Abstract data extraction and reformation for IoT
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 senso...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2022
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| Online Access: | http://eprints.utar.edu.my/4659/ http://eprints.utar.edu.my/4659/1/fyp_CS_2022_LXH.pdf |
| Summary: | 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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