DocumentCode :
417503
Title :
Good-Turing estimation of the number of operating sensors: a large deviations analysis
Author :
Budianu, Cristian ; Tong, Lang
Author_Institution :
Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA
Volume :
2
fYear :
2004
fDate :
17-21 May 2004
Abstract :
We have proposed an estimator for the number of operating sensors in a wireless sensor network based on the Good-Turing non-parametric estimator of the missing mass (Budianu and Tong, Proc. Asilomar Conf. on Sig., Systems and Computers, 2003). We now investigate the performance of this estimator using the theory of large deviations. We determine the asymptotic behavior of the large deviations exponent as the ratio n/N between the number of collected samples n and the number of operating sensors N decreases to zero. The simulations reveal that the confidence intervals obtained using the large deviations formula are upper bounds for the actual performance of the estimator. Together with the asymptotic behavior of the exponent, this suggests the surprising fact that if the scaling law n=f(N) is used for the number of samples, then reliable estimation can be done if n grows at least as fast as √N. Separately, it is shown that, if limN→∞(n/√N)=0, the estimator cannot be used.
Keywords :
estimation theory; telecommunication network planning; wireless sensor networks; Good-Turing estimation; large deviations analysis; nonparametric estimator; scaling law; wireless sensor network; Access protocols; Batteries; Computer networks; Contracts; Estimation theory; Femtocell networks; Maximum likelihood estimation; Research initiatives; Upper bound; Wireless sensor networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-7803-8484-9
Type :
conf
DOI :
10.1109/ICASSP.2004.1326436
Filename :
1326436
Link To Document :
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