Accurate and Low-Cost Location Estimation using Machine Learning Techniques in Wireless Sensor Networks, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Wireless sensor networks have a wide range of applications in the world. In most of the applications, collected data is not usable without the knowledge about the localization of events. There are two approaches to specifying the location of a sensor: using hardware solutions such as GPS, which is an expensive solution, and using the localization algorithm. Therefore, localization has an important role in sensor networks. Most of the current localization algorithms are non-adaptive and proposed for fixed wireless sensor networks. Recently, adaptive localization algorithms have been considered because of their simple implementation, fast result and low computation overhead for each node. In...
Cataloging briefAccurate and Low-Cost Location Estimation using Machine Learning Techniques in Wireless Sensor Networks, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Wireless sensor networks have a wide range of applications in the world. In most of the applications, collected data is not usable without the knowledge about the localization of events. There are two approaches to specifying the location of a sensor: using hardware solutions such as GPS, which is an expensive solution, and using the localization algorithm. Therefore, localization has an important role in sensor networks. Most of the current localization algorithms are non-adaptive and proposed for fixed wireless sensor networks. Recently, adaptive localization algorithms have been considered because of their simple implementation, fast result and low computation overhead for each node. In...
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