Macro programming through Bayesian networks: Distributed inference and anomaly detection

被引：7

作者：

Mamei, Marco ^{[1
]}

Nagpal, Radhika ^{[2
]}

机构：

[1] Univ Modena, DISMI, Via Amendola 2, Reggio Emilia, Italy

[2] Harvard Univ, EECS, Cambridge, MA 02138 USA

来源：

FIFTH ANNUAL IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS, PROCEEDINGS | 2007年

关键词：

D O I：

10.1109/PERCOM.2007.19

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Macro programming a distributed system, such as a sensor network, is the ability to specify application tasks at a global level while relying on compiler-like software to translate the global tasks into the individual component activities. Bayesian networks can be regarded as a powerful tool for macro programming a distributed system in a variety of data analysis applications. In this paper we present our architecture to program a sensor network by means of Bayesian networks. We also present some applications developed on a microphone-sensor network, that demonstrate calibration, classification and anomaly detection.

引用

页码：87 / +

页数：2

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