A BP-Neural Network Improvement to Hop-Counting for Localization in Wireless Sensor Networks

GDL (Geographic Distributed Localization) is a distributed localization technique useful for letting nodes in a network determines their relative locations without special hardware or configuration. As compared with previous proposed measurement techniques, which require additional hardware (such as special antennas), this technique is based on hop counting and only requires the connectivity of network. In this paper, we propose to incorporate neural networks into the standard hop counting-based approach to improve the accuracy of localization over the existing method. We use one neural network at each node to compute its hop-coordinates, after a training procedure. Using network simulations, we show that incorporating our technique improves the accuracy of localization results generated by GDL by 15%. The application of this method is not limited to the GDL algorithm; it can also be used in other hop-counting based localization algorithms, such as MDS-MAP. It is also designed to be suitable for implementation in current embedded systems, such as standard motes like Moteiv’s Invent.

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