Macro programming through bayesian networks: Distributed inference and anomaly detection

Marco Mamei; Radhika Nagpal

doi:10.1109/PERCOM.2007.19

Macro programming through bayesian networks: Distributed inference and anomaly detection

Marco Mamei, Radhika Nagpal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007
Publisher	IEEE Computer Society
Pages	87-93
Number of pages	7
ISBN (Print)	0769527876, 9780769527871
DOIs	https://doi.org/10.1109/PERCOM.2007.19
State	Published - 2007
Externally published	Yes
Event	5th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007 - White Plains, NY, United States Duration: Mar 19 2007 → Mar 23 2007

Publication series

Name	Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007

Conference

Conference	5th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007
Country/Territory	United States
City	White Plains, NY
Period	3/19/07 → 3/23/07

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Hardware and Architecture
Software

Access to Document

10.1109/PERCOM.2007.19

Cite this

Mamei, M., & Nagpal, R. (2007). Macro programming through bayesian networks: Distributed inference and anomaly detection. In Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007 (pp. 87-93). Article 4144753 (Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007). IEEE Computer Society. https://doi.org/10.1109/PERCOM.2007.19

Mamei, Marco ; Nagpal, Radhika. / Macro programming through bayesian networks : Distributed inference and anomaly detection. Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007. IEEE Computer Society, 2007. pp. 87-93 (Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007).

@inproceedings{be003e1e30494a069097290366d00859,

title = "Macro programming through bayesian networks: Distributed inference and anomaly detection",

abstract = "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.",

author = "Marco Mamei and Radhika Nagpal",

year = "2007",

doi = "10.1109/PERCOM.2007.19",

language = "English (US)",

isbn = "0769527876",

series = "Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007",

publisher = "IEEE Computer Society",

pages = "87--93",

booktitle = "Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007",

address = "United States",

note = "5th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007 ; Conference date: 19-03-2007 Through 23-03-2007",

}

Mamei, M & Nagpal, R 2007, Macro programming through bayesian networks: Distributed inference and anomaly detection. in Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007., 4144753, Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007, IEEE Computer Society, pp. 87-93, 5th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007, White Plains, NY, United States, 3/19/07. https://doi.org/10.1109/PERCOM.2007.19

Macro programming through bayesian networks: Distributed inference and anomaly detection. / Mamei, Marco; Nagpal, Radhika.
Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007. IEEE Computer Society, 2007. p. 87-93 4144753 (Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Macro programming through bayesian networks

T2 - 5th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007

AU - Mamei, Marco

AU - Nagpal, Radhika

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=34547634869&partnerID=8YFLogxK

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DO - 10.1109/PERCOM.2007.19

M3 - Conference contribution

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SN - 0769527876

SN - 9780769527871

T3 - Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007

SP - 87

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BT - Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007

PB - IEEE Computer Society

Y2 - 19 March 2007 through 23 March 2007

ER -

Mamei M, Nagpal R. Macro programming through bayesian networks: Distributed inference and anomaly detection. In Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007. IEEE Computer Society. 2007. p. 87-93. 4144753. (Proceedings - Fifth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2007). doi: 10.1109/PERCOM.2007.19

Macro programming through bayesian networks: Distributed inference and anomaly detection

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All Science Journal Classification (ASJC) codes

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