CSINR: Interconnected nodes reading for accurate GPS precision and mapping to achieve geographic centric self-learning nodes

Abstract

Global Positioning System (GPS) provides a reliable solution for accurate mapping and geographical labeling. The coordination of nodes in communication are wider and has a centralized monitoring cum reporting ecosystem. These nodes fail to label the inter-connected object spaces between reporting agents and nodes using integer ambiguity based satellite to node coordination. The time delay of Total Electron Content (TEC) is computational higher. In this article, a dedicated GPS clock prediction based precision and mapping is conducted for an interconnected nodes. The technique uses a novel technique of Centric Self-Learning Interconnected Nodes Reading (CSINR) for validating the GPS accuracy and precision. The interconnected nodes are dependent on the information shared via network managers and hence, CSINR technique extracts the nodal relationship of interconnected nodes to build a pseudo connected network based on temporal factors and clock offset. The technique is further supported with a self-learning nodes approach, with each node extracts the principle value of supporting neighboring nodes to maintain reliable source accuracy information. The proposed technique has achieved an evaluation ratio of 97.32% in precision and 92.78% in sensitivity of node occurrence. The overall technique has proposed 97.43% accuracy over the cluster of 32nodes and 97.12% in cluster of 64nodes respectively.