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Mobility and network traffic have been traditionally studied separately.Their interaction is vital for generations of future mobile services andeffective caching, but has not been studied in depth with real-world big data.In this paper, we characterize mobility encounters and study the correlationbetween encounters and web traffic profiles using large-scale datasets (30TB insize) of WiFi and NetFlow traces. The analysis quantifies these correlationsfor the first time, across spatio-temporal dimensions, for device types groupedinto on-the-go Flutes and sit-to-use Cellos. The results consistently show aclear relation between mobility encounters and traffic across differentbuildings over multiple days, with encountered pairs showing higher trafficsimilarity than non-encountered pairs, and long encounters being associatedwith the highest similarity. We also investigate the feasibility of learningencounters through web traffic profiles, with implications for disseminationprotocols, and contact tracing. This provides a compelling case to integrateboth mobility and web traffic dimensions in future models, not only at anindividual level, but also at pairwise and collective levels. We have releasedsamples of code and data used in this study on GitHub, to supportreproducibility and encourage further research(https://github.com/BabakAp/encounter-traffic).

On the Relation Between Mobile Encounters and Web Traffic Patterns: A Data-driven Study