Enabling Human-Aware D2D Communication Strategies Through Data Extraction and Analysis
Abstract
Real Datasets can reveal user characteristics such as mobility, social interactions, and others that will support Future Mobile Networks in routines prediction and resource management. This work introduces a framework with practices for user-data extraction and manipulation, and proposes human-aware metrics to support a novel opportunistic communication strategy. The experience is reported through a study case with Macaco Dataset and results from trace and metrics analysis, showing the importance of human-behavior based decision factors for future networking solutions.
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