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Data Estimation in Sensor Networks

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Data imputation

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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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Recommended Reading

  1. Gruenwald L, Yang H, Sadik MS, Shukla R. Using data mining to handle missing data in multi-hop sensor network applications. In: Proceedings of the 9th ACM International Workshop on Data Engineering for Wireless and Mobile Access; 2010, p. 9–16.

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  2. Madden S, Franklin M, Hellerstein J, Hong W. TinyDB: an acquisitional query processing system for sensor networks. ACM Trans Database Syst. 2005;30(1):122–73.

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  3. Pan L, Gao Hu, Li J, Gao Ho, Guo X. CIAM: an adaptive 2-in-1 missing data estimation algorithm in wireless sensor networks. In: Proceedings of the 19th IEEE International Conference on Networks; 2013. p. 1–6.

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Correspondence to Le Gruenwald .

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Gruenwald, L. (2018). Data Estimation in Sensor Networks. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_99

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