In wireless sensor network, the power supply is, generally, a non-renewable battery. Consequently, energy effectiveness is a crucial factor. To maximize the battery life and therefore, the duration of network service, a robust wireless communication protocol providing a best energy efficiency is required. In this paper, we present a uniform balancing energy routing protocol. In this later the transmission path is chosen for maximizing the whole network lifetime. Every transmission round, only the nodes which have their remaining energies greater than a threshold can participate as routers for other nodes in addition to sensing the environment. This choice allows the distribution of energy load among any sensor nodes; thus extends network lifetime. The experimental results shows that the proposed protocol outperforms some protocols given in the literature.

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Prognostic of future health state relies on the estimation of the Remaining Useful Life (RUL) of physical systems or components based on their current health state. RUL can be estimated by using three main approaches: model-based, experience-based and data-driven approaches. This paper deals with a datadriven prognostics method which is based on the transformation of the data provided by the sensors into models that are able to characterize the behavior of the degradation of bearings. For this purpose, we used Support Vector Machine (SVM) as modeling tool. The experiments on the recently published data base taken from the platform PRONOSTIA clearly show the superiority of the proposed approach compared to well established method in literature like Mixture of Gaussian Hidden Markov Models (MoG-HMMs).

Ultra-wideband (UWB) technology for the localization of wireless sensor networks has received considerable attention last few years. This technology is dedicated for indoor localization using a fme delay of resolution and obstacle-penetration capabilities. A lot of challenges remain before implementation of UWB can be deployed on a large scale like non-line-of-sight (NLOSwhich is especially critical for most location-based applications because the NLOS propagation introduces positive bias in the estimation of distance, which can seriously affect the perfonnance of localization.In this paper, we present a technique for identifying between both line-of-sight(LOS) and non-line-of-sight (NLOS) contexts based on stable distribution parameters using SVM (Support Vector Machine) methods for classification. This characterization was applied to UWB measurements collected from whyless.com project by the IMST group, over bandwidth of 10 GHz.

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Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, Alpha-stable distribution was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by Alpha-stable distribution parameters, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance.

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