Deep Mining of Redundant Data in Wireless Sensor Network Based on Genetic Algorithm
| Autor: | Haijun Diao |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Data processing
Wireless network Computer science Node (networking) Real-time computing 020206 networking & telecommunications 02 engineering and technology Rejection rate Control and Systems Engineering Signal Processing Genetic algorithm Computer Science::Networking and Internet Architecture 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Rough set MATLAB computer Wireless sensor network Software computer.programming_language |
| Zdroj: | Automatic Control and Computer Sciences. 52:291-296 |
| ISSN: | 1558-108X 0146-4116 |
| DOI: | 10.3103/s0146411618040053 |
| Popis: | Mining effective data from wireless sensor network node data is one of the main subjects in studies concerning wireless sensor network data processing. Wireless sensor network data are muli-dimensional and dynamic. Generally, data mining technology cannot satisfy the requirements of wireless sensor network. A large amount of accumulated and redundant wireless sensor network monitoring data reduces the efficiency of data processing. To solve the above problems, this study proposed a data mining algorithm, which integrated rough set algorithm and genetic algorithm to mine redundant data in node network data. The results of the simulated calculation based on MATLAB platform suggested that the identification rate, false accept rate and reject rate of the proposed algorithm were 94.65, 1.753 and 2.331%; compared to network data mining algorithm based on improved genetic algorithm, it has higher efficiency and accuracy in data mining. The algorithm could effectively excavate redundant data in wireless sensor network and optimize the operation environment of wireless sensor network. The application of the rough set and genetic algorithm based data mining algorithm in wireless network has a promising prospect. |
| Databáze: | OpenAIRE |
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