Efficient analytical architecture for sensor networks using Hadoop

摘要

Big Data is the new experience curve in the new economy driven by huge data with larger volume, velocity and variety. The real time application processing of remote sensing of large volumes of Big Data seems at first, and provide extracting and analysis in useful information with an effective manner leads a advanced system toward a novel computational challenges, such as to analyze, aggregate, and store, where data are remotely gathered. The three main units comprise the proposed architecture the three units are The proposed architecture can store raw data for the analysis of the offline data when required. The proposed architecture comprises three main units, such as 1) remote sensing Big Data acquisition unit (RSDU); 2) data processing unit (DPU); and 3) data analysis decision unit (DADU). First, RSDU acquires data from the satellite and sends this data to the Base Station, where initial processing takes place. Second, DPU plays a vital role in architecture for efficient processing of real-time Big Data by providing filtration, load balancing, and parallel processing. Third, DADU is the upper layer unit of The proposed structure, which is liable for compilation, storage of the results, and generation of determination founded on the outcome obtained from DPU. The proposed architecture has the ability of dividing, load balancing, and concurrent processing of only useful data. For that reason, it outcome in effectively analyzing real time remote sensing tremendous data utilizing earth observatory method. Additionally, the proposed big data architecture provides the capacity of storing and making analysis of incoming raw knowledge to perform offline evaluation on mostly stored dumps, when required. Ultimately, a targeted analysis of remotely sensed earth observatory tremendous data for land and sea field is supplied making use of Hadoop. In addition, more than a few algorithms are proposed for each and every stage of RSDU, DPU, and DADU to detect land as good as sea discipline to complicated the working of architecture.