DeepOpp: Facilitating Mobile Access to Social Media Content on Urban Underground Metro Systems


London Underground carries millions of workers, tourists and students every day, and Londoners spend an average of 45 minutes a day on it. Its trains complete over two billion journeys each year, travelling through tunnels deep below ground as well as tunnels on or near the surface that navigate under roads, parks, and even rivers. These conditions make access to social media while relying on existing mobile networking infrastructures extremely difficult. On sub-surface lines with intermittent signal coverage, passengers have trouble knowing when they can use their phones; they may repeatedly attempt to access content and will often give up trying whereas on deep-level lines passengers assume they have no access to content at all. Besides the Underground, other metropolitan rapid transit systems such as the Paris Metro, the New York City Subway and in some areas the subways in China face similar issues. Therefore, efficient solutions are needed for mobile access to social media in underground environments. 

Opportunistic mobile data prefetching has been proposed as a potential solution for providing access to content when no connectivity is available while also helping to reduce energy consumption by only accessing data at times of high signal strength. Most of the existing content prefetching solutions base their operations on network conditions. 

However, prefetching social media content to mobile clients has unique features, e.g. 1) mobile users have strong personal preferences about social media content; 2) a user typically only sees a subset of social media he subscribes to; and 3) freshness of social media is critical due to its time-sensitive nature.  Therefore the challenge of when, what and how to prefetch/access social media data are all tightly coupled. The goal is to exploit signal coverage, and fetch, cache, and make content available to a client application.To facilitate mobile connectivity for travellers using underground mass transit systems, DeepOpp system is designed. DeepOpp is a context-aware system that enables offline access to social media content. A mobile client running DeepOpp employs opportunistic content prefetching based on intermittent availability of connectivity opportunities (such as urban 3G or station WiFi),  to enable offline access to online social media. To achieve efficient prefetching of content on the resource-restricted mobile client, DeepOpp detects connectivity opportunities by learning from previous signal traces, and optimally chooses which social media items to cache based on the context of network condition, user preference and phone’s status.

DeepOpp can measure signal characteristics (strength, bandwidth and latency), and based on current and historical information make signal coverage predictions in order to activate data 

prefetching of social media content. An Android-based implementation of DeepOpp has enabled real testing on the London underground and demonstration of its benefits, which promote it as a viable solution for cities with similar metro systems.The prototype of the DeepOpp client application to support prefetching, caching and displaying of social media contents from Facebook, as a proof-of-concept, under intermittent signal availability on the Underground is implemented. The DeepOpp app also provides some controls that mobile user of the phone can manage. In the general settings, the user can opt to enable optimiser, download over 3G (need data plan support), and choose content to download (e.g. text, image and video). Once the optimiser is selected, the user can control specific optimiser thresholds for power, storage and data plan.

In the future, the system will be extended to operate for Instagram and Twitter social streams as well.  Another future plan is the definition and implementation of optimisation techniques. Thus, the mobile prefetching and caching operations will consider network conditions and user preferences to reduce smartphone resource consumption such as power and storage, as well as data plan usage.