Sandy Mahfouz

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This paper describes an original method for target tracking in wireless sensor networks. The proposed method combines machine learning with a Kalman filter to estimate instantaneous positions of a moving target. The target’s accelerations, along with information from the network, are used to obtain an accurate estimation of its position. To this end,(More)
Indoor localization is an important issue in wireless sensor networks for a very large number of applications. Recently, localization techniques based on the received signal strength indicator (RSSI) have been widely used due to their simple and low cost implementation. In this paper, we propose an algorithm for localization in wireless sensor networks(More)
This paper introduces an original method for target tracking in wireless sensor networks that combines machine learning and Kalman filtering. A database of radio-fingerprints is used, along with the ridge regression learning method, to compute a model that takes as input RSSI information, and yields, as output, the positions where the RSSIs are measured.(More)
In this paper, we propose a semiparametric regression model that relates the received signal strength indicators (RSSIs) to the distances separating stationary sensors and moving sensors in a wireless sensor network. This model combines the well-known log-distance theoretical propagation model with a nonlinear fluctuation term, estimated within the(More)
Sensors localization has become an essential issue in wireless sensor networks. This paper presents a decentralized localization algorithm that makes use of radio-location fingerprinting and kernel methods. The proposed algorithm consists of dividing the network into several zones, each of which having a calculator capable of emitting, receiving and(More)
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