An Extensible Sensor based Inferencing Framework for Context Aware Applications

Development of modern context-aware applications requires the acquisition and interpretation of data from one or more sensors. This paper presents a framework designed to enable developers of such applications to focus on high level design and functionalities rather than spending time in low level implementation details. The framework enables this behavior and tight real-time control of an inferencing workload by representing it with a directed acyclic graph and by providing horizontal capabilities in order to adapt to a number of different usages. The main features enabled by the framework are reusability across algorithms, standard interfaces among them, improved efficiency through easy to use optimization techniques, and parallel processing of different workloads. We will show how the framework can be used to implement a number of disparate workloads that enable a range of context aware use cases, describe its implementation and finally discuss future work.