  As Wireless Sensor Networks are penetrating into the industrial domain, many
research opportunities are emerging. One such essential and challenging
application is that of node localization. A feed-forward neural network based
methodology is adopted in this paper. The Received Signal Strength Indicator
(RSSI) values of the anchor node beacons are used. The number of anchor nodes
and their configurations has an impact on the accuracy of the localization
system, which is also addressed in this paper. Five different training
algorithms are evaluated to find the training algorithm that gives the best
result. The multi-layer Perceptron (MLP) neural network model was trained using
Matlab. In order to evaluate the performance of the proposed method in real
time, the model obtained was then implemented on the Arduino microcontroller.
With four anchor nodes, an average 2D localization error of 0.2953 m has been
achieved with a 12-12-2 neural network structure. The proposed method can also
be implemented on any other embedded microcontroller system.
