Nisrine Ghadban

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This paper deals with the issue of monitoring physical phenomena using wireless sensor networks. It provides principal component analysis for the time series of sensors' measurements. Without the need to compute the sample covariance matrix, we derive several in-network strategies to estimate the principal axis, including noncooperative and diffusion(More)
This paper deals with the principal component analysis in networks, where it is improper to compute the sample covariance matrix. To this end, we derive several in-network strategies to estimate the principal axes, including noncooperative and cooperative (diffusion-based) strategies. The performance of the proposed strategies is illustrated on diverse(More)
Cloud computing is gaining an important role in providing high quality IT services. However, the heterogeneous and dynamic nature of the activities it hosts makes the related management operations, serving performance or security purposes, complexes. Leveraging the autonomic paradigm, represents a promising solution but it requires efficient grounded(More)
This paper deals with the issues of the dimensionality reduction and the extraction of the structure of data using principal component analysis for the multivariable data in large-scale networks. In order to overcome the high computational complexity of this technique, we derive several in-network strategies to estimate the principal axes without the need(More)
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