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Kernel-based machine learning using radio-fingerprints for localization in WSNs

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This paper introduces an original method for sensors localization in WSNs. Based on radio-location fingerprinting and machine learning, the method consists of defining a model, whose inputs and outputs are respectively the RSSIs and the sensors locations. To define this model, several kernel-based machine learning techniques are investigated, such as the ridge regression, the support vector regression, and the vector-output regularized least squares. The performance of the method is illustrated using both simulated and real data.

Hichem Snoussi

Field of Interest

“The field of interest shall be the organization, systems engineering, design, development, integration, and operation of complex systems for space, air, ocean, or ground environments. These systems include but are not limited to navigation, avionics, mobile electric power and electronics, radar, sonar, telemetry, military, law-enforcement, automatic test, simulators, and command and control."


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