Sensors 2010, 10(8), 7632-7650; doi:10.3390/s100807632
Article

A Gaussian Mixture Model-Based Continuous Boundary Detection for 3D Sensor Networks

1 Global COE Program International Research and Education Center for Ambient SoC, Waseda University, Tokyo, 169-8555, Japan 2 Graduate School of Global Information and Telecommunication Studies, Waseda University, Tokyo, 169-0051, Japan
* Author to whom correspondence should be addressed.
Received: 25 June 2010; in revised form: 18 July 2010 / Accepted: 30 July 2010 / Published: 13 August 2010
(This article belongs to the Section Physical Sensors)
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Abstract: This paper proposes a high precision Gaussian Mixture Model-based novel Boundary Detection 3D (BD3D) scheme with reasonable implementation cost for 3D cases by selecting a minimum number of Boundary sensor Nodes (BNs) in continuous moving objects. It shows apparent advantages in that two classes of boundary and non-boundary sensor nodes can be efficiently classified using the model selection techniques for finite mixture models; furthermore, the set of sensor readings within each sensor node’s spatial neighbors is formulated using a Gaussian Mixture Model; different from DECOMO [1] and COBOM [2], we also formatted a BN Array with an additional own sensor reading to benefit selecting Event BNs (EBNs) and non-EBNs from the observations of BNs. In particular, we propose a Thick Section Model (TSM) to solve the problem of transition between 2D and 3D. It is verified by simulations that the BD3D 2D model outperforms DECOMO and COBOM in terms of average residual energy and the number of BNs selected, while the BD3D 3D model demonstrates sound performance even for sensor networks with low densities especially when the value of the sensor transmission range (r) is larger than the value of Section Thickness (d) in TSM. We have also rigorously proved its correctness for continuous geometric domains and full robustness for sensor networks over 3D terrains.
Keywords: 3D sensor network; Gaussian Mixture Model; continuous boundary detection

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MDPI and ACS Style

Chen, J.; Salim, M.B.; Matsumoto, M. A Gaussian Mixture Model-Based Continuous Boundary Detection for 3D Sensor Networks. Sensors 2010, 10, 7632-7650.

AMA Style

Chen J, Salim MB, Matsumoto M. A Gaussian Mixture Model-Based Continuous Boundary Detection for 3D Sensor Networks. Sensors. 2010; 10(8):7632-7650.

Chicago/Turabian Style

Chen, Jiehui; Salim, Mariam B.; Matsumoto, Mitsuji. 2010. "A Gaussian Mixture Model-Based Continuous Boundary Detection for 3D Sensor Networks." Sensors 10, no. 8: 7632-7650.

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