The main purpose for wireless sensor networks is especially for data collection. Nevertheless, since the sensors are mostly immobile, the operation of transferring data they captured to the base station is a critical task since the exchange environment is unstable. Our approach consists in injecting mobile relay nodes whose mission is mainly to maximize the lifetime of the network, also to provide connectivity between any sensor and the base station, and to minimize the latency of receiving data sent to the base station. Smart mobility is the key for our approach to ensure reliability by forming a virtual mobile circle using relay nodes. We have proved formally and by simulation the effectiveness of our approach, the obtained results prove the efficiency of our approach.
In this paper, we propose a large-update primal-dual interior point algorithm for linear optimization. The method is based on a new class of kernel functions which differs from the existing kernel functions in which it has a double barrier term. The investigation according to it yields the best known iteration bound O( √ n log(n) log( n ε )) for large-update algorithm with the special choice of its parameter m and thus improves the iteration bound obtained in Bai et al. [2] for large-update algorithm.
This paper proposes a new wavelet family based on fractional calculus. The Haar wavelet is extended to fractional order by a generalization of the associated conventional low-pass filter using the fractional delay operator Z-D. The high-pass fractional filter is then designed by a simple modulation of the low-pass filter. In contrast, the scaling and wavelet functions are constructed using the cascade Daubechies algorithm. The regularity and orthogonality of the obtained wavelet basis are ensured by a good choice of the fractional filter coefficients. An application example is presented to illustrate the effectiveness of the proposed method. Thanks to the flexibility of the fractional filters, the proposed approach provides better performance in term of image denoising.
