Context modeling and recognition represent complex tasks that allow mobile and ubiquitous computing applications to adapt to the user's situation. Current solutions mainly focus on limited context information generally processed on centralized architectures, potentially exposing users' personal data to privacy leakage, and missing personalization features. For these reasons on-device context modeling and recognition represent the current research trend in this area. Among the different information characterizing the user's context in mobile environments, social interactions and visited locations remarkably contribute to the characterization of daily life scenarios. In this paper we propose a novel, unsupervised and lightweight approach to model the user's social context and her locations based on ego networks directly on the user mobile device. Relying on this model, the system is able to extract high-level and semantic-rich context features from smartphone-embedded sensors data. Specifically, for the social context it exploits data related to both physical and cyber social interactions among users and their devices. As far as location context is concerned, we assume that it is more relevant to model the familiarity degree of a specific location for the user's context than the raw location data, both in terms of GPS coordinates and proximity devices. By using 5 real-world datasets, we assess the structure of the social and location ego networks, we provide a semantic evaluation of the proposed models and a complexity evaluation in terms of mobile computing performance. Finally, we demonstrate the relevance of the extracted features by showing the performance of 3 machine learning algorithms to recognize daily-life situations, obtaining an improvement of 3% of AUROC, 9% of Precision, and 5% in terms of Recall with respect to use only features related to physical context.

翻译：情境建模与识别是实现移动与普适计算应用适应用户情境的复杂任务。当前解决方案主要聚焦于有限的情境信息，通常采用集中式架构处理，这可能导致用户个人数据面临隐私泄露风险，且缺乏个性化特征。基于上述原因，端侧情境建模与识别已成为该领域当前的研究趋势。在移动环境中表征用户情境的不同信息中，社交互动与访问地点对日常生活场景的刻画具有显著贡献。本文提出了一种新颖、无监督且轻量级的方法，直接在用户移动设备上基于自我网络对用户社交情境及其位置进行建模。依托该模型，系统能够从智能手机嵌入式传感器数据中提取高层次的语义丰富情境特征。具体而言，针对社交情境，该方法同时利用用户及其设备间的物理与网络社交互动数据。就位置情境而言，我们认为对于用户情境而言，对特定位置的熟悉程度进行建模比原始位置数据（包括GPS坐标与邻近设备信息）更具相关性。通过使用5个真实世界数据集，我们评估了社交与位置自我网络的结构，对提出模型进行了语义评估，并从移动计算性能角度进行了复杂度评估。最后，我们通过展示3种机器学习算法在识别日常生活情境中的表现，论证了所提取特征的相关性：与仅使用物理情境相关特征相比，在AUROC、精确率与召回率上分别获得了3%、9%与5%的提升。