Scalable-Application Design for the IoT
| Autor: | Tajana Rosing, Alper Sinan Akyurek, Jagannathan Venkatesh, Christine S. Chan, Baris Aksanli |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry Distributed computing 020208 electrical & electronic engineering Software development 020206 networking & telecommunications Context (language use) 02 engineering and technology Smart grid Software deployment Scalability 0202 electrical engineering electronic engineering information engineering Redundancy (engineering) Context awareness Overhead (computing) The Internet business Software |
| Zdroj: | IEEE Software. 34:62-70 |
| ISSN: | 1937-4194 0740-7459 |
| DOI: | 10.1109/ms.2017.4 |
| Popis: | The Internet of Things envisions a web-connected infrastructure of sensing 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 deployment. A proposed approach breaks these applications up into an equivalent set of functional units called context engines, whose I/O transformations are driven by general-purpose machine learning. This approach decreases computational redundancy and complexity with a minimal impact on accuracy. Researchers evaluated this approach's scalability--how the context engines' overhead grows as the input data and number of computational nodes increase. In a large-scale case study of residential smart-grid control, this approach provided better accuracy and scaling than the state-of-the-art single-stage approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|