
Proceedings Paper
Learning sensor models for wireless sensor networksFormat | Member Price | Non-Member Price |
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Paper Abstract
Sensor data generation is a key component of high fidelity design and testing of applications at scale. In
addition to its utility in validation of applications and network services, it provides a theoretical basis for the
design of algorithms for efficient sampling, compression and exfiltration of the sensor readings. Modeling of
the environmental processes that gives rise to sensor readings is the core problem in physical sciences. Sensor
modeling for wireless sensor networks combine the physics of signal generation and propagation with models of
transducer saturation and fault models for hardware. In this paper we introduce a novel modeling technique
for constructing probabilistic models for censored sensor readings. The model is an extension of the Gaussian
process regression and applies to continuous valued readings subject to censoring. We illustrate the performance
of the proposed technique in modeling wireless propagation between nodes of a wireless sensor network. The
model can capture the non-isotropic nature of the propagation characteristics and utilizes the information from
the packet reception failures. We use measured data set from the Kansei sensor network testbed using 802.15.4
radios.
Paper Details
Date Published: 8 May 2007
PDF: 8 pages
Proc. SPIE 6560, Intelligent Computing: Theory and Applications V, 65600N (8 May 2007); doi: 10.1117/12.722409
Published in SPIE Proceedings Vol. 6560:
Intelligent Computing: Theory and Applications V
Kevin L. Priddy; Emre Ertin, Editor(s)
PDF: 8 pages
Proc. SPIE 6560, Intelligent Computing: Theory and Applications V, 65600N (8 May 2007); doi: 10.1117/12.722409
Show Author Affiliations
Emre Ertin, The Ohio State Univ. (United States)
Published in SPIE Proceedings Vol. 6560:
Intelligent Computing: Theory and Applications V
Kevin L. Priddy; Emre Ertin, Editor(s)
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