A converging distributed positioning algorithm for internet-of-things

Abstract

A distributed algorithm for node and user localization for Internet-of-Things (IoT) Ad-Hoc networks is presented. Proposed algorithm is independent of specific wireless communication technologies and protocols. Expected 0,3 m accuracy of ranging measurements are used as representation of 1ns timing resolution within Time-of-Arrival (ToA) ranging measurements. Simulation of such algorithm is analysed and discussed. It is shown that algorithm is scalable, capable of adaptive incorporation of external positioning information and self-correcting. Novelty of proposed positioning algorithm for IoT nodes consists of introduction of Local Reference Frames (LRF) and means for positioning solution to transition between them, and to integrate absolute positioning solution in order to achieve network convergence. Relative positioning solution can be used to track relative position and movement of the network nodes before network converges to absolute coordinate system. Proposed method allows convergence to occur in parts of the network independent of any centralized algorithm and can employ various independent measurements.