the London underground represents a typical transportation network with intermittent Internet connectivity. To deal with disruption in connectivity along the sub-surface and deep-level underground lines on the London underground, we have designed a context-aware mobile system called DeepOpp. DeepOpp enables efficient offline access to online social media by prefetching and caching content opportunistically when signal availability (e.g. urban 3Gand stationWiFi) is detected.DeepOpp canmeasure, crowdsource and predict signal characteristics such as strength, bandwidth and latency; it can use these predictions of mobile network signal to activate prefetching, and then employ an optimization routine to determine which social content should be cached in the system given real-time network conditions and device capacities. DeepOpp has been implemented as an Android application and tested on the London Underground; it shows significant improvement over existing approaches, e.g. reducing the amount of power needed to prefetch socialmedia items by 2.5 times.While we use the London Underground to test our system, it is equally applicable in New York, Paris,Madrid, Shanghai, or any other urban underground metro system, or indeed in any situation in which users experience long breaks in connectivity.