Factor graph and fisher information matrix-assisted indoor cooperative positioning algorithm for wireless sensor networks

Smart cities and autonomous vehicles have been emerging to be predominant and key players in today's cutting-edge technology. Their emergence has been rapidly escalated with the advancements of concepts of Internet of Things (IoTs). IoTs are characterized by nodes and agents which help in effective collection of vital information, relaying the information to adjacent nodes to the destination thus incorporating smartness into existing systems. Localization of these nodes and agents is a challenging issue especially in the applications such smart vehicles, autonomous vehicles etc. which are continuously in the mobile state. Continuous monitoring of their position and coordinates dictates the overall efficiency and precision of the IoT based system. A cooperative localization scheme for determination of these coordinates is proposed based on the Factor Graph-based Fisher Information Matrix theory. The Fisher Information Matrix (FIM) derived for the proposed model is utilized in the Bayesian Paradigm to compute the default prior or the relative density of nodes as a weight function. Finally, the Crammer Rao Lower Bound (CRLB) model is used to compute best set of neighboring nodes which help in triangulating the position of the target node. The proposed work has been tested for its efficiency against benchmark methods to validate the superiority of proposed work.