Indoor Localization of Cooperative WSN Using PSO Assisted AKF with Optimum References

Finding exact node position with adjacent nodes is known as localization. Localization of Wireless Sensor Networks (WSN) is critical for many applications in indoor environment. In cooperative WSN all nodes assist in localization process in which some nodes give inaccurate estimates. Hence selection and rejection of information from nodes is a big challenge. Crammer Rao Bound (CRB) is used to select accurate nodes as the reference nodes. Kalman filter is a repetitive filter, widely used for estimation of location in linear environment. Extended Kalman Filter (EKF) is modification to the linear Kalman Filter used in nonlinear environment. In indoor environment due to noise, multipath effects EKF do not guarantee a best solution. Particle Swarm Optimization (PSO) is a population based search algorithm, which is formulated on the swarm intelligence like social behavior of birds, bees or a school of fishes. In this paper PSO Assisted Extended Kalman Filter (PSO-AKF) with optimum references is proposed. Localization of cooperative WSN with optimum selection of reference nodes using PSO-AKF shows better results in terms of position accuracy, latency and complexity.