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| 【计算机springer journal】similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks |
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| 发布人：kyzx 发布时间：2019-04-22 浏览次数:241 |
| 作者： | , , , , | | 作者单位： | 1hubei co-innovation center of information technology service for elementary education, hubei university of education
2department of mathematics and computer science, northeastern state university
3school of computer science & engineering, guilin university of aerospace technology
| | 刊名： | eurasip journal on wireless communications and networking, 2019, vol.2019 (1), pp.1-11 | | 来源数据库： | springer journal | | doi： | | | 关键词： | ; ; ; ; | | 英文摘要： | abstract(#br)for resource-constrained iot systems, data collection is one of the fundamental operations to reduce the energy dissipation of sensor nodes and improve the network lifetime. however, an anomaly or deviation will exert a great influence on the quality of data collected, especially for a data aggregation scheme. by taking into account data-aware clustering and detection of anomalous events, a similarity-aware data aggregation using a fuzzy c-means approach for wireless sensor networks is proposed. firstly, by using a fuzzy c-means approach, the clustering process can be performed to organize sensors into clusters based on data similarity. next, an effective support degree function is defined for further outlier diagnosis. afterwards, the appropriate weight of valid data can be... |
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