Sanchari is currently working on bridging the gap between user-defined intents and the underlying device interactions in Internet-of-Things (IoT) environments, under Prof. Anduo Wang at Temple University.
Her research aims to provide a systematic approach to address these challenges by exploring new methods for identifying conflicting policies while meeting user intent. This work involves a literature review to assess the effectiveness of existing approaches.
Additionally, she is proposing a scalable representation of a smart home system that leverages data integration mechanisms to define proper abstractions and query answering. This representation aims to provide insights into the interactions between seemingly disparate IoT applications.
Overall, her research parallels the development of software-defined networking in managing heterogeneous network devices. Starting with a unified lamp control in a room, the research progressively incorporates complexity to encompass multiple devices and intents, reflecting user intents and uncovering conflicting policies across applications at various levels of a hierarchical structure to ensure the optimal operation of smart home environments.
RAVEL: In the battle with system complexity, if knowledge representation and logic reasoning give a deep answer, does networking make a profitable problem? This project (supported by the National Science Foundation Award CNS-1909450 and CNS 1657285) explores the pain points in today’s network analysis — such as unavailable information, uncertain environments, and non-monotonic dynamic behaviors — to motivate our initial conjectures to this question.