The widespread diffusion of connected smart devices has greatly contributed to the rapid expansion and evolution of the Internet at its edge, where personal mobile devices follow the behavior of their human users and interact with other smart objects located in the surroundings. In such a scenario, the user context is represented by a large variety of information that can rapidly change, and the ability of personal mobile devices to locally process this data is fundamental to make the system able to quickly adapt its behavior to the current situation. This ability, in practice, can be represented by a single elaboration process integrated in the final user application, or by a middleware platform aimed at implementing different context processing and reasoning to support third-party applications. However, the lack of public datasets that take into account the complexity of the user context in the mobile environment strongly limits the advance of the research in this field.

In this paper, we present MyDigitalFootprint, a novel large-scale dataset composed of smartphone embedded sensors data, physical proximity information, and Online Social Networks interactions aimed at supporting multimodal context-recognition and social relationships modeling. The dataset includes two months of measurements and information collected from the personal mobile devices of 31 volunteer users, in their natural environment, without limiting their usual behavior. Existing public datasets generally consist of a limited set of context data, aimed at optimizing specific application domains (human activity recognition is the most common example). On the contrary, our dataset contains a comprehensive set of information describing the user context in the mobile environment. In order to demonstrate the efficacy of the proposed dataset, we present three context-aware applications based on different machine learning tasks: (i) a social link prediction algorithm based on physical proximity data, (ii) the recognition of daily-life activities based on smartphone-embedded sensors data, and (iii) a pervasive context-aware recommender system. To the best of our knowledge, this is the first large-scale dataset containing such heterogeneity of information, representing an invaluable source of data to validate new research in mobile and edge computing.

互联智能设备的广泛普及，极大地促进了互联网在其边缘的快速扩展和发展，个人移动设备跟随其人类用户的行为并与周围其他智能对象进行交互。在这种情况下，用户上下文由可以快速更改的各种信息表示，个人移动设备本地处理此数据的能力是使系统能够快速使其行为适应当前情况的基础。实际上，此功能可以由集成在最终用户应用程序中的单个阐述过程来表示，也可以由旨在实现不同上下文处理和推理以支持第三方应用程序的中间件平台来表示。然而，

在本文中，我们提出了MyDigitalFootprint，这是一个新颖的大规模数据集，由智能手机嵌入式传感器数据，物理接近度信息和在线社交网络交互组成，旨在支持多模式上下文识别和社交关系建模。该数据集包括两个月的测量值和从31位志愿者用户的自然环境中的个人移动设备收集的信息，而没有限制他们的通常行为。现有的公共数据集通常由一组有限的上下文数据组成，旨在优化特定的应用程序域（人类活动识别是最常见的示例）。相反，我们的数据集包含一整套描述移动环境中用户上下文的信息。为了证明所提出的数据集的有效性，我们提出了三种基于不同机器学习任务的情境感知应用程序：（i）基于物理接近度数据的社交链接预测算法，（ii）基于嵌入智能手机的传感器数据来识别日常生活活动，以及（iii）普及的上下文感知推荐系统。据我们所知，这是第一个包含此类信息异质性的大规模数据集，代表了验证移动和边缘计算领域新研究的宝贵数据来源。