Kernel-based machine learning using radio-fingerprints for localization in WSNs
Résumé
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 received signal strength indicators and the sensors locations. To define this model, several kernel-based machine-learning techniques are investigated, such as the ridge regression, support vector regression, and vector-output regularized least squares. The performance of the method is illustrated using both simulated and real data.
Mots clés
- Computational modeling
- learning (artificial intelligence)
- regression analysis
- sensor placement
- support vector machines
- sensors localization
- radio-location fingerprinting
- received signal strength indicators
- ridge regression
- support vector regression
- Mathematical model
- Sensors
- Databases
- Kernel
- Wireless sensor networks
- Optimization
- machine learning
- vector-output regularized least squares
- telecommunication computing
- WSNS
- kernel-based machine-learning techniques