Estimating sensor population via probabilistic sequential polling

Amir Leshem; Lang Tong

doi:10.1109/LSP.2005.845595

Estimating sensor population via probabilistic sequential polling

Amir Leshem, Lang Tong

Signal Processing

Cornell University

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

A probabilistic sequential polling protocol (PSPP) is presented for the estimation of the sensor population in a large-scale sensor network with a mobile access point. It is shown that PSPP requires O(log₂ N) sensor transmissions and a total of O ((log₂ N)²) polls to achieve an arbitrarily predetermined level of accuracy.

Original language	English
Pages (from-to)	395-398
Number of pages	4
Journal	IEEE Signal Processing Letters
Volume	12
Issue number	5
DOIs	https://doi.org/10.1109/LSP.2005.845595
State	Published - May 2005

Bibliographical note

Funding Information:
Manuscript received August 8, 2004; revised November 30, 2004. This work was supported in part by he Multidisciplinary University Research Initiative (MURI) by the Army Research Laboratory CTA on Communication and Networks under Grant DAAD19-01-2-0011. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Erchin Serpedin.

Funding

Manuscript received August 8, 2004; revised November 30, 2004. This work was supported in part by he Multidisciplinary University Research Initiative (MURI) by the Army Research Laboratory CTA on Communication and Networks under Grant DAAD19-01-2-0011. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Erchin Serpedin.

Funders	Funder number
Army Research Laboratory CTA on Communication and Networks	DAAD19-01-2-0011

Keywords

Estimation
Polling
Probabilistic algorithms
Sensor network with mobile access (SENMA)
Sensor networks

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10.1109/LSP.2005.845595

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Estimating sensor population via probabilistic sequential polling

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