Kernel Based Machine Learning For Tracking And Environmental Monitoring In Wireless Sensor Networkds

Download or read book Kernel-based Machine Learning for Tracking and Environmental Monitoring in Wireless Sensor Networkds written by Sandy Mahfouz and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on the problems of localization and gas field monitoring using wireless sensor networks. First, we focus on the geolocalization of sensors and target tracking. Using the powers of the signals exchanged between sensors, we propose a localization method combining radio-location fingerprinting and kernel methods from statistical machine learning. Based on this localization method, we develop a target tracking method that enhances the estimated position of the target by combining it to acceleration information using the Kalman filter. We also provide a semi-parametric model that estimates the distances separating sensors based on the powers of the signals exchanged between them. This semi-parametric model is a combination of the well-known log-distance propagation model with a non-linear fluctuation term estimated within the framework of kernel methods. The target's position is estimated by incorporating acceleration information to the distances separating the target from the sensors, using either the Kalman filter or the particle filter. In another context, we study gas diffusions in wireless sensor networks, using also machine learning. We propose a method that allows the detection of multiple gas diffusions based on concentration measures regularly collected from the studied region. The method estimates then the parameters of the multiple gas sources, including the sources' locations and their release rates.