Abstract data extraction and reformation for IoT

Lim, Xin Hui (2022) Abstract data extraction and reformation for IoT. Final Year Project, UTAR.

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Abstract

In a Wireless Sensor Network (WSN), sensor nodes are able to communicate with each other and relay the sensed data to a base station. In general, sensor nodes have a limited energy supply and each packet transmission consumes a certain amount of energy from a sensor node. Therefore, if each sensor node can strategically abstract only the critical data from the sensed data to report, a substantial amount of packet transmission and energy consumption can be reduced, thus prolonging the lifetime of WSN. Data abstraction is a term used to describe such a scheme. On the other hand, to prevent data loss, the abstract data reported to the base station must contain sufficient information in such a way that the full set of data can be reconstructed as accurately as possible at the base station using a data reformation scheme. This paper studies the zeroth-, first-, and second-order DAR algorithms, as well as their applications in WSN, which are investigated in this work. Through the study, it was found that the number of packet transmissions can be greatly reduced with a proper selection of DAR algorithms, and yet the data can be reformed at the base station with acceptable accuracy.

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