A Fletcher–Reeves Conjugate Gradient Neural-Network-Based Localization Algorithm for Wireless Sensor Networks

Multihop connectivity-based algorithms have been receiving increased attention in recent times for localization in wireless sensor networks (WSNs). This paper proposes the development of a Fletcher-Reeves update-based conjugate gradient (CG) multilayered feedforward neural network for multihop connectivity-based localization of a large number of sensor nodes in a 2-D sensor network on the basis of information gathered from beacon nodes. The neural-network-based system employs a classification scheme where the location of a sensor is simultaneously estimated in both the x- and y-directions. The usefulness of the proposed scheme is demonstrated by employing the scheme for three case studies, with varied environments, where it could consistently show better performance than two popular recently proposed schemes.