18 May 2017 Context-aware system design
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Abstract
The Internet of Things envisions a web-connected infrastructure of billions of sensors and actuation devices. However, the current state-of-the-art presents another reality: monolithic end-to-end applications tightly coupled to a limited set of sensors and actuators. Growing such applications with new devices or behaviors, or extending the existing infrastructure with new applications, involves redesign and redeployment. We instead propose a modular approach to these applications, breaking them into an equivalent set of functional units (context engines) whose input/output transformations are driven by general-purpose machine learning, demonstrating an improvement in compute redundancy and computational complexity with minimal impact on accuracy. In conjunction with formal data specifications, or ontologies, we can replace application-specific implementations with a composition of context engines that use common statistical learning to generate output, thus improving context reuse. We implement interconnected context-aware applications using our approach, extracting user context from sensors in both healthcare and grid applications. We compare our infrastructure to single-stage monolithic implementations with single-point communications between sensor nodes and the cloud servers, demonstrating a reduction in combined system energy by 22-45%, and multiplying the battery lifetime of power-constrained devices by at least 22x, with easy deployment across different architectures and devices.
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Christine S. Chan, Michael H. Ostertag, Alper Sinan Akyürek, Tajana Šimunić Rosing, "Context-aware system design", Proc. SPIE 10194, Micro- and Nanotechnology Sensors, Systems, and Applications IX, 101940B (18 May 2017); doi: 10.1117/12.2263232; https://doi.org/10.1117/12.2263232
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