Towards Human-Aware D2D Communication

Abstract

Mobility, social interactions, and other human characteristics shall support Future Mobile Networks in routine prediction and resource management. This work investigates human-aware metrics supporting services or protocols leveraging opportunistic communication. These metrics represent different types of knowledge extracted from people routine present in their movements. Because of the strong routine component of human mobility, such metrics capture different but recurrent behaviors on wireless encounters between mobile users. We report the experience through a case study with a real-world dataset along with results from trace and metrics analysis. The results show heterogeneity in metric coefficients and contact occurrence and duration in different periods of the day, highlighting the need for characterising traces before their use.