Cottone, P, Gaglio, S, Lo Re, G and Ortolani, M ORCID: https://orcid.org/0000-0001-6759-7698 (2016) A machine learning approach for user localization exploiting connectivity data. Engineering Applications of Artificial Intelligence, 50. 125 - 134.

Full text not available from this repository.

Abstract

The growing popularity of Location-Based Services (LBSs) has boosted research on cheaper and more pervasive localization systems, typically relying on such monitoring equipment as Wireless Sensor Networks (WSNs), which allow to re-use the same instrumentation both for monitoring and for localization without requiring lengthy off-line training.

This work addresses the localization problem, exploiting knowledge acquired in sample environments, and extensible to areas not considered in advance. Localization is turned into a learning problem, solved by a statistical algorithm. Additionally, parameter tuning is fully automated thanks to its formulation as an optimization problem based only on connectivity information.

Performance of our approach has been thoroughly assessed based on data collected in simulation as well as in actual deployment.

Item Type: Article
Additional Information: The final version of this publication is available online at https://www.sciencedirect.com/science/article/pii/S0952197616000063
Uncontrolled Keywords: Wireless sensor networks; Range-free localization; Support vector machines
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Natural Sciences > School of Computing and Maths
Depositing User: Symplectic
Date Deposited: 01 Apr 2019 09:57
Last Modified: 01 Apr 2019 10:29
URI: http://eprints.keele.ac.uk/id/eprint/6130

Actions (login required)

View Item View Item