Enabling Efficient Offline Mobile Access to Online Social Media on Urban Underground Metro Systems

• Published in: IEEE transactions on intelligent transportation systems 2020-07, Vol.21 (7), p.2750-2764
• Main Authors: Wu, Di, Lambrinos, Lambros, Przepiorka, Thomas, Arkhipov, Dmitri I., Liu, Qiang, Regan, Amelia C., McCann, Julie A.
• Format: Article
• Language: English
• Subjects: Ambient intelligence; Availability; Bandwidths; Caching; Connectivity; Context-aware computing; Context-aware services; Digital media; Electronic devices; Internet; mobile crowdsourcing; Mobile handsets; Network latency; opportunistic networking; Optimization; Performance evaluation; Prefetching; Social networking (online); Social networks; Subways; Urban areas; Wireless networks
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2019.2911624

In many parts of the world, passengers traveling on underground metro systems do not enjoy uninterrupted Internet connectivity. This results in passenger frustration since during such trips the access of online social media services is a highly popular activity. Being the world's oldest underground metro system, London's underground is a typical transportation environment, where the Internet connectivity is often not available during journeys which predominantly take place underground along sub-surface and deep-level track lines. To alleviate the absence of continuous connectivity, we designed DeepOpp, a context-aware mobile system that facilitates offline access to online social media content. The DeepOpp operates efficiently due to its opportunistic approach: it executes content prefetching and caching operations when adequate urban 3G or WiFi signal is detected. The functionality of DeepOpp includes the crowdsourcing of measurements of signal characteristics (strength, bandwidth availability, and latency) which are subsequently used in predicting mobile network signal coverage and initiating data prefetching operations. During data prefetching, an optimization scheme selectively specifies the social media content to be cached based on current network conditions and device storage availability. We implemented DeepOpp as an Android application which we trialled during real trips on the London underground. Our evaluations show that the DeepOpp offers significant reduction when compared with existing approaches in terms of power usage and the volume of data downloaded. Even though we only tested DeepOpp in the London underground metro system, its feature set makes it readily applicable in similar underground metro systems (in cities like New York, Paris, and Shanghai) as well as in situations, where mobile device users suffer from significant connectivity interruptions.