MyDigitalFootprint: an extensive context dataset for pervasive computing applications at the edge

TL;DR

MyDigitalFootprint is a large, comprehensive dataset of smartphone sensor data, social interactions, and proximity information collected over two months, enabling advanced research in mobile and edge computing applications.

Contribution

The paper introduces MyDigitalFootprint, a novel extensive dataset that captures complex user context data in natural environments, supporting diverse context-aware applications.

Findings

Effective social link prediction using proximity data

Accurate daily activity recognition from sensor data

Enhanced context-aware recommender system performance

Abstract

The widespread diffusion of connected smart devices has contributed to the rapid expansion and evolution of the Internet at its edge. Personal mobile devices interact with other smart objects in their surroundings, adapting behavior based on rapidly changing user context. The ability of mobile devices to process this data locally is crucial for quick adaptation. This can be achieved through a single elaboration process integrated into user applications or a middleware platform for context processing. However, the lack of public datasets considering user context complexity in the mobile environment hinders research progress. We introduce MyDigitalFootprint, a large-scale dataset comprising smartphone sensor data, physical proximity information, and Online Social Networks interactions. This dataset supports multimodal context recognition and social relationship modeling. It spans two months of measurements from 31 volunteer users in their natural environment, allowing for unrestricted behavior. Existing public datasets focus on limited context data for specific applications, while ours offers comprehensive information on the user context in the mobile environment. To demonstrate the dataset's effectiveness, we present three context-aware applications utilizing various machine learning tasks: (i) a social link prediction algorithm based on physical proximity data, (ii) daily-life activity recognition using smartphone-embedded sensors data, and (iii) a pervasive context-aware recommender system. Our dataset, with its heterogeneity of information, serves as a valuable resource to validate new research in mobile and edge computing.