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Reactive programming optimizations in pervasive computing

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Reactive programming optimizations in pervasive computing. / Chen, C.; Xu, Y.; Li, K. et al.
2010 10th Annual International Symposium on Applications and the Internet, SAINT 2010. IEEE, 2010. p. 96-104.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Chen, C, Xu, Y, Li, K & Helal, S 2010, Reactive programming optimizations in pervasive computing. in 2010 10th Annual International Symposium on Applications and the Internet, SAINT 2010. IEEE, pp. 96-104. https://doi.org/10.1109/SAINT.2010.92

APA

Chen, C., Xu, Y., Li, K., & Helal, S. (2010). Reactive programming optimizations in pervasive computing. In 2010 10th Annual International Symposium on Applications and the Internet, SAINT 2010 (pp. 96-104). IEEE. https://doi.org/10.1109/SAINT.2010.92

Vancouver

Chen C, Xu Y, Li K, Helal S. Reactive programming optimizations in pervasive computing. In 2010 10th Annual International Symposium on Applications and the Internet, SAINT 2010. IEEE. 2010. p. 96-104 doi: 10.1109/SAINT.2010.92

Author

Chen, C. ; Xu, Y. ; Li, K. et al. / Reactive programming optimizations in pervasive computing. 2010 10th Annual International Symposium on Applications and the Internet, SAINT 2010. IEEE, 2010. pp. 96-104

Bibtex

@inproceedings{fc370e18b65a47a693883811a7662f02,
title = "Reactive programming optimizations in pervasive computing",
abstract = "Pervasive computing systems are begging for programming models and methodologies specifically suited to the particular cyber-physical nature of these systems. Reactive (rule-based) programming is an attractive model to use due to its built-in safety features and intuitive application development style. Without careful optimization however, reactive programming engines could turn into monstrous power drains of the pervasive system and its sensor network. In this paper we propose two optimizations for reactivity engines. The first, which we prove to be optimal, assumes all sensors in the space are equally important to the application. The other, which is adaptive, employs and estimates a probability for each sensor based on application usage. Both optimizations use a mixed push/pull approach to achieve optimal or near optimal energy efficiency. We present an experimental evaluation of the two algorithms to quantify their performance over a range of parameters. {\textcopyright} 2010 IEEE.",
keywords = "Optimization, Performance, Programming models in pervasive spaces, Reactivity engines, Rule based processing, Application development, Experimental evaluation, Pervasive computing, Pervasive computing systems, Pervasive systems, Physical nature, Programming models, Reactive programming, Rule based, Safety features, Energy efficiency, Internet, Models, Sensors, Ubiquitous computing",
author = "C. Chen and Y. Xu and K. Li and Sumi Helal",
year = "2010",
doi = "10.1109/SAINT.2010.92",
language = "English",
isbn = "9781424475261",
pages = "96--104",
booktitle = "2010 10th Annual International Symposium on Applications and the Internet, SAINT 2010",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Reactive programming optimizations in pervasive computing

AU - Chen, C.

AU - Xu, Y.

AU - Li, K.

AU - Helal, Sumi

PY - 2010

Y1 - 2010

N2 - Pervasive computing systems are begging for programming models and methodologies specifically suited to the particular cyber-physical nature of these systems. Reactive (rule-based) programming is an attractive model to use due to its built-in safety features and intuitive application development style. Without careful optimization however, reactive programming engines could turn into monstrous power drains of the pervasive system and its sensor network. In this paper we propose two optimizations for reactivity engines. The first, which we prove to be optimal, assumes all sensors in the space are equally important to the application. The other, which is adaptive, employs and estimates a probability for each sensor based on application usage. Both optimizations use a mixed push/pull approach to achieve optimal or near optimal energy efficiency. We present an experimental evaluation of the two algorithms to quantify their performance over a range of parameters. © 2010 IEEE.

AB - Pervasive computing systems are begging for programming models and methodologies specifically suited to the particular cyber-physical nature of these systems. Reactive (rule-based) programming is an attractive model to use due to its built-in safety features and intuitive application development style. Without careful optimization however, reactive programming engines could turn into monstrous power drains of the pervasive system and its sensor network. In this paper we propose two optimizations for reactivity engines. The first, which we prove to be optimal, assumes all sensors in the space are equally important to the application. The other, which is adaptive, employs and estimates a probability for each sensor based on application usage. Both optimizations use a mixed push/pull approach to achieve optimal or near optimal energy efficiency. We present an experimental evaluation of the two algorithms to quantify their performance over a range of parameters. © 2010 IEEE.

KW - Optimization

KW - Performance

KW - Programming models in pervasive spaces

KW - Reactivity engines

KW - Rule based processing

KW - Application development

KW - Experimental evaluation

KW - Pervasive computing

KW - Pervasive computing systems

KW - Pervasive systems

KW - Physical nature

KW - Programming models

KW - Reactive programming

KW - Rule based

KW - Safety features

KW - Energy efficiency

KW - Internet

KW - Models

KW - Sensors

KW - Ubiquitous computing

U2 - 10.1109/SAINT.2010.92

DO - 10.1109/SAINT.2010.92

M3 - Conference contribution/Paper

SN - 9781424475261

SP - 96

EP - 104

BT - 2010 10th Annual International Symposium on Applications and the Internet, SAINT 2010

PB - IEEE

ER -