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：情境建模与识别是使移动及普适计算应用能够适应用户所处场景的复杂任务。现有解决方案主要关注基于集中式架构处理的有限情境信息，这可能导致用户个人数据泄露风险，且缺乏个性化功能。因此，移动端情境建模与识别已成为该领域当前的研究趋势。在移动环境下表征用户情境的各类信息中，社交互动与访问地点对刻画日常生活场景具有显著贡献。本文提出一种新颖、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级且语义丰富的情境特征。具体而言，对于社交情境，本方法利用了用户及其设备之间物理与网络社交交互的相关数据。针对位置情境，我们认为，相较于原始位置数据（包括GPS坐标与邻近设备），建模特定位置对用户情境的熟悉程度更具相关性。通过使用5个真实数据集，我们评估了社交与位置自我网络的结构，对提出的模型进行了语义评估，并从移动计算性能角度开展复杂度评估。最后，我们通过展示3种机器学习算法识别日常生活场景的性能，论证了所提取特征的相关性：相较于仅使用物理情境相关特征，AUROC提升3%、精确率提升9%、召回率提升5%。