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Statistical Location Estimation in Container-Grown Seedlings Based on Wireless Sensor Networks
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 Title & Authors
Statistical Location Estimation in Container-Grown Seedlings Based on Wireless Sensor Networks
Lee, Sang-Hyun; Moon, Kyung-Il;
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 Abstract
This paper presents a sensor location decision making method respect to Container-Grown Seedlings in view of precision agriculture (PA) when sensors involved in tree container measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the container-grown seedlings system have a known location, whereas the remaining locations must be estimated. We derive Rao-Cramer bounds and maximum-likelihood estimators under Gaussian and log-normal models for the TOA and RSS measurements, respectively.
 Keywords
Container-Grown Seedlings;Wireless Sensor Network;Sensor Position Location;Signal Strength;Time-of-Arrival;Rao-Cramer bound;
 Language
English
 Cited by
 References
1.
A. H. Carles, L. Thierry, L. Yonghua, N. Navid, W. Thomas, and A. Z. Jesus, Machine-to-machine: An emerging communication paradigm. Trans. on Emerging Telecommunications Technologies, Vol. 24, Issue 4, pp. 353-354, June 2013. crossref(new window)

2.
D. D. McCrady, L. Doyle, H. Forstrom, T. Dempsy, and M. Martorana. Mobile ranging with low accuracy clocks, IEEE Trans. Microwave Theory Tech., vol. 48, pp. 951-957, June 2000. crossref(new window)

3.
J. M. Rabaey, M. J. Ammer, J. L. da Silva, Jr., D. Patel, and S. Roundy. Picorodio supports ad hoc ultra-low power wireless networking, IEEE Comput., vol. 33, pp. 42.48, July 2000. crossref(new window)

4.
T. S. Rappaport. Wireless Communications: Principles and Practice. Englewood Cliffs, NJ: Prentice-Hall, 1996.