Combined sensor selection and node location optimization for reducing the localization uncertainties in wireless sensor networks

Local Positioning Systems (LPS) address the limitations of Global Navigation Satellite Systems in harsh environments. LPS must attain the optimal location of their sensors in space for optimal performance, which is known as the Node Location Problem. In addition, not all the sensors under coverage present the best performance for determining the location. For this purpose, the optimal selection of the best combination of nodes requires addressing the Sensor Selection Problem. In this paper, for the first time in the literature, we present a methodology for the combined optimization of both problems which may attain improved results than separated optimizations. Results show that the consideration of SSP based sensor selection strategies during the NLP suppose a reduction up to 10% in the localization uncertainties with respect to traditional baseline sensor selection strategies of the NLP, thus proving the effectiveness of the devised technique.