Edge AI cosmos blockchain distributed network for precise ablh detection

The detection of the vertical profiles of atmospheric variables or aerosols is a vital component in the accurate calculation of the atmospheric boundary layer height (ABLH). In many existing techniques, the precise ABLH detection has poor resource consumption, heterogeneous IoT conditions, lack of interoperability, and online and offline analyzers overdue are impacted by the processing of high streaming data from the ceilometer. Hence, a novel Edge AI cosmos blockchain distributed network is proposed to eliminate the efficient lowering parameters to provide a user-scalable networking based on a deep learning algorithm, in which the cosmos ledger blockchain method gets the data without affecting its precision or specificity, eliminates interoperability, increases usability. Moreover, the existing techniques has no real-time adjustment when processing the high streaming data. Hence, a model Real-time batch scheduling technique has been introduced to reduce a significant amount of computing power. In existing techniques, the scheduling process is very complicated as the ABLH layer attribution, precipitation, lofted aerosol layers, low aerosol conditions, and clouds. To overcome these issues, a gubernatorial algorithm is used to detect the ABLH value hourly which is gathered and stored data in a very optimized and efficient manner. Overall, the proposed technique compares effective data management and produces a computer environment which is optimal for ABLH.