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In wireless sensor networks, sensors typically transmit their data to servers at predefined time intervals. In this environment, data packets are very susceptible to losses, delays or corruption due to various reasons, such as power outage at the sensor’s node, a higher bit error rate of the wireless radio transmissions compared to the wire communication alternative, an inefficient routing algorithm implemented in the network, or random occurrences of local interferences (e.g., mobile radio devices, microwaves or broken line-of-sight path). To process queries that need to access the missing data, if repeated requests are sent to sensors asking them to resend the missing information, this would incur power-costly communications as those sensors must be constantly in the listening mode. In addition, it is not guaranteed that those sensors would resend their missing data or would resend them in a timely manner. Alternatively, one might choose to...
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Gruenwald L., Chok H., and Aboukhamis M. Using data mining to estimate missing sensor data. In Proc. 7th IEEE ICDM Workshop on Optimization-Based Data Mining Techniques with Applications, 2007, pp. 207–212.
Madden S., Franklin M., Hellerstein J., and Hong W. TinyDB: an acquisitional query processing system for sensor networks. ACM Trans. Database Syst., 30(1):122–173, 2005.
Papadimitriou S., Sun J., and Faloutsos C. Pattern discovery in multiple time-series. In Proc. 31st Int. Conf. on Very Large Data Bases, 2005, pp. 697–708.
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© 2009 Springer Science+Business Media, LLC
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Gruenwald, L. (2009). Data Estimation in Sensor Networks. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_99
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DOI: https://doi.org/10.1007/978-0-387-39940-9_99
Publisher Name: Springer, Boston, MA
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