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.

翻译：智能连接设备的广泛普及推动了互联网在边缘侧的快速扩展与演进。个人移动设备与周围其他智能物体交互，根据快速变化的用户情境调整行为。移动设备本地处理这些数据的能力对于快速适应至关重要。这可通过集成到用户应用中的单一处理流程或用于情境处理的中间件平台实现。然而，缺乏考虑移动环境中用户情境复杂性的公共数据集阻碍了研究进展。我们提出MyDigitalFootprint，这是一个包含智能手机传感器数据、物理邻近信息及在线社交网络交互的大规模数据集。该数据集支持多模态情境识别与社会关系建模，涵盖31名志愿者用户在自然环境中为期两个月的测量数据，允许不受限制的行为记录。现有公共数据集侧重于特定应用的有限情境数据，而我们的数据集提供了移动环境中用户情境的全面信息。为展示该数据集的有效性，我们提出了三种利用不同机器学习任务的情境感知应用：（i）基于物理邻近数据的社交链接预测算法，（ii）利用智能手机嵌入式传感器数据的日常生活活动识别，以及（iii）普适情境感知推荐系统。凭借其信息的异构性，我们的数据集可作为验证移动与边缘计算领域新研究的宝贵资源。