A Novel CSINR Technique for Accurate and Precise GPS Communication by Geographical Centric Self-learning Nodes

Abstract

Since its introduction, the Global Positioning System (GPS) is finding many countless, useful, and emergency applications, focused mainly on track. As the technology is advancing day by day and the best feature of GPS, which does not, relies on mobile signal to work, making it a feasible feature to incorporate into other devices as functionalities. By adopting GPS to a system, accurate mapping and geographical labeling can be obtained. GPS works better with the coordination of nodes and requires centralized monitoring and a reporting system. As it is a known fact that a demerit follows merit anywhere else, In GPS also, the major attention is required to make the nodes to success in mapping the intermediate space between agent node used for reporting and the remaining nodes of a cluster, where satellite and node coordination can be possible integer ambiguity technique. Many researchers have proposed solutions to the aforementioned problem; unfortunately still today the proposed methods are weaker in achieving lesser time delay of Total Electron Content (TEC). The proposed Centric Self-Learning Interconnected Nodes Reading (CSINR) technique is novel in terms addressing the intermediate nodes failing to label the inter-connected object spaces between reporting agents and nodes using integer ambiguity technique for node co-ordination and using a dedicated GPS prediction-based clock system, which predicts precise and accurate mapping between interconnected nodes. Based on the information shared between among the network managers a separate pseudo-connected network will be formed and further this network will be considered an interconnected nodes network. From the information calculated from temporal factors and clock offset the separate pseudo network is extracted by using the proposed CSINR technique. Add-on self-improvement is introduced to the proposed method by a self--learning feature to an individual join extract the principal rate of partisan neighbouring join to sustain accuracy in order consistent basis. An evaluation ratio of 97.43%, sensitivity of node occurrence is resulted as 92.78% and accuracy of 97.43% and 97.12% is achieved among a cluster of 32and 64 nodes respectively.