TY - JOUR
T1 - Adaptive sensor placement and boundary estimation for monitoring mass objects
AU - Guo, Zhen
AU - Zhou, Meng Chu
AU - Jiang, Guofei
N1 - Funding Information:
Manuscript received October 3, 2006; revised February 28, 2007 and July 23, 2007. This work was supported in part by Nippon Electric Company, Limited (NEC) Laboratories America, Chang Jiang Scholars Program, PRC Ministry of Education, and the Department of Defense under Subcontract 86190NBS21. This paper was recommended by Associate Editor E. Santos.
PY - 2008/2
Y1 - 2008/2
N2 - Sensor networks are widely used in monitoring and tracking a large number of objects. Without prior knowledge on the dynamics of object distribution, their density estimation could be learned in an adaptive manner to support effective sensor placement. After sensors observe the "current" locations of objects, the estimates of object distribution are updated with these new observations through a recursive distributed expectation-maximization algorithm. Based on the real-time estimates of object distribution, an adaptive sensor placement algorithm could be designed to achieve stable and high accuracy in tracking mass objects. This paper constructs a Gaussian mixture model to characterize the mixture distribution of object locations and proposes a novel methodology to adaptively update sensor placement. Our simulation results demonstrate the effectiveness of the proposed algorithm for adaptive sensor placement and boundary estimation of mass objects.
AB - Sensor networks are widely used in monitoring and tracking a large number of objects. Without prior knowledge on the dynamics of object distribution, their density estimation could be learned in an adaptive manner to support effective sensor placement. After sensors observe the "current" locations of objects, the estimates of object distribution are updated with these new observations through a recursive distributed expectation-maximization algorithm. Based on the real-time estimates of object distribution, an adaptive sensor placement algorithm could be designed to achieve stable and high accuracy in tracking mass objects. This paper constructs a Gaussian mixture model to characterize the mixture distribution of object locations and proposes a novel methodology to adaptively update sensor placement. Our simulation results demonstrate the effectiveness of the proposed algorithm for adaptive sensor placement and boundary estimation of mass objects.
KW - Expectation-maximization (EM)
KW - Gaussian mixture model (GMM)
KW - Learning
KW - Maximum likelihood (ML)
KW - Sensor networks
KW - Sensor placement
KW - Wireless sensor network
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U2 - 10.1109/TSMCB.2007.910531
DO - 10.1109/TSMCB.2007.910531
M3 - Article
C2 - 18270093
AN - SCOPUS:39649085339
SN - 1083-4419
VL - 38
SP - 222
EP - 232
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IS - 1
ER -